May 2025 arXiv papers — page 72

Augusto Tagle, Javier Ruiz-del-Solar, Felipe Tobar

Offline reinforcement learning (RL) recovers the optimal policy $\pi$ given historical observations of an agent. In practice, $\pi$ is modeled as a weighted version of the agent's behavior policy $\mu$, using a weight function $w$ working as a critic of the agent's behavior. Though recent approaches to offline RL based on diffusion models have exhibited prom

Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi

Diffusion models have demonstrated state-of-the-art performance across vision, language, and scientific domains. Despite their empirical success, prior theoretical analyses of the sample complexity suffer from poor scaling with input data dimension or rely on unrealistic assumptions such as access to exact empirical risk minimizers. In this work, we provide

Yash Kumar Atri, Ahmed Alaa, Thomas Hartvigsen

Large language models (LLMs) have revolutionized natural language processing, yet their practical utility is often limited by persistent issues of hallucinations and outdated parametric knowledge. Although post-training model editing offers a pathway for dynamic updates, existing methods frequently suffer from overfitting and catastrophic forgetting. To tack

Jack Goffinet, Youngjo Min, Carlo Tomasi, David E. Carlson

Accurate and scalable quantification of animal pose and appearance is crucial for studying behavior. Current 3D pose estimation techniques, such as keypoint- and mesh-based techniques, often face challenges including limited representational detail, labor-intensive annotation requirements, and expensive per-frame optimization. These limitations hinder the st

Miao Li, Wenhao Ding, Haohong Lin, Yiqi Lyu

Training and evaluating autonomous driving algorithms requires a diverse range of scenarios. However, most available datasets predominantly consist of normal driving behaviors demonstrated by human drivers, resulting in a limited number of safety-critical cases. This imbalance, often referred to as a long-tail distribution, restricts the ability of driving a

Míriam Máximo, Antonio Santo, Arturo Gil, Mónica Ballesta

The location of a robot is a key aspect in the field of mobile robotics. This problem is particularly complex when the initial pose of the robot is unknown. In order to find a solution, it is necessary to perform a global localization. In this paper, we propose a method that addresses this problem using a coarse-to-fine solution. The coarse localization reli

Adel Ebadi, Raha Kalantarpour, Fariborz Ataei, Hesam Ami Ahmadi

Ternary interactions between hetero-fluid particles, particularly the dynamics of droplets spreading over curved fluid interfaces remain insufficiently understood compared to the two-phase coalescence. In this study, we combine lattice Boltzmann simulations, high-speed imaging, and theoretical scaling to investigate the collision and encapsulation of an air

Caique C. Oliveira, Pedro A. S. Autreto

The catalytic properties of Pt2XSe3 (X = Hg, Zn) in hydrogen-electrode- (HER-) based catalysts have been investigated based on state-of-the-art ab initio simulations. Our results show that the late transition metal sites (Hg and Zn) exhibit the best activity for HER in an acidic environment. Furthermore, lattice stretching and compression can effectively mod

Rajarshi Bhattacharya, Shakeeb Murtaza, Christian Desrosiers, Jose Dolz

Person re-identification (ReID) models are known to suffer from camera bias, where learned representations cluster according to camera viewpoints rather than identity, leading to significant performance degradation under (inter-camera) domain shifts in real-world surveillance systems when new cameras are added to camera networks. State-of-the-art test-time a

Aurora Poggi, Giuseppe Alessio D'Inverno, Hjalmar Brismar, Ozan Öktem

Data-driven discovery of dynamics in biological systems allows for better observation and characterization of processes, such as calcium signaling in cell culture. Recent advancements in techniques allow the exploration of previously unattainable insights of dynamical systems, such as the Sparse Identification of Non-Linear Dynamics (SINDy), overcoming the l

Jian Tang, Thomas Siyuan Ding, Chengdong Wang, Ning Mao

The quantum spin Hall (QSH) effect, first predicted in graphene by Kane and Mele in 2004, has emerged as a prototypical platform for exploring spin-orbit coupling, topology, and electronic interactions. Initially realized experimentally in quantum wells exhibiting characteristic QSH signatures, the field has since expanded with the discovery of van der Waals

Yuqi Jia, Zedian Shao, Yupei Liu, Jinyuan Jia

Large Language Models (LLMs) are vulnerable to prompt injection attacks, and several defenses have recently been proposed, often claiming to mitigate these attacks successfully. However, we argue that existing studies lack a principled approach to evaluating these defenses. In this paper, we argue the need to assess defenses across two critical dimensions: (

Rahul Thomas, Louai Zahran, Erica Choi, Akilesh Potti

Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such as Secure Multiparty Computation (SMPC), often rely on cryptographic methods. However, these methods are thousands of times slower than sta

Naghmeh Jamali, Milad Mohammadi, Danial Baledi, Zahra Rezvani

Medical consumer question answering (CQA) is crucial for empowering patients by providing personalized and reliable health information. Despite recent advances in large language models (LLMs) for medical QA, consumer-oriented and multilingual resources, particularly in low-resource languages like Persian, remain sparse. To bridge this gap, we present PerMedC

Xiaofan Shi, Ziwei Dou, Dong Pan, Guoan Li

Modern industry seeks next-generation microelectronics with ultra-low dissipation and noise beyond semiconducting systems, where the superconducting electronics offer promise. Its physical foundation is the superconducting diode effect (SDE) with nonreciprocal supercurrent. SDE has hitherto mainly relied on material-specific intrinsic symmetry breaking in su

Sophie Libkind, David Jaz Myers

We present a unified framework for categorical systems theory which packages a collection of open systems, their interactions, and their maps into a symmetric monoidal loose right module of systems over a symmetric monoidal double category of interfaces and interactions. As examples, we give detailed descriptions of (1) the module of open Petri nets over und

William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft

We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks bef

Xinchen Du, Wanrong Zhu, Wei Biao Wu, Sen Na

Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online inference methods have become crucial for enabling real-time decision-making without the need to store historical data. In this work, we de

Jeongone Seo, Tawfiq Ammari

Older immigrant adults face unique barriers to digital participation, often framed as skill deficits. Through a community-based study with 22 older Korean immigrants in the greater New York area, we reframe these behaviors as active strategies. We identify pragmatic disengagement, where users selectively reject emotionally taxing or linguistically risky tech

Licheng Pan, Yongqi Tong, Xin Zhang, Xiaolu Zhang

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they often refuse to answer legitimate queries--a phenomenon known as overrefusal. Overrefusal typically stems from over-conservative safety alignment, causing models to treat many reasonable prompts as potentially risky. To systematically understand this

David G. Harris, Vladimir Kolmogorov

We consider the problem of estimating the partition function $Z(\beta)=\sum_x \exp(\beta(H(x))$ of a Gibbs distribution with the Hamiltonian $H:\Omega\rightarrow\{0\}\cup[1,n]$. As shown in [Harris & Kolmogorov 2024], the log-ratio $q=\ln (Z(\beta_{\max})/Z(\beta_{\min}))$ can be estimated with accuracy $\epsilon$ using $O(\frac{q \log n}{\epsilon^2})$ calls

Nicolas Küchler, Ivan Petrov, Conrad Grobler, Ilia Shumailov

For nearly a decade the academic community has investigated backdoors in neural networks, primarily focusing on classification tasks where adversaries manipulate the model prediction. While demonstrably malicious, the immediate real-world impact of such prediction-altering attacks has remained unclear. In this paper we introduce a novel and significantly mor

Zhuozhuo Joy Liu, Farhan Samir, Mehar Bhatia, Laura K. Nelson

LLMs have been demonstrated to align with the values of Western or North American cultures. Prior work predominantly showed this effect through leveraging surveys that directly ask (originally people and now also LLMs) about their values. However, it is hard to believe that LLMs would consistently apply those values in real-world scenarios. To address that,

Kun Huang, Andrés Galindo-Olarte, Rodrigo González-Hernández, Irene M. Gamba

In this work, we introduce a structure-preserving local discontinuous Galerkin (LDG) method \cite{cockburn1998local} for solving the non-local non-linear Fokker-Planck-Landau (FPL) equations. We rephrase the structure-preserving strategy of Shiroto and Sentoku\cite{shiroto2019structure} in the language of numerical analysis, and extend it to the LDG framewor

Sifan Wu, Huan Zhang, Yizhan Li, Farshid Effaty

The emergence of Multimodal Large Language Models (MLLMs) that integrate vision and language modalities has unlocked new potentials for scientific reasoning, outperforming prior benchmarks in both natural language and coding domains. Current materials science evaluation datasets such as MaScQA and SciQA remain largely text-based and fail to capture the visua

Jacob Kewarth

Here we consider the set $\Sigma_S$ of roots of power series whose coefficients lie in a given set $S$ and how such sets of roots vary as the set $S$ varies. We give an estimate of the depth that complex roots can reach into the disc, offer some criterion for the set of roots to be connected or disconnected, and show that for two finite symmetric sets $S$ an

Lucia Morotti

It is conjectured that irreducible representations of symmetric groups have no non-trivial self-extension over fields of odd characteristic. We improve on partial results showing evidence of this conjecture.

Mariano Rivera, Angello Hoyos

We introduce CoLoRA (Convolutional Low-Rank Adaptation), a parameter-efficient fine-tuning method for convolutional neural networks (CNNs). CoLoRA extends LoRA to convolutional layers by decomposing kernel updates into lightweight depthwise and pointwise components. This design reduces the number of trainable convolutional-update parameters by over 80\% comp

Nathan Benjamin, Cyuan-Han Chang, A. Liam Fitzpatrick, Tobi Ramella

We study the spectrum of scalar primary operators in any two-dimensional conformal field theory. We show that the scalars alone obey a nontrivial crossing equation. This extends previous work that derived a similar equation for Narain conformal field theories. Additionally, we show that at high temperature, the difference between the true scalar partition fu

Matan Haroush, Daniel Soudry

Accelerator memory and networking constraints have emerged as dominant bottlenecks when training large language models LLMs with billions of parameters. Existing low rank gradient estimators such as GaLoRE and FLORA compress gradients and optimizer tensors by projecting weight gradients onto a rank r subspace, enabling LLM training on consumer hardware. Yet,

Maksym Pyatnytskyy

We extended the $O-C$ diagram for V965 Cep with all currently available observations in the Johnson V filter and added unfiltered ones. The new, up-to-date $O-C$ diagram shows that the seeming period change previously revealed by the author does not occur uniformly. Instead, the near-parabolic part of the $O-C$ diagram can be a part of a periodic curve. This

Sushant Sharma Chaudhary, Gianmarco Puleo, Marco Cavaglia

Low-latency pipelines analyzing gravitational waves from compact binary coalescence events rely on matched filter techniques. Limitations in template banks and waveform modeling, as well as non-stationary detector noise cause errors in signal parameter recovery, especially for events with high chirp masses. We present a quantile regression neural network mod

Saeed Hashemi, Genchang Peng, Mehrdad Nourani, Omar Nofal

Stereo-electroencephalography (SEEG) is an invasive technique to implant depth electrodes and collect data for pre-surgery evaluation. Visual inspection of signals recorded from hundreds of channels is time consuming and inefficient. We propose a machine learning approach to rank the impactful channels by incorporating clinician's selection and computational

Jorge Reyes, Ping-Hsuan Tsai, Ian Moore, Honghu Liu

Large eddy simulation reduced order models (LES-ROMs) are ROMs that leverage LES ideas (e.g., filtering and closure modeling) to construct accurate and efficient ROMs for convection-dominated (e.g., turbulent) flows. Eddy viscosity (EV) ROMs (e.g., Smagorinsky ROM (S-ROM)) are LES-ROMs whose closure model consists of a diffusion-like operator in which the vi

Nikolai N. Shchechilin, Nicolas Chamel, Andrey I. Chugunov

Historically, a sequence of nuclear pasta shapes was predicted to appear in the deepest region of the inner crust of a neutron star within the compressible liquid-drop picture, when the filling fraction $u$ exceeds some threshold values. However, later calculations showed that these values depend on the details of the liquid-drop model. Here we investigate t

L. A. Gutiérrez-Soto, M. Belén Mari, W. A. Weidmann, F. R. Faifer

Planetary nebulae (PNe) are pivotal for advancing our knowledge of stellar evolution and galactic chemical enrichment. Recent progress in surveys and data analysis has revolutionized PN research, leading to the discovery of new objects and deeper insights into their properties. We have devised a novel photometric selection method, integrating GAIA and Pan-ST

Nick Heesters, David Chemaly, Oliver Müller, Elisabeth Sola

The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-s

Karly Hou, Wanhua Li, Hanspeter Pfister

Recently, Gaussian Splatting methods have emerged as a desirable substitute for prior Radiance Field methods for novel-view synthesis of scenes captured with multi-view images or videos. In this work, we propose a novel extension to 4D Gaussian Splatting for dynamic scenes. Drawing on ideas from residual learning, we hierarchically decompose the dynamic scen

Hudson Silva Borges, Marco Tulio Valente

GitHub is the most popular social coding platform and widely used by developers and organizations to host their open-source projects around the world. Besides that, the platform has a web API that allow developers collect information from public repositories hosted on it. However, collecting massive amount of data from GitHub can be very challenging due to e

Mustafa Sencer Aydın

We establish a new BKM-type blow-up criterion for solutions of the incompressible Euler equations that belong to Sobolev or H\" older spaces. Our criterion involves the $L^2$ norm in time of the $L^\infty$ norm of the first order tangential derivatives. Moreover, it applies to various domains such as the full space, the half-space, torus, (in)finite channel,

Lan Wei, Dandan Zhang

Optical microrobots, manipulated via optical tweezers (OT), have broad applications in biomedicine. However, reliable pose and depth perception remain fundamental challenges due to the transparent or low-contrast nature of the microrobots, as well as the noisy and dynamic conditions of the microscale environments in which they operate. An open dataset is cru

Gefei Shen, Yung-Hong Sun, Yu Hen Hu, Hongrui Jiang

Two sampling strategies are investigated to enhance efficiency in training a deep learning object detection model. These sampling strategies are employed under the assumption of Lipschitz continuity of deep learning models. The first strategy is uniform sampling which seeks to obtain samples evenly yet randomly through the state space of the object dynamics.

Lucina Kainka, Reza Shaebani, Kathi Kaiser, Jonas Bosche

Circulating tumor cells (CTCs) have crucial roles in the spread of tumors during metastasis. A decisive step is the extravasation of CTCs from the blood stream or lymph system, which depends on the ability of cells to attach to vessel walls. Recent work suggests that such adhesion is facilitated by microtubule (MT)-based membrane protrusions called microtent

Avinash Madasu, Vasudev Lal, Phillip Howard

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks:

Jie Hu, Yi-Ting Ma, Do Young Eun

We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbol{\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-re

Bryce Palmer, Scott Weady, Michael O'Brien, Blakesley Burkhart

Suspensions of swimming particles exhibit complex collective behaviors driven by hydrodynamic interactions, showing persistent large-scale flows and long-range correlations. While heavily studied, it remains unclear how such structures depend on the system size and swimmer concentration. To address these issues, we simulate very large systems of suspended sw

Jinyan Su, Claire Cardie

Large language models (LLMs) have demonstrated strong reasoning abilities in mathematical tasks, often enhanced through reinforcement learning (RL). However, RL-trained models frequently produce unnecessarily long reasoning traces -- even for simple queries -- leading to increased inference costs and latency. While recent approaches attempt to control verbos

Kristoffer Andersson, Alessandro Gnoatto, Camilo Andrés García Trillos

We present the first deep-learning solver for backward stochastic Volterra integral equations (BSVIEs) and their fully-coupled forward-backward variants. The method trains a neural network to approximate the two solution fields in a single stage, avoiding the use of nested time-stepping cycles that limit classical algorithms. For the decoupled case we prove

Soumyaranjan Khuntia, Wageesh Mishra

Understanding the thermal and turbulence properties of interplanetary coronal mass ejections (ICMEs) is essential for analyzing their evolution and interactions with the surrounding medium. This study explores these characteristics across different regions of two distinct ICMEs observed at 1 AU, utilizing in-situ measurements from the Wind spacecraft. The po

Hong Zhong, Ying Peng, Feng Lin, Ange Benise Niyikiza

Cubic boron arsenide (c-BAs) has been theoretically predicted to exhibit thermal conductivity \k{appa} comparable to that of diamond, yet experimental measurements have plateaued at ~1300W/mK. We report room-temperature \k{appa} exceeding 2000W/mK in c-BAs, on par with single-crystal diamond. This finding is enabled by high-quality single crystals and a newl

Michel Bauer

We study from a statistical mechanics viewpoint some of the simplest mathematical objects, finite pure sets. Starting from the empty set, new generations are produced step by step, sets of the next generation being those whose elements are the sets of the current generation. We compute in particular correlations and limiting laws for chains of various length

Peeter Saari, Ioannis Besieris

The behavior of wave signals in the far zone is not only of theoretical interest but also of paramount practical importance in communications and other fields of applications of optical, electromagnetic or acoustic waves. Long time ago T. T. Wu introduced models of 'electromagnetic missiles' whose decay could be made arbitrarily slower than the usual inverse

Zifu Wan, Yaqi Xie, Ce Zhang, Zhiqiu Lin

Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, information retrieval, and grounding. However, many of these models perceive objects as indivisible, overlooking the components that constitute them. Understanding these components and thei

Scott Seidenberger, Anindya Maiti

We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events, EtherBee provides unique insights into benign and malicious activit

Saar Cohen, Noa Agmon, Uri Shaham

Graph Neural Networks (GNNs) are key tools for graph representation learning, demonstrating strong results across diverse prediction tasks. In this paper, we present Convexified Message-Passing Graph Neural Networks (CGNNs), a novel and general framework that combines the power of message-passing GNNs with the tractability of convex optimization. By mapping

Yash Patel, Unique Subedi, Ambuj Tewari

We consider the problem of learning the evolution operator for the time-dependent Schr\"{o}dinger equation, where the Hamiltonian may vary with time. Existing neural network-based surrogates often ignore fundamental properties of the Schr\"{o}dinger equation, such as linearity and unitarity, and lack theoretical guarantees on prediction error or time general

Pallavi Jain, Andrzej Kaczmarczyk

In a recently introduced model of successive committee elections (Bredereck et al., AAAI-20) for a given set of ordinal or approval preferences one aims to find a sequence of a given length of "best" same-size committees such that each candidate is a member of a limited number of consecutive committees. However, the practical usability of this model remains

Mingyan Gao, Yanzi Li, Banruo Liu, Yifan Yu

Multi-agent systems (MAS) decompose complex tasks and delegate subtasks to different large language model (LLM) agents and tools. Prior studies have reported the superior accuracy performance of MAS across diverse domains, enabled by long-horizon context tracking and error correction through role-specific agents. However, the design and deployment of MAS inc

Davood Momeni, Ratbay Myrzakulov

The study of modified gravity models has garnered significant attention because of their potential to provide alternative explanations for cosmological phenomena, such as the accelerated expansion of the universe and the nature of dark energy. One such model, the Einstein-Gauss-Bonnet-Myrzakulov $R + F(T, G)$ gravity (EGBMG), which incorporates the curvature

Pritam Anand, Aadesh Minz, Asish Joel

Uncertainty Quantification (UQ) in wind speed forecasting is a critical challenge in wind power production due to the inherently volatile nature of wind. By quantifying the associated risks and returns, UQ supports more effective decision-making for grid operations and participation in the electricity market. In this paper, we design a sequence of deep learn

Jianghao Wu, Feilong Tang, Yulong Li, Ming Hu

Recent advances such as Chain-of-Thought prompting have significantly improved large language models (LLMs) in zero-shot medical reasoning. However, prompting-based methods often remain shallow and unstable, while fine-tuned medical LLMs suffer from poor generalization under distribution shifts and limited adaptability to unseen clinical scenarios. To addres

Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, Di Niu

Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial computational costs, primarily due to extensive reliance on external Process Reward Models (PRMs) or sampling methods like Best-of-N (BoN). This paper introduces Guided by Gut (GG), an efficient self-guided TTS framework that achieves PRM-level performa

Nitin Jha, Abhishek Parakh, Mahadevan Subramaniam

In the past decade, several small-scale quantum key distribution networks have been established. However, the deployment of large-scale quantum networks depends on the development of quantum repeaters, quantum channels, quantum memories, and quantum network protocols. To improve the security of existing networks and adopt currently feasible quantum technolog

Tsai Hor Chan, Dora Yan Zhang, Guosheng Yin, Lequan Yu

Bayesian neural networks (BNNs) treat neural network weights as random variables, which aim to provide posterior uncertainty estimates and avoid overfitting by performing inference on the posterior weights. However, the selection of appropriate prior distributions remains a challenging task, and BNNs may suffer from catastrophic inflated variance or poor pre

Alireza Rezazadeh, Zichao Li, Ange Lou, Yuying Zhao

Complex tasks are increasingly delegated to ensembles of specialized LLM-based agents that reason, communicate, and coordinate actions-both among themselves and through interactions with external tools, APIs, and databases. While persistent memory has been shown to enhance single-agent performance, most approaches assume a monolithic, single-user context-ove

Rabab Alkhalifa, Fatima Alkhomayes, Boushra Almazroua, Dana Alhaidan

The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem with wide-ranging applications in logistics, routing, and intelligent systems. Due to its factorial complexity, solving large-scale instances requires scalable and efficient algorithmic frameworks, often enabled by parallel computing. This literature review provid

Md Arman Islam, Devi Varaprasad Jonnala, Ritika Rekhi, Pratik Pokharel

As the quality of code generated by Large Language Models (LLMs) improves, their adoption in the software industry for automated code generation continues to grow. Researchers primarily focus on enhancing the functional correctness of the generated code while commonly overlooking its energy efficiency and environmental impact. This paper investigates the ene

Joshua S. Rule, Steven T. Piantadosi

Though humans seem to be remarkable learners, arguments in cognitive science and philosophy of mind have long maintained that learning something fundamentally new is impossible. Specifically, Jerry Fodor's arguments for radical concept nativism hold that most, if not all, concepts are innate and that what many call concept learning never actually leads to th

Lorenzo Baldassari, Josselin Garnier, Knut Solna, Maarten V. de Hoop

Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces - where such problems are naturally formulated - is crucial to ensure stability and convergence as the discretization of the underlying problem is refined. In this paper, we contribute to this line of work by analyzing a widely used sa

M. N. Mazziotta

The solar steady emission in gamma rays is due to the interactions of Galactic cosmic rays with the solar atmosphere and with the low-energy solar photon field via inverse Compton scattering. The emission is sensitive to the magnetic field nearby the Sun and to the cosmic-ray transport in the magnetic field in the inner solar system. Modeling the inverse Com

Daniel Pepper

This work concerns notions of multi-algebra independence introduced by Liu and how they can be studied in the context of bi-free probability. In particular, we show how the free-free-Boolean independence for triples of algebras can be embedded intro and therefore studied from a lens of bi-free probability. It is also shown how its cumulants can be constructe

Amro Asali, Yehuda Ben-Shimol, Itshak Lapidot

The objective of automatic speaker verification (ASV) systems is to determine whether a given test speech utterance corresponds to a claimed enrolled speaker. These systems have a wide range of applications, and ensuring their reliability is crucial. In this paper, we propose a spoofing-robust automatic speaker verification (SASV) system employing a score-aw

Siddhartha Sarkar, Biswarup Ash, Yueyang Wu, Nicholas Boechler

Active solids combine internal active driving with elasticity to realize states with nonequilibrium mechanics and autonomous motion. They are often studied in overdamped settings, e.g., in soft materials, and the role of inertia is less explored. We construct a model of a chemically active solid that incorporates mechanochemical feedback and show that, when

Viacheslav M Sadykov, Leon Ofman, Scott A Boardsen, Yogesh

Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification

Ivan Bao, José C. Díaz Peón González Pacheco, Atharva Navsalkar, Andrew Scheffer

Omnidirectional aerial vehicles (OMAVs) have opened up a wide range of possibilities for inspection, navigation, and manipulation applications using drones. In this paper, we introduce MorphEUS, a morphable co-axial quadrotor that can control position and orientation independently with high efficiency. It uses a paired servo motor mechanism for each rotor ar

Quan Zhou, Mark Kozdoba, Shie Mannor

We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal set of actions is large, actions are highly correlated: many yield similar rewards across environments, making it wasteful to maintain the full set. Our aim is to understand whether it is possible -- and how -- to

Emerson Chiquillo

Through an effective quantum field theory including zero temperature Gaussian fluctuations we derive analytical and explicit expressions for the equation of state of three-dimensional ultracold Rabi-coupled two-component bosonic gases with nonuniversal corrections to the interactions. At mean-field level the system presents two ground-states, one symmetric a

Md Riajul Haque, Debaprasad maity

We explore a class of minimal plateau inflationary models constrained by the latest cosmological observations from ACT DR6, Planck 2018, BICEP/Keck 2018, and DESI, collectively referred to as P-ACT-LB-BK18. These models, characterized by a non-polynomial potential, are analyzed using both inflationary and post-inflationary reheating dynamics, and the limits

Gavin McCracken, Gabriela Moisescu-Pareja, Vincent Letourneau, Doina Precup

We propose a testable universality hypothesis, asserting that seemingly disparate neural network solutions observed in the simple task of modular addition are unified under a common abstract algorithm. While prior work interpreted variations in neuron-level representations as evidence for distinct algorithms, we demonstrate - through multi-level analyses spa

Rohith Sajith, Zijian Song, Brenden Roberts, Varun Menon

We propose protocols to implement non-Clifford logical gates between stabilizer codes by entangling into a non-Abelian topological order as an intermediate step. Generalizing previous approaches, we provide a framework that generates a large class of non-Clifford and non-diagonal logical gates between qudit surface codes by gauging the topological symmetry o

Shahriar Noroozizadeh, Sayantan Kumar, George H. Chen, Jeremy C. Weiss

Clinical narratives encode temporal dynamics essential for modeling patient trajectories, yet large-scale temporally annotated resources are scarce. We introduce PMOA-TTS, a corpus of 124,699 single-patient PubMed Open Access case reports converted into structured textual timelines of (event, time) pairs using a scalable large-language-model pipeline (Llama

Carlos G. Boiza, Maria Petronikolou, Mariam Bouhmadi-López, Emmanuel N. Saridakis

We investigate the viability of $f(Q)$ gravity as an alternative framework to address the $H_0$ and $S_8$ tensions in cosmology. Focusing on three representative $f(Q)$ models, we perform a comprehensive Bayesian analysis using a combination of cosmological observations, including cosmic chronometers, Type Ia supernovae, gamma-ray bursts, baryon acoustic osc

Lorraine C. Marcelin, Jaclyn B. Champagne, Feige Wang, Xiaohui Fan

Mergers play a critical role in galaxy evolution, but their relationship to their surrounding environments is unexplored at high redshift. We investigate the galaxy merger rate among 124 [OIII] emitters at $5.3<z<6.9$ as a function of local galaxy density. Identified in the ASPIRE JWST/NIRCam grism survey, we investigate three density regimes: a $z=6.6$ quas

Jacopo Lestingi, Giovanni D'Addario, Thomas P. Sotiriou

We revisit the modelling of black hole ringdown beyond General Relativity (GR), emphasizing the limitations of approaches that rely solely on shifted quasinormal mode (QNM) frequencies. Starting from modified Teukolsky equations in such scenarios, we classify the distinct types of deviations that can arise -- those shifting QNM frequencies, and those introdu

Filippo Ferrari, Vincenzo Savona, Fabrizio Minganti

The eigenstate thermalization hypothesis (ETH) provides a cornerstone for understanding thermalization in isolated quantum systems, linking quantum chaos with statistical mechanics. In this work, we extend the ETH framework to open quantum systems governed by Lindblad dynamics. We introduce the concept of Liouvillian stripe (spectral subset of the non-Hermit

Admir Greljo, Xavier Ponce Díaz, Anders Eller Thomsen

We investigate the phenomenology of a model in which the proton is rendered absolutely stable by an IR mechanism that remains robust against unknown quantum gravity effects. A linear combination of baryon number and lepton flavors is gauged and spontaneously broken to a residual $\mathbb{Z}_9$ discrete gauge symmetry enforcing a strict selection rule: $\Delt

Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Anik Halder

The impact of feedback from galaxy formation on cosmological probes is typically quantified in terms of the suppression of the matter power spectrum in hydrodynamical compared to gravity-only simulations. In this paper, we instead study how baryonic feedback impacts halo assembly histories and thereby imprints on cosmological observables. We investigate the

Rom Yaakovyan, Sivan Ginzburg, Jim Fuller, Nicholas Z. Rui

When the effective temperature of a cooling white dwarf $T_{\rm eff}$ drops below the ionization limit, it develops a surface convection zone that may generate a magnetic field $B$ through one of several dynamo mechanisms. We revisit this possibility systematically using detailed stellar evolution computations, as well as a simple analytical model that track

Peleg Emanuel, Eyal Cornfeld, Ravid Alon, Shmuel Ur

Control of quantum operations is a crucial yet expensive construct for quantum computation. Efficient implementations of controlled operations often avoid applying control to certain subcircuits, which can significantly reduce the number of gates and overall circuit depth. However, these methods are specialized and circuits frequently need to be implemented

T. Ryu, R. Sari, S. E. de Mink, O. David

Mass transfer is crucial in binary evolution, yet its theoretical treatment has long relied on analytic models whose key assumptions remain debated. We present a direct and systematic evaluation of these assumptions using high-resolution 3D hydrodynamical simulations including the Coriolis force. We simulate streams overflowing from both the inner and outer

T. C. Mooney, Dong Yuan, Adam Ehrenberg, Christopher L. Baldwin

While the impact of locality restrictions on quantum dynamics and algorithmic complexity has been well studied in the general case of time-dependent Hamiltonians, the capabilities of time-independent protocols are less well understood. Using clock constructions, we show that the light cone for time-independent Hamiltonians, as captured by Lieb-Robinson bound

Alexander Hickey, Jeffrey G. Rau, Subhankar Khatua, Michel J. P. Gingras

When magnetic moments are subject to competing or frustrated interactions, continuous degeneracies that are not protected by any symmetry of the parent Hamiltonian can emerge at the classical (mean-field) level. Such "accidental" degeneracies are often lifted by both thermal and quantum fluctuations via a mechanism known as order-by-disorder (ObD). The leadi

Meredith J. Durbin, Yumi Choi, Alessandro Savino, Daniel Weisz

We present uniformly measured resolved stellar photometry and star formation histories (SFHs) for 36 nearby ($\lesssim$ 400 kpc) ultra-faint dwarf galaxies (UFDs; $-7.1 \le M_V \le +0.0$) from new and archival HST imaging. We measure homogeneous distances to all systems via isochrone fitting and find good agreement ($\le$ 2%) for the 18 UFDs that have litera

Yi-Ming Zhong, Stuart L. Shapiro

We present a new two-fluid conduction scheme to simulate the evolution of an isolated, self-gravitating, equilibrium cluster of stars and collisionless dark matter on secular (gravothermal) timescales. We integrate the equations in Lagrangian coordinates via a second-order, semi-implicit algorithm, which is unconditionally stable when the mass of the lighter

Tony An, Félix Desrochers, Yong Baek Kim

Numerous experiments on pyrochlore oxides Pr$_2$(Zr, Sn, Hf, Ir)$_2$O$_7$ with non-Kramers Pr$^{3+}$ ions suggest that they support a quantum spin liquid (QSL) ground state, but the precise nature of the QSL remains unclear. Quantum spin ice with dominant dipolar Ising and smaller quadrupolar transverse exchange interactions is one such candidate, but a domi

Dong Yuan, Chao Yin, T. C. Mooney, Christopher L. Baldwin

The speed of information propagation in long-range interacting quantum systems is limited by Lieb-Robinson-type bounds, whose tightness can be established by finding specific quantum state-transfer protocols. Previous works have given quantum state-transfer protocols that saturate the corresponding Lieb-Robinson bounds using time-dependent Hamiltonians. Are

Jeremiah Lübke, Patrick Reichherzer, Sophie Aerdker, Frederic Effenberger

Cosmic-ray transport in turbulent astrophysical environments remains a multifaceted problem and, despite decades of study, the impact of complex magnetic field geometry -- evident in simulations and observations -- has only recently received more focussed attention. To understand how ensemble-averaged transport behaviour emerges from the intricate interactio

Ya Wu, Qiang Sheng, Danding Wang, Guang Yang

Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely on single-step evaluations, failing to capture how models adapt to evolving ethical challenges. Addressing this gap, we i

Savya Khosla, Sethuraman TV, Barnett Lee, Alexander Schwing

We introduce the Region Encoder Network (REN), a fast and effective model for generating region-based image representations using point prompts. Recent methods combine class-agnostic segmenters (e.g., SAM) with patch-based image encoders (e.g., DINO) to produce compact and effective region representations, but they suffer from high computational cost due to

Wafa Alghallabi, Ritesh Thawkar, Sara Ghaboura, Ketan More

Arabic poetry is one of the richest and most culturally rooted forms of expression in the Arabic language, known for its layered meanings, stylistic diversity, and deep historical continuity. Although large language models (LLMs) have demonstrated strong performance across languages and tasks, their ability to understand Arabic poetry remains largely unexplo