November 2025 arXiv papers — page 191

Sílvia P. Nunes, José D. V. Arbañil, Juan M. Z. Pretel, Sérgio B. Duarte

White Dwarfs (WDs), the final evolutionary stage of most stars, are frequently modeled considering only a dense plasma matter. However, their potential interaction with dark matter (DM), especially in galactic halos where DM is expected to be prevalent, may lead to significant consequences. This work proposes a novel EoS (EoS) that consistently incorporates

David Martin de Diego, Najma Mosadegh

In this paper we give a geometric description of the Jacobi equations associated to a first-order Lagrangian field theory using a prolongation of the Lagrangian $L$ on a $k$-cosymplectic formulation. Moreover, using an appropriate modification of the prolonged Lagrangian, we obtain a variational formulation of field theories with dissipation.

Tatiana S. Parlanti, Carlos A. Catania

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a systematic review of over 40 studies published between 2020 and 2025

Keshav Dasgupta, Suddhasattwa Brahma, Bohdan Kulinich, Archana Maji

One of the long-standing puzzles in string theory has been on the existence of a four-dimensional de Sitter and quasi de Sitter configurations, the latter being defined with a temporally varying dark energy, in E_8 x E_8 and SO(32) heterotic theories. In this work, novel dynamical duality-sequences are devised that provide natural constructions of de Sitter

Aimee Maurais, Bamdad Hosseini, Youssef Marzouk

We bring a control perspective to the problem of identifying paths of measures for sampling via dynamic measure transport (DMT). We highlight the fact that commonly used paths may be poor choices for DMT and connect existing methods for learning alternate paths to mean-field games. Based on these connections we pose a flexible family of optimization problems

Elijah Pelofske

One of the surprising, and potentially very useful, capabilities of analog quantum computers, such as D-Wave quantum annealers, is sampling from the Boltzmann, or Gibbs, distribution defined by a classical Hamiltonian. In this study, we thoroughly examine the ability of D-Wave quantum annealers to sample from the Boltzmann distribution defined by a canonical

Ruchira Dhar, Danae Sanchez Villegas, Antonia Karamolegkou, Alice Schiavone

Evaluation has long been a central concern in NLP, and transparent reporting practices are more critical than ever in today's landscape of rapidly released open-access models. Drawing on a survey of recent work on evaluation and documentation, we identify three persistent shortcomings in current reporting practices: reproducibility, accessibility, and govern

Kobe Marshall-Stevens, Gongping Niu, Davide Parise

We establish generic regularity results for isoperimetric regions in closed Riemannian manifolds of dimension eight. In particular, we show that every isoperimetric region has a smooth nondegenerate boundary for a generic choice of smooth metric and enclosed volume, or for a fixed enclosed volume and a generic choice of smooth metric.

Alexander Nikolaenko, Pietro M. Bonetti, Subir Sachdev

We present a theory for the spin fluctuation spectrum of the hole-doped cuprates in a ground state with period 4 unidirectional charge density wave (`stripe') order. Motivated by recent experimental evidence for a fractionalized Fermi liquid (FL*) description of the intermediate temperature pseudogap metal, we employ a theory of fermionic spinons which are c

Nicolò Brizio, Tommaso Morone, Nicolò Primi, Roberto Tateo

We introduce and study a class of two-dimensional integrable quantum field theories that carry an internal $\mathbb{Z}_n$ structure. These models extend factorised scattering beyond the conventional framework, featuring both the usual hierarchy of integer-spin conserved charges and an additional tower of fractional-spin ones. Our construction relies on a rep

Akhil Uniyal, Indu K. Dihingia, Yosuke Mizuno, Luciano Rezzolla

The horizon-scale images of supermassive black holes (BHs) by the Event Horizon Telescope Collaboration (EHT) have provided new opportunities to test general relativity and other theories of gravity. In view of future projects, such as the next-generation Event Horizon Telescope (ngEHT) and the Black-Hole Explorer (BHEX), having the potential of enhancing ou

Sebastiano Cantarella, Gabriella De Lucia, Fabio Fontanot, Michaela Hirschmann

The James Webb Space Telecope (JWST) opened a new window for the study of the highest redshift ($z>7$) Universe. This work presents a theoretical investigation of the very-high redshift Universe using the state-of-the-art GALaxy Evolution and Assembly (GAEA) model, run on merger trees from the Planck-Millennium $N$-body simulation. We show that GAEA successf

Martin Hoferichter, Gabriele Levati

Testing New-Physics (NP) scenarios that couple predominantly to the third generation is notoriously difficult experimentally, as exemplified by comparing limits for the $\tau$ lepton dipole moments to those of electrons and muons. In this case, extracting limits from processes such as $e^+e^-\to\tau^+\tau^-$ often relies on effective-field-theory (EFT) argum

Jorge Moreno, Coral Wheeler, Francisco J. Mercado, M. Katy Rodriguez Wimberly

It is widely believed that the ultraviolet background produced during the epoch of reionization conspires against the formation of low-mass galaxies. Indeed, this mechanism is often invoked as a solution to the so-called `missing satellites problem.' In this paper we employ FIREbox, a large-volume cosmological simulation based on the Feedback In Realistic En

Pratyusha Chowdhury, Felix M. Haehl, Adrián Sánchez-Garrido, Ying Zhao

We describe a holographic mechanism for black hole formation via the collision of two shock waves in three-dimensional anti-de Sitter spacetime. In the dual conformal field theory (CFT), a two-shock-wave state corresponds to the insertion of two boosted precursor operators in complementary Rindler patches. Their operator product expansion is initially descri

Xuhao Jiang, Jad C. Halimeh, N. S. Srivatsa

In high-energy physics, confinement denotes the tendency of fundamental particles to remain bound together, preventing their observation as free, isolated entities. Interestingly, analogous confinement behavior emerges in certain condensed matter systems, for instance, in the Ising model with both transverse and longitudinal fields, where domain walls become

Haoyu Guo, Maria Tikhanovskaya, Paul Raccuglia, Alexey Vlaskin

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the a

Willy Fischler, Hare Krishna, Sarah Racz

We consider a de Sitter observer in his rest frame at late times who observes a particle slightly displaced from unstable equilibrium. Initially, the observer notices an axisymmetric and parity-violating deformation along the trajectory of the displaced particle of his cosmological horizon. On a time scale of order $\ell$, the de Sitter radius, the particle

Haider Alhazmi, Doojin Kim, Kyoungchul Kong, Aishah Sumayli

We present a comprehensive framework for interpreting electron recoil signals induced by fast-moving dark matter (DM), applicable across a wide range of theoretically motivated models. Amid both null results in conventional weakly interacting massive particle searches and growing interest in alternative DM scenarios, we focus on (semi-)relativistic DM compon

Sergio E. Aguilar-Gutierrez

We investigate entanglement entropy between the pair of type II$_1$ algebras of the double-scaled SYK (DSSYK) model given a chord state, its holographic interpretation as generalized horizon entropy; particularly in the (anti-)de Sitter ((A)dS) space limits of the bulk dual; and its connection with Krylov complexity. The density matrices in this formalism ar

Csaba Csáki, Ameen Ismail, Larissa Kiriliuk

We calculate energy correlators in a holographic model incorporating elements of asymptotic freedom and confinement. We model a running coupling by considering a geometry with a warp factor that deviates logarithmically from anti-de Sitter (AdS). A novel aspect of our bulk metric is that it smoothly interpolates between a Randall-Sundrum solution with a hard

Vinícius Bernardes, Theodore Erler, Atakan Hilmi Fırat

We discuss a new formula for the symplectic structure on the phase space of open string field theory. Revisiting the setup of Cho, Mazel, and Yin, we use the formula to compute the energy of rolling tachyon solutions on unstable D-branes. An important aspect of the analysis is dealing with the singular ultraviolet behavior of string vertices in Lorentzian si

X. Hernandez, Pavel Kroupa

Dynamical friction implies a consistency check on any system where dark matter particles are hypothesised to explain orbital dynamics requiring more mass under Newtonian gravity than is directly detectable. Introducing the assumption of a dominant dark matter halo will also imply a decay timescale for the orbits in question. A self-consistency constraint hen

Vijay Balasubramanian, Pawel Caputa, Joan Simón

Spread complexity uses the distribution of support of a time-evolving state in the Krylov basis to quantify dispersal across accessible dimensions of a Hilbert space. Here, we describe how variations in initial conditions, the Hamiltonian, and the dimension of the Hilbert space affect spread complexity and Krylov basis structure. We introduce Koherence, the

Jaden Park, Mu Cai, Feng Yao, Jingbo Shang

Recent advances in Vision-Language Models (VLMs) have achieved state-of-the-art performance on numerous benchmark tasks. However, the use of internet-scale, often proprietary, pretraining corpora raises a critical concern for both practitioners and users: inflated performance due to test-set leakage. While prior works have proposed mitigation strategies such

Davide Bincoletto, Korbinian Stein, Jonas Motyl, Jakob S. Kottmann

The individual optimization of quantum circuit parameters is currently one of the main practical bottlenecks in variational quantum eigensolvers for electronic systems. To this end, several machine learning approaches have been proposed to mitigate the problem. However, such method predominantly aims at training and predicting parameters tailored to individu

Jongseo Lee, Wooil Lee, Gyeong-Moon Park, Seong Tae Kim

Effective explanations of video action recognition models should disentangle how movements unfold over time from the surrounding spatial context. However, existing methods based on saliency produce entangled explanations, making it unclear whether predictions rely on motion or spatial context. Language-based approaches offer structure but often fail to expla

George Tsoukalas, Rahul Saha, Amitayush Thakur, Sabrina Reguyal

We take two key steps in automating the open-ended discovery of new mathematical theories, a grand challenge in artificial intelligence. First, we introduce $\emph{FERMAT}$, a reinforcement learning (RL) environment that models concept discovery and theorem-proving using a set of symbolic actions, opening up a range of RL problems relevant to theory discover

Zhaorun Chen, Zhuokai Zhao, Kai Zhang, Bo Liu

While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity, all of which obstruct the collection of scalable experience data. To address these challenges, we

Richard Dewey, Janos Botyanszki, Ciamac C. Moallemi, Andrew T. Zheng

AI researchers have long focused on poker-like games as a testbed for environments characterized by multi-player dynamics, imperfect information, and reasoning under uncertainty. While recent breakthroughs have matched elite human play at no-limit Texas hold'em, the multi-player dynamics are subdued: most hands converge quickly with only two players engaged

Yao Ji, Guanghui Lan

This paper develops a unified high-order accumulative regularization (AR) framework for convex and uniformly convex gradient norm minimization. Existing high-order methods often exhibit a gap: the function-value residual decreases fast, while the gradient norm converges much slower. To close this gap, we introduce AR that systematically transforms the fast f

Pénélope Azuelos

For any cardinal $\kappa \geq 2$, there is a unique complete real tree whose points all have valence $\kappa$. In this note, we show that, when $\kappa \geq 3$, it is necessary to assume completeness. More precisely, we show that there exist uncountably many homogeneous incomplete real trees whose points all have valence $\kappa$.

Stergios Chatzikyriakidis, Dimitris Papadakis, Sevasti-Ioanna Papaioannou, Erofili Psaltaki

We present an extended Greek Dialectal Dataset (GRDD+) 1that complements the existing GRDD dataset with more data from Cretan, Cypriot, Pontic and Northern Greek, while we add six new varieties: Greco-Corsican, Griko (Southern Italian Greek), Maniot, Heptanesian, Tsakonian, and Katharevusa Greek. The result is a dataset with total size 6,374,939 words and 10

Aashish Sapkota, Niraj Aryal, Xiao Hu, Masaaki Matsuda

Dirac semimetals of the form $A$Mn$X_2$ ($A =$ alkaline-earth or divalent rare earth; $X =$ Bi, Sb) host conducting square-net Dirac-electron layers of $X$ atoms interleaved with antiferromagnetic Mn$X$ layers. In these materials, canted antiferromagnetism can break time-reversal symmetry (TRS) and produce a Weyl semimetallic state. CaMnBi$_2$ was proposed t

J. O. Austin-Harris, P. Sigdel, C. Binegar, S. E. Begg

Utilizing ultracold spinor gases as large-scale, many-body quantum simulation platforms, we establish a toolbox for the precise control, characterization, and detection of nonequilibrium dynamics via internal spinor phases. We develop a method to extract the phase evolution from the observed spin population dynamics, allowing us to define an order parameter

Alif Elham Khan

Medical image labels are often organized by taxonomies (e.g., organ - tissue - subtype), yet standard self-supervised learning (SSL) ignores this structure. We present a hierarchy-preserving contrastive framework that makes the label tree a first-class training signal and an evaluation target. Our approach introduces two plug-in objectives: Hierarchy-Weighte

Sawyer Jack Robertson, Finn Southerland, Erlang Surya

A graph $G=(V,E)$ on $n$ vertices is said to be \emph{distance exceptional} if the equation $D\vec{x} = \vec{1}$ admits no solution $\vec{x}\in\mathbb{R}^{n}$, where $D\in\mathbb{R}^{n\times n}$ is the shortest path distance matrix of $G$. These graphs were first studied by Steinerberger in the context of a notion of discrete curvature (``Curvature on graphs

Nan Li, Albert Gatt, Massimo Poesio

Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for eac

Shakil Ahmed

This paper presents a noise-aware quantum-assisted framework for blockage prediction in reconfigurable intelligent surface (RIS)-enabled wireless networks. The proposed architecture integrates a Quantum Base Station (QBS), a Quantum RIS (QRIS), and a mobile Quantum User Node (QUN). Visual information captured by an onboard RGB camera is amplitude-encoded int

Monika Henzinger, Robin Münk, Harald Räcke

A single-commodity congestion approximator for a graph is a compact data structure that approximately predicts the edge congestion required to route any set of single-commodity flow demands in a network. A hierarchical congestion approximator (HCA) consists of a laminar family of cuts in the graph and has numerous applications in approximating cut and flow p

Mahdi Samiei, Mahdi Mansouri, Mahdieh Soleymani Baghshah

Large language models (LLMs) have achieved remarkable results on tasks framed as reasoning problems, yet their true ability to perform procedural reasoning, executing multi-step, rule-based computations remains unclear. Unlike algorithmic systems, which can deterministically execute long-horizon symbolic procedures, LLMs often degrade under extended reasonin

Peter Senchyna, Calum Hawcroft, Miriam Garcia, Aida Wofford

A remarkable span of frontier astrophysics, from gravitational-wave archaeology to the origin of the elements to interpreting snapshots of the earliest galaxies, depends sensitively on our understanding of massive star formation and evolution in near-pristine, relatively enriched gas. From the surprisingly massive black holes detected by LIGO/Virgo to highly

Bao-Jun Cai, Bao-An Li, Yu-Gang Ma

While the equation of state (EOS) $P(\varepsilon)$ of neutron star (NS) matter has been extensively studied, the EOS-parameter $\phi = P/\varepsilon$ or equivalently the dimensionless trace anomaly $\Delta = 1/3 - \phi$, which quantifies the balance between pressure $P$ and energy density $\varepsilon$, remains far less explored, especially in NS cores. Its

Turgut Yilmaz, Yi Sheng Ng, Anil Rajapitamahuni, Asish Kundu

Angle-resolved photoemission spectroscopy is employed to study the electronic structure of bulk TiSe2 before and after doping with potassium impurities. A splitting in the conduction band into two branches is observed after room-temperature deposition. The splitting energy increases to approximately 130 meV when the sample is cooled to 40 K. One branch exhib

J. Pereira, R. O. Ruiz

This work introduces a surrogate-based model for efficiently estimating the frequency response of dynamic mechanical metamaterials, particularly when dealing with large parametric perturbations and aperiodic substructures. The research builds upon a previous matrix interpolation method applied on top of a Craig-Bampton modal reduction, allowing the variation

Guanning Zeng, Zhaoyi Zhou, Daman Arora, Andrea Zanette

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for post-training large reasoning models (LRMs) using policy-gradient methods such as GRPO. To stabilize training, these methods typically center trajectory rewards by subtracting the empirical mean reward for each prompt. Statistically, this centering acts as a control

Nick Dorey, Paul Luis Roehl

We investigate the mathematical and physical content of the giant graviton expansion of three-dimensional $\mathcal{N}=4$ superconformal field theories in a simplifying limit. We uncover an interesting relation between the coefficients in this expansion, the Hilbert series of certain quiver varieties and the representation theory of vertex algebras. In parti

Rong Jiang, Cong Ma

We study batched nonparametric contextual bandits under a margin condition when the margin parameter $\alpha$ is unknown. To capture the statistical cost of this ignorance, we introduce the regret inflation criterion, defined as the ratio between the regret of an adaptive algorithm and that of an oracle knowing $\alpha$. We show that the optimal regret infla

Camilo García-Cely, Andreas Ringwald

Using the bimetric formalism, we compute the production and emission rates of light spin-2 particles in non-degenerate stellar interiors through photoproduction and bremsstrahlung processes, including the effects of plasma screening. By comparing the resulting energy-loss rates with observational limits on stellar cooling, we derive bounds on the coupling st

Yu-Erh Pan, Ayesha Siddika Nipu

Air quality monitoring is central to environmental sustainability and public health, yet traditional systems remain difficult for non-expert users to interpret due to complex visualizations, limited interactivity, and high deployment costs. Recent advances in Large Language Models (LLMs) offer new opportunities to make sensor data more accessible, but their

Dominik S. Górni, Grzegorz W. Deptuch

Event-driven imagers and sensor arrays commonly employ asynchronous arbiter trees with a synchronous acknowledge to serialize requests. We present an analytical framework that models the root as an \(M/D/1\) queue with deterministic quantum \(T\) and implements losses at the sources through one-slot gating. The admitted rate, loss probability, utilization, a

Prashanth Amireddy, Amik Raj Behera, Srikanth Srinivasan, Madhu Sudan

All known proofs of the PCP theorem rely on multiple "composition" steps, where PCPs over large alphabets are turned into PCPs over much smaller alphabets at a (relatively) small price in the soundness error of the PCP. Algebraic proofs, starting with the work of Arora, Lund, Motwani, Sudan, and Szegedy use at least 2 such composition steps, whereas the "Gap

Reuven Hodges, Hanzhang Yin

We generalize Fulton's determinantal construction of Schur modules to the skew setting, providing an explicit and functorial presentation using only elementary linear algebra and determinantal identities, in parallel with the partition case. Building on the non-iterative straightening formula of the first author for partition shapes, we develop a non-iterati

Warsimakram Katapur, Ambresh Shivaji

We investigate a UV-complete model in which the dark matter (DM) particle interacts with gluons through a colored scalar mediator. This framework provides a phenomenologically viable scenario testable at hadron colliders. While mono-jet signatures are relevant for collider searches, zero-jet processes correspond to complete annihilation of Standard Model (SM

Francesco Corso, Francesco Pierri, Gianmarco De Francisci Morales

We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain, and how easily they can be conditioned into adopting conspiratorial perspectives. Conspiracy beliefs play a central role in the spread of misinformation and in shaping distrust toward institutions, making them a

Pavel Kalugin

We present an open-source Julia-based software toolkit for solving the phase problem using dual-space iterative algorithms. The toolkit is specifically designed for aperiodic crystals and quasicrystals, supporting general space group symmetries in arbitrary dimensions. A key feature is the symmetry-breaking anti-aliasing sampling scheme, optimized for comput

Mohsen Ahmadzadeh, Kaichang Chen, Georges Gielen

Analog/mixed-signal circuits are key for interfacing electronics with the physical world. Their design, however, remains a largely handcrafted process, resulting in long and error-prone design cycles. While the recent rise of AI-based reinforcement learning and generative AI has created new techniques to automate this task, the need for many time-consuming s

Zihan Li, Dan A. Mazilu, Irina Mazilu

Quantum computing offers a powerful new perspective on probabilistic and collective behaviors traditionally taught in statistical physics. This paper presents two classroom-ready modules that integrate quantum computing into the undergraduate curriculum using Qiskit: the quantum random walk and the Ising model. Both modules allow students to simulate and con

Lipeng Zu, Hansong Zhou, Xiaonan Zhang

Offline reinforcement learning (RL) enables training from fixed data without online interaction, but policies learned offline often struggle when deployed in dynamic environments due to distributional shift and unreliable value estimates on unseen state-action pairs. We introduce Behavior-Adaptive Q-Learning (BAQ), a framework designed to enable a smooth and

Hao Li, Shonosuke Sugasawa, Shota Katayama

The Fr\'echet regression is a useful method for modeling random objects in a general metric space given Euclidean covariates. However, the conventional approach could be sensitive to outlying objects in the sense that the distance from the regression surface is large compared to the other objects. In this study, we develop a robust version of the global Fr\'

Md Ahasanul Arafath, Abhijit Kumar Ghosh, Md Rony Ahmed, Sabrin Afroz

Colorectal cancer (CRC) grading is a critical prognostic factor but remains hampered by inter-observer variability and the privacy constraints of multi-institutional data sharing. While deep learning offers a path to automation, centralized training models conflict with data governance regulations and neglect the diagnostic importance of multi-scale analysis

Ashu Gupta

Estimating species and gene trees from sequence data is challenging. Gene tree estimation is often hampered by low phylogenetic signal in alignments, leading to inaccurate trees. Species tree estimation is complicated by incomplete lineage sorting (ILS), where gene histories differ from the species' history. Summary methods like MP-EST, ASTRAL2, and ASTRID i

Zhihang Qin, Yueheng Zhang, Wan Su, Linxin Hou

Conventional fluid-driven soft grippers typically depend on external sources, which limit portability and long-term autonomy. This work introduces a self-contained soft gripper with fixed size that operates solely through internal liquid redistribution among three interconnected bistable snap-through chambers. When the top sensing chamber deforms upon contac

Xingyao Wang, Simon Rosenberg, Juan Michelini, Calvin Smith

Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task. Deploying software agents effectively requires flexibility in implementation and experimentation, reliable and secure execution, and interfaces for users to interact with agents. In this paper, we present the OpenHan

Pradeep Niroula, Shouvanik Chakrabarti, Steven Kordonowy, Niraj Kumar

Large classical datasets are often processed in the streaming model, with data arriving one item at a time. In this model, quantum algorithms have been shown to offer an unconditional exponential advantage in space. However, experimentally implementing such streaming algorithms requires qubits that remain coherent while interacting with an external data stre

Nicolas Tokka

We study the Ising model with an external magnetic field on random tetravalent planar maps and investigate its critical behavior. Explicit expressions for spontaneous magnetization and the susceptibility are computed and the critical exponents $\alpha=-1$ (third order phase transition), $\beta=\frac{1}{2}$ (spontaneous magnetization), $\gamma=2$ (susceptibil

Safoura Mirkhalaf, Helmut Ritsch, Karol Gietka

It is often argued that two linearly coupled quantum harmonic oscillators, even when cooled to their ground state, display no inherently quantum features beyond quantized energy levels. Here, we challenge this view by showing that their classical observables encode genuinely quantum features. In particular, we demonstrate that the characteristic frequency sh

Minzhao Liu, Pradeep Niroula, Matthew DeCross, Cameron Foreman

Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into nearly perfect randomness, but prior demonstrations have required physically co-located, loophole-free Bell tests, constraining the feasibility of remote operation. Here we realize

Eugène Berta, David Holzmüller, Michael I. Jordan, Francis Bach

Post-hoc recalibration methods are widely used to ensure that classifiers provide faithful probability estimates. We argue that parametric recalibration functions based on logistic regression can be motivated from a simple theoretical setting for both binary and multiclass classification. This insight motivates the use of more expressive calibration methods

Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi

Persistent cost and schedule deviations remain a major challenge in the U.S. construction industry, revealing the limitations of deterministic CPM and static document-based estimating. This study presents an integrated 4D/5D digital-twin framework that couples Building Information Modeling (BIM) with natural-language processing (NLP)-based cost mapping, comp

Shiyu Peng, Donnie Pinkston, Jia Yao, Sergei Kliavinek

The Boltzmann transport equation (BTE) with electron-phonon (e-ph) interactions computed from first principles is widely used to study electronic transport and nonequilibrium dynamics in materials. Calculating the e-ph collision integral is the most important step in the BTE, but it remains computationally costly, even with current MPI+OpenMP parallelization

Peixin Zhu, Lisa J. Kewley, Tiger Yu-Yang Hsiao, James Trussler

The detections of bright UV nitrogen emission lines in some high-redshift galaxies suggest unexpectedly high nitrogen-to-oxygen ratios ($\log(\rm N/O)\gtrsim-1.0$) compared to local values ($\log(\rm N/O)\gtrsim-1.5$) at similar metallicities ($12+\log(\rm O/H)\lesssim8.0$). Although the presence of these `N-enhanced' galaxies indicates signatures of atypica

Marie Albenque, Laurent Ménard, Nicolas Tokka

We develop a new bijective framework for the enumeration of bipartite planar maps with control on the degree distribution of black and white vertices. Our approach builds on the blossoming-tree paradigm, introducing a family of orientations on bipartite maps that extends Eulerian and quasi-Eulerian orientations and connects the bijection of Bousquet-M\'elou

Ruiying Wen, Yuntao Dai, Hongyong Wang

Monitoring the in-service aerodynamic performance of airliners is critical for operational efficiency and safety, but using operational Quick Access Recorder (QAR) data for this purpose presents significant challenges. This paper first establishes that the absence of key parameters, particularly aircraft moments of inertia, makes conventional state-propagati

Ailin Zhang, Shigui Ruan, Xi Huo

Bacteria develop resistance to antibiotics through various mechanisms, with the specific mechanism depending on the drug-bacteria pair. It remains unclear, however, which resistance mechanism best supports favorable treatment outcomes, specifically in clearing infections and inhibiting further resistance. In this study, we use periodic ordinary differential

Taito Tashiro, Tomoko Yonezawa, Hirotake Yamazoe

This study investigates how human motion cues can be used to design expressive robot-arm movements. Using the imperfect-information game Geister, we analyzed two types of human piece-moving motions: natural gameplay (unconscious tendencies) and instructed expressions (intentional cues). Based on these findings, we created phase-specific robot motions by vary

Geoff McDonald, Jonathan Bar Or

Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by analyzing packet size and timing patterns in streaming responses.

Shubham Rohal, Dong Yoon Lee, Shijia Pan

People passively interact with ambient surfaces such as tables, chair backs, and armrests throughout daily life, making them natural candidates for ubiquitous tactile interfaces. However, transforming these everyday surfaces into practical haptic interfaces remains challenging. Existing solutions typically rely on dense arrays of actuators, resulting in bulk

Gerhard Knieper

In this article we study geodesic flows on closed Riemannian manifolds without conjugate points and divergence property of geodesic rays. If the fundamental group is Gromov hyperbolic and residually finite we prove, under appropriate assumptions on the expansive set, that the geodesic flow has a unique measure of maximal entropy. This generalizes correspondi

Bennett Lamb, Brandon B. Le

We investigate and quantify the basin geometry and extreme final state uncertainty of two identical electrically asymmetrically coupled Chialvo neurons. The system's diverse behaviors are presented, along with the mathematical reasoning behind its chaotic and nonchaotic dynamics as determined by the structure of the coupled equations. The system is found to

Daniel Perkins, Oscar J. Escobar, Luke Green

We present a detailed study of Deep Q-Networks in finite environments, emphasizing the impact of epsilon-greedy exploration schedules and prioritized experience replay. Through systematic experimentation, we evaluate how variations in epsilon decay schedules affect learning efficiency, convergence behavior, and reward optimization. We investigate how priorit

Oleg R. Musin

Every graph G can be embedded in a Euclidean space as a two-distance set. This allows us to reformulate the analogue of Borsuk's conjecture for two-distance sets in terms of graphs. This conjecture remains open for dimensions from 4 to 63. This short note also discusses an approach for finding counterexamples using graphs, as well as its generalization for s

Luigi D'amico, Eleonora Villa, Fatima Modica Bittordo, Aldo Barca

Complex inference tasks, such as those encountered in Pulsar Timing Array (PTA) data analysis, rely on Bayesian frameworks. The high-dimensional parameter space and the strong interdependencies among astrophysical, pulsar noise, and nuisance parameters introduce significant challenges for efficient learning and robust inference. These challenges are emblemat

Dongkeun Kim, Minsu Cho, Suha Kwak

Social interactions often emerge from subtle, fine-grained cues such as facial expressions, gaze, and gestures. However, existing methods for social interaction detection overlook such nuanced cues and primarily rely on holistic representations of individuals. Moreover, they directly detect social groups without explicitly modeling the underlying interaction

Mehdi Sefidgar Dilmaghani, Francis Fowley, Peter Corcoran

This paper presents a lightweight three-dimensional convolutional neural network (3DCNN) for human activity recognition (HAR) using event-based vision data. Privacy preservation is a key challenge in human monitoring systems, as conventional frame-based cameras capture identifiable personal information. In contrast, event cameras record only changes in pixel

Elsa Jöchl, Anna-Lydia Vieli, Lucy Hale, Felix Helmrich

We study the electrical tunability of ultrastrong light-matter interactions between a single terahertz circuit-based complementary split ring resonator (cSRR) and a two-dimensional electron gas. For this purpose, transmission spectroscopy measurements are performed under the influence of a strong magnetic field at different set points for the electric gate b

Yannis Coutouly, Emmanuel Godard

Distributed computing tasks can be presented with a triple $(\I,\Ou,\Delta)$. The solvability of a colorless task on the Iterated Immediate Snapshot model (IIS) has been characterized by the Colorless Computability Theorem \cite[Th.4.3.1]{HKRbook}. A recent paper~\cite{CG-24} generalizes this theorem for any message adversaries $\ma \subseteq IIS$ by geometr

Mahek Desai, Apoorva Rumale, Marjan Asadinia

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1) detecting malicious cyberattacks and (2) identifying faulty device anomalies, leveraging a dataset of 200,000 records. Ei

Matthew L. Elliott, Matthew O. Jackson

We introduce a parsimonious multi-sector model of international production and use it to study the impact of a disruption in the production of some goods propagates to other goods and consumers, and how that impact depends on the goods' positions in, and overall structure of, the production network. We show that the short-run impact of a disruption can be dr

Aurora Wilde, Marcel Neeleman, Romain Meyer, Roberto Decarli

We present a study determining the spatial offset between the position of the supermassive black hole (as traced through their broad line regions) and the host galaxy in six $z > 6$ quasars. We determined the host galaxy's position from $\lesssim$$0.1^{\prime\prime}$ ($\lesssim$ 600 pc) resolution Atacama Large Millimeter/sub-millimeter Array (ALMA) [CII] 15

Marcelo Actis, Silvano Figueroa, Eduardo M. Garau

In this article, we study dyadic coarsening operators in univariate spline spaces and in tensor-product spline spaces over uniform grids. Our construction is strongly motivated by the work of Bartels, Golub, and Samavati (2006), Some observations on local least squares, BIT, 46(3):455--477. The proposed operators are local in nature and yield approximations

Jing Gao, Shutiao Luo, Yumeng Liu, Yuanming Li

With the rapid advancement of natural language processing (NLP) technologies, the demand for high-quality Chinese document question-answering datasets is steadily growing. To address this issue, we present the Chinese Multi-Document Question Answering Dataset(ChiMDQA), specifically designed for downstream business scenarios across prevalent domains including

Mikel Antoñana, Joseba Makazaga, Ander Murua

The main purpose of this work is to present a SIMD-vectorized implementation of the symplectic 16th-order 8-stage implicit Runge-Kutta integrator based on collocation with Gauss-Legendre nodes (IRKGL16-SIMD), and to show that it can outperform state-of-the-art symplectic explicit integrators for high-precision numerical integrations (in double-precision floa

Dogucan Yaman, Fevziye Irem Eyiokur, Hazım Kemal Ekenel, Alexander Waibel

Video editing-based talking face generation aims to preserve video details such as pose, lighting, and gestures while modifying only lip motion, often using an identity reference image to maintain speaker consistency. However, this mechanism can introduce lip leakage, where generated lips are influenced by the reference image rather than solely by the drivin

Cynthia Dwork, Pranay Tankala

Given a small random sample of $n$-bit strings labeled by an unknown Boolean function, which properties of this function can be tested computationally efficiently? We show an equivalence between properties that are efficiently testable from few samples and properties with structured symmetry, which depend only on the function's average values on an efficient

Azizollah Taheri, Derya Aksaray

This paper addresses a motion planning problem to achieve spatio-temporal-logical tasks, expressed by syntactically co-safe linear temporal logic specifications (scLTL\next), in uncertain environments. Here, the uncertainty is modeled as some probabilistic knowledge on the semantic labels of the environment. For example, the task is "first go to region 1, th

Andrei A. Korigodskii, Oleg D. Kalachev, Artem E. Vasiunik, Matvei V. Urvantsev

This paper presents the innovative design and successful deployment of a pioneering autonomous unmanned aerial system developed for executing the world's largest mural painted by a drone. Addressing the dual challenges of maintaining artistic precision and operational reliability under adverse outdoor conditions such as wind and direct sunlight, our work int

Parthiban Loganathan, Elias Zea, Ricardo Vinuesa, Evelyn Otero

Rapid changes and increasing climatic variability across the widely varied Koppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative downscaling framework that incorporates Vision Transformer (ViT), Convolutional Long Short-Term Memory

Debarshi Chanda

We propose a randomized algorithm with query access that given a graph $G$ with arboricity $\alpha$, and average degree $d$, makes $\widetilde{O}\left(\frac{\alpha}{\varepsilon^2d}\right)$ \texttt{Degree} and $\widetilde{O}\left(\frac{1}{\varepsilon^2}\right)$ \texttt{Random Edge} queries to obtain an estimate $\widehat{d}$ satisfying $\widehat{d} \in (1\pm\

Ádám Gyenge, Timothy Logvinenko

For any noncommutative smooth and proper variety we construct three actions of the Heisenberg algebra on the total Hochschild homology of its symmetric quotient stacks. One is defined algebraically using the orbifold decomposition of Hochschild homology. One decategorifies the 2-categorical Heisenberg action of Gyenge-Koppensteiner-Logvinenko. One is defined