December 2025 arXiv papers — page 24

Ella P. Walsh, Sepehr Ahmadi, Alexander J. Healey, David A. Simpson

Spin relaxometry based on quantum spin systems has developed as a valuable tool in medical and condensed matter systems, offering the advantage of operating without the need for external DC or RF fields. Spin relaxometry with nitrogen-vacancy (NV) centers has been applied to paramagnetic sensing using both single crystal diamond and nanodiamond materials. Ho

Jian Chen, Leilei Su, Cong Sun

Background and Objective: Biomedical Named Entity Recognition (BioNER) is a foundational task in medical informatics, crucial for downstream applications like drug discovery and clinical trial matching. However, adapting general-domain Large Language Models (LLMs) to this task is often hampered by their lack of domain-specific knowledge and the performance d

Aiwei Liu, Minghua He, Shaoxun Zeng, Sijun Zhang

Autoregressive (AR) generation is the standard decoding paradigm for Large Language Models (LLMs), but its token-by-token nature limits parallelism at inference time. Diffusion Language Models (DLLMs) offer parallel decoding by recovering multiple masked tokens per step; however, in practice they often fail to translate this parallelism into deployment speed

Yevgeniya Filanova, Igor Pontes Duff, Pawan Goyal, Peter Benner

Mechanical systems are often characterized only by their response to certain loads known from experiments or simulations. The obtained data can be used for various purposes: system analysis, design of mathematical models, or construction of reduced-order models for further simulations under different loading conditions. The use of data for reduced-order mode

Giampaolo Bonomi

We study how open disagreement influences team performance in a dynamic production game. Team members can hold different priors about the productivity of the available production technologies. Initial beliefs are common knowledge and updated based on observed production outcomes. We show that when only one technology is available, a player works harder early

Michelle Valenzuela, Francisco Leiva, Javier Ruiz-del-Solar

In mobile robotics, coverage navigation refers to the deliberate movement of a robot with the purpose of covering a certain area or volume. Performing this task properly is fundamental for the execution of several activities, for instance, cleaning a facility with a robotic vacuum cleaner. In the mining industry, it is required to perform coverage in several

Jiaqi Shao, Yufeng Miao, Wei Zhang, Bing Luo

Long-horizon reinforcement learning (RL) for large language models faces critical scalability challenges from unbounded context growth, leading to context folding methods that compress interaction history during task execution. However, existing approaches treat summary actions as standard actions, overlooking that summaries fundamentally modify the agent's

Md Badsha Biswas

Infant mortality remains a significant public health concern in the United States, with birth defects identified as a leading cause. Despite ongoing efforts to understand the causes of negative pregnancy outcomes like miscarriage, stillbirths, birth defects, and premature birth, there is still a need for more comprehensive research and strategies for interve

Alexander Kushkuley

Identities of complex irreducible representations of finite groups can be explicitly constructed from character value sets. Among other things, these identities determine representations up to Gassmann equivalency. Some examples of identities related to spherical space forms and to representations of finite $p$-groups are presented. Some old results on irred

Roberto C. G. Porto, Rodrigo C. de Lamare

This work proposes an iterative channel estimation, detection and decoding (ICEDD) scheme for the uplink of multi-user multi-antenna systems assisted by multiple reconfigurable intelligent surfaces (RIS)}. A novel iterative code-aided channel estimation (ICCE) technique is developed that uses low-density parity-check (LDPC) codes and iterative processing to

Youssef Megahed, Robin Ducharme, Inok Lee, Inbal Willner

Cystic hygroma is a high-risk prenatal ultrasound finding that portends high rates of chromosomal abnormalities, structural malformations, and adverse pregnancy outcomes. Automated detection can increase reproducibility and support scalable early screening programs, but supervised deep learning methods are limited by small labelled datasets. This study asses

Amir Azarmehr, Soheil Behnezhad, Shane Ferrante, Mohammad Saneian

We study streaming algorithms for the maximum directed cut problem. The edges of an $n$-vertex directed graph arrive one by one in an arbitrary order, and the goal is to estimate the value of the maximum directed cut using a single pass and small space. With $O(n)$ space, a $(1-\varepsilon)$-approximation can be trivially obtained for any fixed $\varepsilon

Peter Wang, Neelesh Gupta, Viktor Prasanna

The need for long-context reasoning has led to alternative neural network architectures besides Transformers and self-attention, a popular model being Hyena, which employs causal 1D-convolutions implemented with FFTs. Long convolutions enable efficient global context mixing, but requirements for intermediate results exceed the 2-3 MB Block RAM capacity of FP

Kazuma Ito

Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. However, the substantial computational expense associated with existing MLIPs has limited their applicabi

Christos Mystilidis, Christos Tserkezis, Guy A. E. Vandenbosch, N. Asger Mortensen

Mesoscopic models of the optical response of metals have emerged as fundamental building blocks in quantum plasmonics, in principle overcoming the computational bottlenecks of ab initio techniques by implementing aspects of the atomistic description of the metal in otherwise classical calculations. Nonetheless, even these approaches are eventually hindered b

Miezel Talara, Yu Tokizane, Tatsunoshin Mori, Ryota Shikata

We demonstrate a compact stabilization scheme for terahertz (THz) sources by exploiting the complementary advantages of microresonator-based optical frequency combs (microcombs) and resonant tunneling diodes (RTDs). A microcomb-driven photomixing THz signal is employed as the master for injection locking of an RTD, enabling faithful transfer of the microcomb

Hatim Ennayar, Juan Camilo Dueñas Torres, Philipp Brockmann, Hyoungsoo Kim

When a droplet impacts a liquid film, a vortex ring form and govern momentum and species transport. We experimentally investigate vortex ring formation, propagation and instability during droplet impact onto liquid films, with particular emphasis on vortex ring-wall interactions. Particle image velocimetry and laser-induced fluorescence are used to study the

Anton Bugleev

George Andrews and Mohamed El Bachraoui recently explored identities for two-color partitions. In particular, they studied the connection between two-colored partitions and overpartitions. Their proofs were analytical, but they conjectured combinatorial proofs of their results. In this paper we use two-modular diagrams to give a combinatorial proof of their

Sandra Robles, Walter Tangarife, Giorgio Busoni

We study the impact of heavy dark matter (DM) captured in massive stars via scattering(s) with the star constituents. We focus on the first stars and use stellar evolution simulations to track down how DM capture evolves over time from the zero-age main sequence to the late metal-rich stages of stellar evolution. During the early hydrogen-helium-dominated ph

Sachli Abdizadeh, Rachel E. Maizel, Dylan L. Haymore, Jing Zhao

Emerging spin-orbit-torque devices based on spin valves require a thin magnetic free layer to maximize the torque per moment. However, reducing the free-layer thickness to $\lesssim 2$ nm deteriorates the giant magnetoresistance (GMR) signal for electrical readout. Here, we demonstrate that the addition of a 1-nm Cu seed layer promotes sharp interfaces in si

Sashank Chapala, Maksym Mironov, Songgaojun Deng

Large Language Models (LLMs) are increasingly used to simulate population responses, a method known as ``Silicon Sampling''. However, responses to socially sensitive questions frequently exhibit Social Desirability Bias (SDB), diverging from real human data toward socially acceptable answers. Existing studies on social desirability bias in LLM-based sampling

Keegan Boyle, Wenzhao Chen, Anthony Conway

This paper studies locally linear involutions on S^4. Our main theorem shows that any such involution with a 1-dimensional fixed-point set is necessarily linear, provided the fixed-point set admits an equivariant tubular neighborhood. The proof combines modified surgery theory with an equivariant version of the Schoenflies theorem, which we establish here. W

Ge Wang, Andreas Kreisel, Peter J. Hirschfeld

We analyze the spin susceptibility of spin-triplet superconductors from the zero-field to finite-field regimes, with emphasis on its implications for Knight-shift measurements. In the zero-field limit, we review the general expression for the static spin susceptibility and highlight the universal zero-temperature sum rule, $\sum_i \chi_{ii}(T=0)=2\chi^N$, wh

Yue Zhou, Shaan Shah, Tamal Dey, Yucheng Zhou

Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. In this work, we introduce an integrated neuromorphic

Junaid Farooq, Quanyan Zhu

The evolution of networked systems, driven by innovations in software-defined networking (SDN), network function virtualization (NFV), open radio access networks (O-RAN), and cloud-native architectures, is redefining both the operational landscape and the threat surface of critical infrastructures. This book offers an in-depth, interdisciplinary examination

Murtaza Nikzad, Kerem Atas

This paper explores vulnerabilities in RSA cryptosystems that arise from improper prime number selection during key generation. We examine two primary attack vectors: Fermat's factorization method, which exploits RSA keys generated with primes that are too close together, and the Greatest Common Divisor (GCD) attack, which exploits keys that share a common p

Gui-Qiang G. Chen, Feimin Huang, Danli Wang

We establish the existence and compactness of global martingale entropy solutions with finite relative-energy for the stochastically forced system of isentropic Euler equations governed by a general pressure law. To achieve these, a stochastic compensated compactness framework in $L^p$ is developed to overcome the difficulty that the uniform $L^{\infty}$ bou

Mikhail Kapranov, Yan Soibelman

We propose a point of view on resurgence theory based on the study of perverse sheaves on the complex line carrying an algebraic structure with respect to additive convolution. In particular, we lift the concept of alien derivatives introduced originally by J. \'Ecalle, to the framework of perverse sheaves and study its behavior under sheaf-theoretic convolu

F. Xavier Trias, Jesús Ruano, Alexey Duben, Andrey Gorobets

Due to the prohibitive cost of resolving all relevant scales, direct numerical simulations of turbulence remain unfeasible for most real-world applications. Consequently, dynamically simplified formulations are needed for coarse-grained simulations. In this regard, eddy-viscosity models for Large-Eddy Simulation (LES) are widely used both in academia and ind

Dave Ojika, Projjal Gupta, Preethi Budi, Herman Lam

Heterogeneous systems increasingly rely on RISC-V cores as orchestration engines to manage data movement, synchronization, and scheduling across accelerators and reconfigurable fabrics. Conventional performance metrics, such as FLOPs, TOPS/W, or energy per operation, do not capture orchestration efficiency, even though it often dictates sustained system beha

Jun Wang

We present a theoretical study of continual and experiential learning in large language model agents that combine episodic memory with reinforcement learning. We argue that the key mechanism for continual adaptation, without updating model parameters, is reflection: the agent's ability to use past experience to guide future actions. Empirical findings sugges

Lipei Du

Event-by-event fluctuations of the mean transverse momentum (mean-$p_T$) provide a sensitive probe of collective dynamics beyond single-particle spectra and anisotropic flow. We present a systematic study of mean-$p_T$ fluctuation observables using a Bayesian-calibrated multistage hydrodynamic framework, including quantitative comparisons to RHIC measurement

Matey Neykov

We develop polynomial-time algorithms for near-optimal minimax mean estimation under $\ell_2$-squared loss in a Gaussian sequence model under convex constraints. The parameter space is an origin-symmetric, type-2 convex body $K \subset \mathbb{R}^n$, and we assume additional regularity conditions: specifically, we assume $K$ is well-balanced, i.e., there exi

Luis Arenas-Carmona, Claudio Bravo

This work is devoted to the study of representations of finite subgroups of the group of units of quaternion division algebras over a global or local field arising from the inclusion via extension of scalars splitting the algebra. Following a question by Serre, we study the set $\mathrm{IF}$ of conjugacy classes of integral representations that are conjugate

Anaelia Ovalle, Candace Ross, Sebastian Ruder, Adina Williams

Large language models demonstrate strong reasoning capabilities through chain-of-thought prompting, but whether this reasoning quality transfers across languages remains underexplored. We introduce a human-validated framework to evaluate whether model-generated reasoning traces logically support their conclusions across languages. Analyzing 65k reasoning tra

Richard van Dongen

In this thesis, we derive the equations of motion of Chiral Higher Spin Gravity (HiSGRA) in terms of its underlying $L_\infty$-algebra. Chiral HiSGRA contains self-dual Yang-Mills and self-dual gravity as closed subsectors, which themselves form closed subsectors of Yang-Mills and general relativity. We begin by constructing a covariant formulation for self-

Md Jafrul Islam, Athul Kunjipurayil, J. Piekarewicz, A. Volya

The saturation of symmetric nuclear matter -- reflected in the nearly constant interior density of heavy nuclei -- is a defining property of nuclear matter. Modern relativistic energy density functionals (EDFs) calibrated exclusively to the properties of finite nuclei, make robust predictions with quantified uncertainties about the bulk properties of symmetr

A. Durán, N. Reguera

In this paper, the periodic initial-value problem for the fractional nonlinear Schr\"odinger (fNLS) equation is discretized in space by a Fourier spectral Galerkin method and in time by diagonally implicit, high-order Runge-Kutta schemes, based on the composition with the implicit midpoint rule (IMR). Some properties and error estimates for the semidiscretiz

Melisa A. Bayrak, David Ripp, Joseph S. Hill, Marcel Schubert

Sensing biological forces with microscopic lasers is an emerging technique that offers significant advantages over conventional fluorescent probes and imaging-based techniques. However, the limited availability of suitable deformable or elastic microlaser materials is restricting the scale of forces that can be detected which strongly narrows their overall a

Paul Dobre, Jackson Cooper, Xin Wang, Hongzhou Yang

3D Asset insertion and novel view synthesis (NVS) are key components for autonomous driving simulation, enhancing the diversity of training data. With better training data that is diverse and covers a wide range of situations, including long-tailed driving scenarios, autonomous driving models can become more robust and safer. This motivates a unified simulat

Ahmed Abdullah, Sana Fatima, Haroon Mahmood

Hope speech has been relatively underrepresented in Natural Language Processing (NLP). Current studies are largely focused on English, which has resulted in a lack of resources for low-resource languages such as Urdu. As a result, the creation of tools that facilitate positive online communication remains limited. Although transformer-based architectures hav

Xuantao Chen, Sergiu Klainerman

We revisit the problem of solving the Einstein constraint equations in vacuum by a new method, which allows us to prescribe four scalar quantities, representing the full dynamical degrees of freedom of the constraint system. We show that once appropriate gauge conditions have been chosen and four scalars freely specified (modulo $\ell\leq 1$ modes), we can r

Sabine Houy, Bruno Kreyssig, Alexandre Bartel

Compiler-based Control-Flow Integrity (CFI) offers strong forward-edge protection but remains challenging to deploy in large C/C++ software due to visibility mismatches, type inconsistencies, and unintended behavioral failures. We present CFIghter, the first fully automated system that enables strict, type-based CFI in real-world projects by detecting, class

Romuald Lenczewski

Infinitesimal moments associated with infinitesimal freeness and infinitesimal conditional freeness are studied. For free random variables, we consider continuous deformations of moment functionals associated with Motzkin paths $w$, which provide a decomposition of their moments, and we compute their derivatives at zero. We show that the first-order derivati

Nina Fatehi, Antar Kumar Biswas, Masoud H. Nazari

This paper presents a novel learning based framework for predicting power outages caused by extreme events. The proposed approach targets low-probability high-consequence outage scenarios and leverages a comprehensive set of features derived from publicly available data sources. We integrate EAGLE-I outage records from 2014 to 2024 with weather, socioeconomi

J. Armijos-Abendaño, S. A. Eales, M. W. L. Smith

We present a catalogue of 453 molecular clouds in M31 extracted from CO J=1-0 data observed with CARMA using a dendrogram. Our clouds have the mean values of 2.8 km s$^{-1}$, 22.1 pc and 10$^{5.2}$ M$_\odot$ for the velocity dispersion, radius and mass, respectively. The velocity dispersion shows a weak anti-correlation with the galactocentric radius. The cl

Isaac Meza, Rahul Singh

We study instrumental variable regression in data rich environments. The goal is to estimate a linear model from many noisy covariates and many noisy instruments. Our key assumption is that true covariates and true instruments are repetitive, though possibly different in nature; they each reflect a few underlying factors, however those underlying factors may

Yan X Zhang

Motivated by a question of Erd\"{o}s and inquiries by Beeson and Laczkovich, we explore the possible $N$ for which a triangle $T$ can tile into $N$ congruent copies of a triangle $R$. The \emph{reptile} cases (where $T$ is similar to $R$) and the \emph{commensurable-angles} cases (where all angles of $R$ are rational multiples of $\pi$) are well-understood.

Mona Moghadampanah, Adib Rezaei Shahmirzadi, Farhana Amin, Dimitrios S. Nikolopoulos

Multimodal large language models (MLLMs) are built on text-only LLMs by incorporating additional modalities, enabling multimodal understanding and a broader range of applications. However, these additions introduce a previously unexplored energy trade-off across modalities that remains poorly understood, as most prior work focuses on text-only models. In thi

Ahmad. Moradpouri

The species scale is the energy scale at which quantum corrections to Einstein's theory of gravity become significant. In many cases, this scale corresponds to the string mass scale, which can be much lower than the Planck scale in the weak coupling limit. In this note, we explore a variant of the TCC conjecture, which we refer to as the refined TCC. This ve

Daiqi Zhang, Bizhu Wang, Wenqi Zhang, Chen Sun

Intelligent Connected Vehicles (ICVs) rely on high-speed data transmission for efficient and safety-critical services. However, the scarcity of wireless resources limits the capabilities of ICVs. Semantic Communication (SemCom) systems can alleviate this issue by extracting and transmitting task-relevant information, termed semantic information, instead of t

André F. T. Martins

Existing machine learning frameworks operate over the field of real numbers ($\mathbb{R}$) and learn representations in real (Euclidean or Hilbert) vector spaces (e.g., $\mathbb{R}^d$). Their underlying geometric properties align well with intuitive concepts such as linear separability, minimum enclosing balls, and subspace projection; and basic calculus pro

Haiyang Wang, Luca Barletta, Alex Dytso

We study the amplitude-constrained additive white Gaussian noise channel. It is well known that the capacity-achieving input distribution for this channel is discrete and supported on finitely many points. The best known bounds show that the support size of the capacity-achieving distribution is lower-bounded by a term of order $A$ and upper-bounded by a ter

Poojan Vanani, Darsh Patel, Danyal Khorami, Siva Munaganuru

Motion capture remains costly and complex to deploy, limiting use outside specialized laboratories. We present Mesquite, an open-source, low-cost inertial motion-capture system that combines a body-worn network of 15 IMU sensor nodes with a hip-worn Android smartphone for position tracking. A low-power wireless link streams quaternion orientations to a centr

Salvador Rodriguez-Sanz, Monica Hernandez

This work proposes a multimodal diffeomorphic registration method using Neural Ordinary Differential Equations (Neural ODEs). Nonrigid registration algorithms exhibit tradeoffs between their accuracy, the computational complexity of their deformation model, and its proper regularization. In addition, they also assume intensity correlation in anatomically hom

Johnathan Xie, Stefan Stojanov, Cristobal Eyzaguirre, Daniel L. K. Yamins

Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simultaneously, recent efforts in scaling video prediction have demonstrated impressive visual realism, yet they struggle to accurately model complex motions despite massive scale. Inspi

José Rodal

Woodward proposed that driven mass-energy fluctuations could yield a frequency-dependent "Machian" gravitational response $\propto \partial_t^2 M_{\rm loc}(t)$, amplified by a Sciama-scale cosmic potential $\Phi/c^2\sim -1$. We test this claim covariantly in (i) Einstein gravity and (ii) Hoyle-Narlikar (HN) conformal scalar-tensor gravity. In GR, the Landau-

Kumar Sai Bondada, Daniel J. Jakubisin, R. Michael Buehrer

This paper proposes a multistatic radar (MSR) system utilizing a distributed wireless synchronization protocol. The wireless synchronization protocol uses a two-tone waveform exchange for frequency synchronization and a bi-directional waveform exchange for time synchronization, independent of GPS. A Bayesian Cramer-Rao lower bound (BCRLB) framework is develo

Ramiro de la Reza, Felix Llorente de Andrés, Emilio J. Alfaro, Carolina Chavero

The stellar evolution of lithium-rich (Li-rich) giant stars at very low metallicities remains largely unexplored to date. Using two recent large LAMOST catalogues of field, low-mass giant stars (both Li-rich and Li-poor) with metallicities from -4.0 to -1.0, we studied the conditions for Li enrichment and the distribution of stellar rotations in the Galactic

José Luis Romero, Cristóbal Isla, Matías Toro, Éric Tanter

Efficiently supporting sound gradual typing in a language with structural types is challenging. To date, the Grift compiler is the only close-to-the-metal implementation of gradual typing in this setting, exploiting coercions for runtime checks, and further extended with monotonic references for efficient access to statically-typed data structures. On the la

Anumanchi Agastya Sai Ram Likhit, Rajib Saha

Detecting primordial B-mode polarization of the Cosmic Microwave Background (CMB) provides a direct probe of inflationary gravitational waves. However, the signal is extremely faint and contaminated by gravitational lensing, instrumental noise, and astrophysical foregrounds. Here we present a score-based diffusion approach, formulated using variance-explodin

Yoshith Roy Kotla, Varshith Roy Kotla

Deploying large language models (LLMs) in high-stakes domains requires rigorous uncertainty quantification, yet standard softmax probabilities are often poorly calibrated. We present a systematic study of Adaptive Prediction Sets (APS) applied to next-token prediction in transformer-based models with large vocabularies (greater than 250,000 tokens). Our cent

ZhenQi Chen, TsaiChing Ni, YuanFu Yang

Recent text-to-image diffusion models have achieved remarkable visual fidelity but often struggle with semantic alignment to complex prompts. We introduce CritiFusion, a novel inference-time framework that integrates a multimodal semantic critique mechanism with frequency-domain refinement to improve text-to-image consistency and detail. The proposed CritiCo

Abderaouf Bahi, Amel Ourici, Chaima Lagraa, Siham Lameche

This study provides a comprehensive overview of recent advances in electrochemical energy storage, including Na+ -ion, metal-ion, and metal-air batteries, alongside innovations in electrode engineering, electrolytes, and solid-electrolyte interphase control. It also explores the integration of machine learning, digital twins, large language models and predic

José P. S. Lemos

This article presents a comprehensive analysis of the physics of gravitational waves, exploring both the theoretical foundations and the most recent experimental advances. After a general introduction to the theory of general relativity and its major implications, the article discusses the history of gravitational waves, from their prediction by Einstein to

Eran Vos, Peter Huybers, Eli Tziperman

Internal modes of climate variability, such as El Ni\~no and the North Atlantic Oscillation, can have strong influences upon distant weather patterns, effects that are referred to as "teleconnections". The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covarian

Trung Hieu Giang, Ngoc Quynh Nguyen

This paper studies a nonlinear shallow shell model proposed by Donnell, Vlasov, Mushtari, Galimov, and Koiter. More specifically, we address the question concerning the asymptotic behavior of minimizing solutions. Our result can be applied to general applied forces. Thus, it substantially extends the one given in \cite{oana2} whereby the tangential component

Sergey Salishev

This thesis develops signal-processing algorithms and implementation schemes under constraints of minimal parallelism and memory space, with the goal of improving energy efficiency of low-power computing hardware. We propose (i) a power/energy consumption model for clocked CMOS logic that supports selecting optimal parallelism, (ii) integer-friendly approxim

Donghwa Kang, Shana Moothedath

Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches have been extensively studied, decentralized methods remain largely underexplored. We study decentralized multi-task representation learning in which the features share a low-rank s

Jiashu Dong, Jiabing Xiang, Lisheng Geng, Suqing Tian

X-ray computed tomography (CT) is widely used in medical imaging, with sparse-view reconstruction offering an effective way to reduce radiation dose. However, ill-posed conditions often result in severe streak artifacts. Recent advances in deep learning-based methods have improved reconstruction quality, but challenges still remain. To address these challeng

Jethro Masis

This paper examines the intellectual legacy of Philip E. Agre by situating his work at the intersection of artificial intelligence, philosophy, and critical theory. It reconstructs Agre's proposal of a critical technical practice, according to which AI should be understood not merely as an engineering discipline but as a form of mathematized philosophy shape

Xiang Cheng, Yulan Hu, Xiangwen Zhang, Lu Xu

Travel planning is a natural real-world task to test large language models' (LLMs) planning and tool-use abilities. Although prior work has studied LLM performance on travel planning, existing settings still differ from real-world needs, mainly due to limited domain coverage, insufficient modeling of users' implicit preferences in multi-turn conversations, a

Achraf Hsain, Fouad Mohammed Abbou

This paper presents the first application of quantum generative models to learned latent space representations of computational fluid dynamics (CFD) data. While recent work has explored quantum models for learning statistical properties of fluid systems, the combination of discrete latent space compression with quantum generative sampling for CFD remains une

Katherine Elkins, Jon Chun

Large language models exhibit systematic negation sensitivity, yet no operational framework exists to measure this vulnerability at deployment scale, especially in high-stakes decisions. We introduce Syntactic Framing Fragility (SFF), a framework for quantifying decision consistency under logically equivalent syntactic transformations. SFF isolates syntactic

David Tayouri, Elad Duani, Abed Showgan, Ofir Manor

Understanding the risks associated with an enterprise environment is the first step toward improving its security. Organizations employ various methods to assess and prioritize the risks identified in cyber threat intelligence (CTI) reports that may be relevant to their operations. Some methodologies rely heavily on manual analysis (which requires expertise

Arya Rashidinejad Meibodi, Mahbod Gholamali Sinaki, Khalil Alipour

The State-Dependent Riccati Equation (SDRE) technique generalizes the classical algebraic Riccati formulation to nonlinear systems by designing an input to the system that optimally(suboptimally) regulates system states toward the origin while simultaneously optimizing a quadratic performance index. In the SDRE technique, we solve the State-Dependent Riccati

Casey Hampson, Marco Guzzi

We present a generalization of the $x$-space $\texttt{Candia}$ algorithm to next-to-next-to-next-to-leading order (N$^3$LO) accuracy in Quantum Chromodynamics (QCD) for solving the DGLAP evolution equations for unpolarized parton densities in the nucleon. The algorithm is based on logarithmic expansions of the solution and can be extended to all orders in QC

Arnaud Rémi, François Damanet, Christophe Geuzaine

Discretizing Helmholtz problems via finite elements yields linear systems whose efficient solution remains a major challenge for classical computation. In this paper, we investigate how variational quantum algorithms could address this challenge. We first show that, for regular meshes, a block encoding of the operators $A$ and $A^\dagger A$ arising from the

Qiankun Li, Feng He, Huabao Chen, Xin Ning

In the big data era, the computer vision field benefits from large-scale datasets such as LAION-2B, LAION-400M, and ImageNet-21K, Kinetics, on which popular models like the ViT and ConvNeXt series have been pre-trained, acquiring substantial knowledge. However, numerous downstream tasks in specialized and data-limited scientific domains continue to pose sign

Saksham Malik, Mohammad Salman, Ruchi Das

We investigate the dynamics of periodic non-autonomous discrete dynamical systems on uniform spaces and topological spaces, focusing on the extension of the classical Auslander-Yorke dichotomy to these settings. We prove various dichotomy theorems in the uniform space framework, showing that a minimal periodic non-autonomous system is either sensitive or equ

Lou van den Dries

Let $C\subseteq M$ be stably embedded in a structure $\cM=(M;\dots)$. We consider {\em Fubini measures} on the subcategory $\Def(C)$ of the category $\Def(\cM)$ of definable sets in $\cM$, with ``Fubini" signaling good behaviour in definable families. We show that such a Fubini measure extends uniquely to the larger subcategory of $\Def(\cM)$ whose objects a

Ikbar Faiz

This study re-evaluates the previously assumed interaction between the galaxy pair UGC 694 and IC 412 through a combined kinematical and photometric approach. While morphological features such as tidal bridges have been visually identified in past surveys, our quantitative analysis using SDSS DR16 kinematical data reveals a significant radial velocity offset

Julia Kończal, Michał Balcerek, Krzysztof Burnecki

In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates

Nabil Awan, Richard J. Chappell

Ranked set sampling (RSS) is a cost-efficient study design that uses inexpensive baseline ranking to select a more informative subset of individuals for full measurement. While RSS is well known to improve precision over simple random sampling (SRS) for uncensored outcomes, survival analysis under RSS has largely been limited to estimation of the Kaplan-Meie

Gang v. Chen, Congjun Wu

The nominal divide between $p$- and $d$-electron systems often obscures a deep underlying unity in condensed matter physics. This review elucidates the orbital homology between the $p$ and $t_{2g}$ orbital manifolds, establishing the correspondence that extends from minimal model Hamiltonians to the complex behaviors of real quantum materials. We demonstrate

Shravan Saranyan, Pramit Saha

Left ventricular ejection fraction (LVEF) is a key indicator of cardiac function and plays a central role in the diagnosis and management of cardiovascular disease. Echocardiography, as a readily accessible and non-invasive imaging modality, is widely used in clinical practice to estimate LVEF. However, manual assessment of cardiac function from echocardiogr

Argha Kamal Samanta, Deepak Mewada, Monalisa Sarma, Debasis Samanta

Clinical electroencephalography is routinely used to evaluate patients with diverse and often overlapping neurological conditions, yet interpretation remains manual, time-intensive, and variable across experts. While automated EEG analysis has been widely studied, most existing methods target isolated diagnostic problems, particularly seizure detection, and

Jiaming Liu, Meng Li

Variational Bayes (VB) is a popular and computationally efficient method to approximate the posterior distribution in Bayesian inference, especially when the exact posterior is analytically intractable and sampling-based approaches are computationally prohibitive. While VB often yields accurate point estimates, its uncertainty quantification (UQ) is known to

B. Eslam Panah, B. Hamil, Manuel E. Rodrigues

In this work, we construct new classes of topological black hole solutions in anti-de Sitter (AdS) spacetime using a novel model of nonlinear electrodynamics called Modification Maxwell (ModMax) and Modification phantom or Modification anti-Maxwell (ModAMax). We then evaluate the thermodynamic quantities and verify the first law of thermodynamics. Our study

Amir El-Ghoussani, André Kaup, Nassir Navab, Gustavo Carneiro

We propose a monocular depth estimation method based on visual autoregressive (VAR) priors, offering an alternative to diffusion-based approaches. Our method adapts a large-scale text-to-image VAR model and introduces a scale-wise conditional upsampling mechanism with classifier-free guidance. Our approach performs inference in ten fixed autoregressive stage

Manlio De Domenico

The possibility that evolutionary forces -- together with a few fundamental factors such as thermodynamic constraints, specific computational features enabling information processing, and ecological processes -- might constrain the logic of living systems is tantalizing. However, it is often overlooked that any practical implementation of such a logic requir

Shuyu Gan, James Mooney, Pan Hao, Renxiang Wang

Large Language Model (LLM) agents can increasingly automate complex reasoning through Test-Time Scaling (TTS), iterative refinement guided by reward signals. However, many real-world tasks involve multi-stage pipeline whose final outcomes lack verifiable rewards or sufficient data to train robust reward models, making judge-based refinement prone to accumula

Nayan Mondal, Nemani V. Suryanarayana

We present all bosonic giant and dual-giant type configurations of a probe D3-brane in the BPS single-parameter Gutowski-Reall black hole in 10d type IIB supergravity that do not break any of its supersymmetries. The resulting D3-brane world-volumes can be given by the common zeros of three holomorphic functions of five complex scalar harmonics of the geomet

Michele Arzano, Alessandra D'Alise, Simone del Rosso, Domenico Frattulillo

A massless scalar field in two spacetime dimensions splits into two independent sectors of left and right-moving modes on the light cone. At the quantum level, these two sectors carry a representation of the group of affine transformations of the real line, with translations corresponding to transformations generated by light-cone momenta and dilations given

Yidi Liu, Zihao Fan, Jie Huang, Jie Xiao

Reinforcement Learning with Human Feedback (RLHF) has proven effective in image generation field guided by reward models to align human preferences. Motivated by this, adapting RLHF for Image Super-Resolution (ISR) tasks has shown promise in optimizing perceptual quality with Image Quality Assessment (IQA) model as reward models. However, the traditional IQA

Kamil Hassan, Henrik Sandberg

The stealth of false data injection attacks (FDIAs) against feedback sensors in linear time-varying (LTV) control systems is investigated. In that regard, the following notions of stealth are pursued: For some finite $\epsilon > 0$, i) an FDIA is deemed $\epsilon$-stealthy if the deviation it produces in the signal that is monitored by the anomaly detector r

Alex Chan

In this article, I present a theorem determining a criterion for divisibility of two generalized Mersenne numbers, which are repunits of the same length in base-$a^m$ and base-$a^k$. In addition to the general proof, I present an alternative proof for a special case of the theorem.

Bahareh Rahmani, Harsha Reddy Bindela, Rama Kanth Reddy Gosula, Krishna Yedubati

A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes, particularly in areas with limited access to radiologists and healthcare resources. In this proj

F. Xavier Trias, Àdel Alsalti-Baldellou, Assensi Oliva

We aim to answer the following question: is the complexity of numerically solving the Poisson equation increasing or decreasing for very large simulations of incompressible flows? Physical and numerical arguments are combined to derive power-law scalings at very high Reynolds numbers. A theoretical convergence analysis for both Jacobi and multigrid solvers d

Haruki Emori, Hiroyasu Tajima

The out-of-time-ordered correlator (OTOC) is a powerful tool for probing quantum information scrambling, a fundamental process by which local information spreads irreversibly throughout a quantum many-body system. Experimentally measuring the OTOC, however, is notoriously challenging due to the need for time-reversed evolution. Here, we present an experiment