February 2025 arXiv papers — page 29

Devansh Saxena, Zoe Kahn, Erina Seh-Young Moon, Lauren M. Chambers

Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and the

Angelique Taylor, Tauhid Tanjim, Michael Joseph Sack, Maia Hirsch

Designing robots to support high-stakes teamwork in emergency settings presents unique challenges, including seamless integration into fast-paced environments, facilitating effective communication among team members, and adapting to rapidly changing situations. While teleoperated robots have been successfully used in high-stakes domains such as firefighting

Jing Xu, Mark Hansen, Megan Ryerson

Disruptions in the National Airspace System (NAS) lead to significant losses to air traffic system participants and raise public concerns. We apply two methods, cluster analysis and anomaly detection models, to identify operational disruptions with geographical patterns in the NAS since 2010. We identify four types and twelve categories of days of operations

Joonas Ilmavirta, Antti Kykkänen, Teemu Saksala

We introduce and study a new family of tensor tomography problems. At rank 2 it corresponds to linearization of travel time of elastic waves, measured for all polarizations. We provide a kernel characterization for ranks up to 2. The kernels consist of potential tensors, but in an unusual sense: the associated differential operators have degree 2 instead of

Shramay Palta, Nirupama Chandrasekaran, Rachel Rudinger, Scott Counts

Using a sample of 25,000 Bing Copilot conversations, we study how the agent responds to users of varying levels of domain expertise and the resulting impact on user experience along multiple dimensions. Our findings show that across a variety of topical domains, the agent largely responds at proficient or expert levels of expertise (77% of conversations) whi

Alexander Scheinker, Alan Williams

Beam loss (BLM) and beam current monitors (BCM) are ubiquitous at particle accelerator around the world. These simple devices provide non-invasive high level beam measurements, but give no insight into the detailed 6D (x,y,z,px,py,pz) beam phase space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate p

Ali Ladak, Matti Wilks, Steve Loughnan, Jacy Reese Anthis

AI systems have rapidly advanced, diversified, and proliferated, but our knowledge of people's perceptions of mind and morality in them is limited, despite its importance for outcomes such as whether people trust AIs and how they assign responsibility for AI-caused harms. In a preregistered online study, 975 participants rated 26 AI and non-AI entities. Over

Devansh Saxena, Ji-Youn Jung, Jodi Forlizzi, Kenneth Holstein

AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address

Yuexi Chen, Yimin Xiao, Kazi Tasnim Zinat, Naomi Yamashita

Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary c

Onur Cankur, Brian Austin, Dhruva Kulkarni, Abhinav Bhatele

GPGPU-accelerated clusters and supercomputers are central to modern high-performance computing (HPC). Over the past decade, these systems continue to expand, and GPUs now expose a wide range of hardware counters that provide detailed views of performance and resource usage. Despite the potential of these counters, few studies have evaluated the insights they

Siqi Guo, Ilgee Hong, Vicente Balmaseda, Changlong Yu

Supervised fine-tuning (SFT) has become a crucial step for aligning pretrained large language models (LLMs) using supervised datasets of input-output pairs. However, despite being supervised, SFT is inherently limited by its generative training objective. To address its limitations, the existing common strategy is to follow SFT with a separate phase of prefe

Esteban Cárdenas, Joseph K. Miller, David Mitrouskas, Nataša Pavlović

This work is inspired by recent experimental observations in ultracold atomic Bose-Fermi mixtures [DeSalvo et al., Nature 568 (2019)]. These experiments reveal the emergence of an attractive fermion-mediated interaction between bosons, as well as a stability-instability transition. We give the first mathematical demonstration of this transition by studying t

Alexei Rybkin

We investigate the long-time asymptotic behavior of solutions to the Cauchy problem for the KdV equation, focusing on the evolution of the radiant wave associated with a Wigner-von Neumann (WvN) resonance induced by the initial data (potential). A WvN resonance refers to an energy level where the potential exhibits zero transmission (complete reflection). Th

Xingyu Bruce Liu, Haijun Xia, Xiang Anthony Chen

We envision the concept of Thoughtful AI, a new human-AI interaction paradigm in which the AI behaves as a continuously thinking entity. Unlike conventional AI systems that operate on a turn-based, input-output model, Thoughtful AI autonomously generates, develops, and communicates its evolving thought process throughout an interaction. In this position pape

S. A. Torres

In this work, a precise quantum formulation of Einstein's Equivalence Principle (EEP) is developed within the framework of nonrelativistic quantum mechanics. By employing detailed analyses in both the Schr\"odinger and Heisenberg pictures, it is demonstrated that an observer in free fall in a uniform gravitational field is equivalent to an inertial observer

S. Mercimek, C. Codella, L. Podio, P. Caselli

The study of deuterium fractionation is a valuable tool for reconstructing our chemical history from the early prestellar stages to the formation of planets. In the context of the ALMA Large Programme FAUST, we observed formaldehyde, H$_2$CO, and its singly and doubly deuterated forms, HDCO and D$_2$CO, towards the protostellar cluster VLA1623-2417, on scale

Jenna M. Landy, Nishanth Basava, Giovanni Parmigiani

Bayesian Poisson Non-Negative Matrix Factorization (NMF) is widely used to model count data, including in cancer mutational signature analysis. However, standard Gibbs samplers rely on computationally expensive Poisson augmentation, and current software implementations learn the latent rank either through slow and potentially subjective heuristic rank select

Ian Steenstra, Farnaz Nouraei, Timothy W. Bickmore

Learning therapeutic counseling involves significant role-play experience with mock patients, with current manual training methods providing only intermittent granular feedback. We seek to accelerate and optimize counselor training by providing frequent, detailed feedback to trainees as they interact with a simulated patient. Our first application domain inv

James Clarke, Francis Cavanna, Aniket Marne, Anthony Davolio

Biological systems tightly regulate their physiological state using control signals. This includes the actomyosin cytoskeleton, a contractile active gel that consumes chemical free energy to drive many examples of cellular mechanical behavior. Upstream regulatory pathways activate or inhibit actomyosin activity. However, the contractile response of the actom

Kashif Sattar, Muhammad Arslan, Saqib Majeed, Salim Iqbal

Wireless Sensor Networks have risen as a highly promising technology suitable for precision agriculture implementations, enabling efficient monitoring and control of agricultural processes. In precision agriculture, accurate and synchronized data collection is crucial for effective analysis and decision making. Using principles of information theory, we can

J. S. Araujo, K. Khan, A. S. Coelho

Quantum optics plays a crucial role in developing quantum computers on different platforms. In photonics, precise control over light's degrees of freedom, including discrete variables (polarization, photon number, orbital angular momentum) and continuous variables (phase, amplitude quadratures, frequency), is fundamental. Our model manipulates photonic syste

Raul Quiroga-Barranco

The quaternionic unit ball carries a Riemannian metric built using regular M\"obius transformations: the slice Riemannian metric. We prove that the geometry induced by this metric is strongly related to the group $\mathrm{Sp}(1,1)$. We also develop the foundations for a Lie theoretic study of the slice Riemannian metric. In particular, we compute its isometr

Niklas Höpner, Ilaria Tiddi, Herke van Hoof

The scalability of instructable agents in robotics or gaming is often hindered by limited data that pairs instructions with agent trajectories. However, large datasets of unannotated trajectories containing sequences of various agent behaviour (play trajectories) are often available. In a semi-supervised setup, we explore methods to extract labelled segments

B. Lowe, T. Nordlander, L. Casagrande, G. S. Da Costa

We present a chemo-dynamical study conducted with 2dF$+$AAOmega of $\sim 6000$ Gaia DR3 non-variable candidate metal-poor stars that lie in the direction of the Galactic plane. Our spectral analysis reveals 15 new extremely metal-poor (EMP) stars, with the lowest metallicity at $\rm{[Fe/H]} = -4.0 \pm 0.2$ dex. Two of the EMP stars are also carbon enhanced,

Meisam Ghasemi Bostanabad, Mojtaba Mohammadi Najafabadi

We explore the top quark flavor-changing 4-Fermi interactions ($tuee$ and $tcee$) with scalar, vector, and tensor structures using machine learning models to analyze tri-lepton processes at the LHC. The study is performed using $t\bar{t}$ and $tW$ processes, where a top quark decays into $u/c+e^{+}+e^{-}$. The analysis incorporates both reducible and irreduc

Theodor Lundqvist, Ludvig Delvret

Existing methods for self-supervised representation learning of geospatial regions and map entities rely extensively on the design of pretext tasks, often involving augmentations or heuristic sampling of positive and negative pairs based on spatial proximity. This reliance introduces biases and limits the representations' expressiveness and generalisability.

Ke Zhu, Hanzhong Liu

We applaud Fiksel (2024) for their valuable contributions to randomization-based inference, particularly their work on inverting the Fisher randomization test (FRT) to construct confidence intervals using the covariate-adjusted test statistic. FRT is advocated by many scholars because it produces finite-sample exact p-values for any test statistic and can be

Serge Galam

I study the conditions under which a democratic dynamics of a public debate drives a Minority-to-Majority transition. A landscape of the opinion dynamics is thus built using the Galam Majority Model (GMM) in a 3-dimensional parameter space for three different sizes r=2, 3, 4 of local discussing groups. The related parameters are (p_0, k, x), the respective p

Marius Smytzek, Martin Eberlein, Lars Grunske, Andreas Zeller

Fault localization is a fundamental aspect of debugging, aiming to identify code regions likely responsible for failures. Traditional techniques primarily correlate statement execution with failures, yet program behavior is influenced by diverse execution features-such as variable values, branch conditions, and definition-use pairs-that can provide richer di

Artem Timoshenko, Chengfeng Mao, John R. Hauser

Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate "jobs to be done." This task is cognitively demanding, time-consuming, and difficult to scale. While current

So Won Jeong, Veronika Ročková

For a long time, the authorship of the Federalist Papers had been a subject of inquiry and debate, not only by linguists and historians but also by statisticians. In what was arguably the first Bayesian case study, Mosteller and Wallace (1963) provided the first statistical evidence for attributing all disputed papers to Madison. Our paper revisits this hist

Carlos Dantas, Tiago Massoni, Camila Sarmento, Rayana Rocha

Retrospective meetings play a vital role in agile development by facilitating team reflection on past work to enhance effectiveness. These meetings address various social aspects, including team dynamics, individual performance, processes, and technologies, ultimately leading to actions for improvement. Despite their importance, limited research has explored

Mads Høgenhaug, Marcus Friis, Morten Pedersen, Luca Rossi

We present TikTok StitchGraph: a collection of 36 graphs based on TikTok stitches. With its rapid growth and widespread popularity, TikTok presents a compelling platform for study, yet given its video-first nature the network structure of the conversations that it hosts remains largely unexplored. Leveraging its recently released APIs, in combination with we

Alexandre Kirilov, Wagner Augusto Almeida de Moraes, Pedro Meyer Tokoro

We study the global hypoellipticity and solvability of strongly invariant operators and systems of strongly invariant operators on closed manifolds. Our approach is based on the Fourier analysis induced by an elliptic pseudo-differential operator, which provides a spectral decomposition of $L^2(M)$ into finite-dimensional eigenspaces. This framework allows u

Felipe Guerra, Tuomo Valkonen

Based on a nonsmooth coherence condition, we construct and prove the convergence of a forward-backward splitting method that alternates between steps on a fine and a coarse grid. Our focus is a total variation regularised inverse imaging problems, specifically, their dual problems, for which we develop in detail the relevant coarse-grid problems. We demonstr

Kevin Pu, Daniel Lazaro, Ian Arawjo, Haijun Xia

AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor activities and task context. We explored thre

Craig D. Johnston, Lars K. S. Daldorff, Peter W. Schuck, Mark G. Linton

Recent observations of the solar atmosphere in cool extreme ultraviolet (EUV) lines have reported the prevalence of coronal rain falling from coronal cloud filaments that are associated with the magnetic dips of coronal X-point structures. These filaments mysteriously appear as clouds of mass in the corona that subsequently shrink and disappear due to mass l

Donghoon Ha, Jeong San Kim

We consider bipartite quantum state discrimination and present a quantum data-hiding scheme utilizing an orthogonal separable state ensemble. Using a bound on local minimum-error discrimination, we provide a sufficient condition for the separable state ensemble to be used in constructing a quantum data-hiding scheme. Our results are illustrated with various

Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi, Fatemeh Nourzad

In Reinforcement Learning (RL), tasks with instantaneous hard constraints present significant challenges, particularly when the decision space is non-convex or non-star-convex. This issue is especially relevant in domains like autonomous vehicles and robotics, where constraints such as collision avoidance often take a non-convex form. In this paper, we estab

Abigail E. Ambrose, Eli Visbal, Mihir Kulkarni, Matthew McQuinn

We present new simulations of Lyman-$\alpha$ (Ly$\alpha$) intensity maps that include Ly$\alpha$ radiative transfer in the intergalactic medium (IGM) and all significant sources of Ly$\alpha$ photons. The sources considered include Ly$\alpha$ directly from galaxies, cooling at the edges of ionized bubbles, recombinations within these bubbles, and reprocessin

Hediyeh Baban, Sai A Pidapar, Aashutosh Nema, Sichen Lu

We introduce a novel multi-agent collaboration framework designed to enhance the accuracy and robustness of text classification models. Leveraging BERT as the primary classifier, our framework dynamically escalates low-confidence predictions to a specialized multi-agent system comprising Lexical, Contextual, Logic, Consensus, and Explainability agents. This

Fengze Yang, Xiaoyue Cathy Liu, Lingjiu Lu, Bingzhang Wang

With the urbanization process, an increasing number of sensors are being deployed in transportation systems, leading to an explosion of big data. To harness the power of this vast transportation data, various machine learning (ML) and artificial intelligence (AI) methods have been introduced to address numerous transportation challenges. However, these metho

Soumyadeep Bhattacharjee, Nicole Reindl, Howard E. Bond, Klaus Werner

In this second paper on our variability survey of central stars of planetary nebulae (CSPNe) using ZTF, we report 11 long-timescale variables with variability timescales ranging from months to years. We also present preliminary analyses based on spectroscopic and/or photometric follow-up observations for six of them. Among them is NGC 6833, which shows a $98

Joachim De Baer, A. Seza Doğruöz, Thomas Demeester, Chris Develder

Optimizing language models for use in conversational agents requires large quantities of example dialogues. Increasingly, these dialogues are synthetically generated by using powerful large language models (LLMs), especially in domains where obtaining authentic human data is challenging. One such domain is human resources (HR). In this context, we compare tw

Zhi-Hao Cui, Andrew J. Millis, David R. Reichman

CrSBr is a layered van der Waals insulator with a quasi one-dimensional electronic structure and in-plane ferromagnetic order. Recent experimental work on Li-doped CrSBr reveals quasi-1D charge modulated states. In this study, we develop ab initio effective models for CrSBr to investigate these states and solve them using mean-field theory and density matrix

Sarthak Das

BoxRL-NNV is a Python tool for the detection of safety violations in neural networks by computing the bounds of the output variables, given the bounds of the input variables of the network. This is done using global extrema estimation via Latin Hypercube Sampling, and further refinement using L-BFGS-B for local optimization around the initial guess. This pap

Jia-Le Ding, Hai Tao Li, Jian Wang

We compute the two-loop soft function for the associated production of a top quark and a $W$ boson near the threshold, where the invariant mass of the $tW$ system approaches the collider energy. We employ the reverse unitarity technique and integration-by-parts identities to reduce soft loop integrals to a minimal basis of master integrals. These master inte

Jiwei Zou, Lin Wang, Senwen Yang, Michael Lacasse

Urban overheating, exacerbated by climate change, threatens public health and urban sustainability. Traditional approaches, such as numerical simulations and field measurements, face challenges due to uncertainties in input data. This study integrates field measurements with machine learning models to predict the duration and severity of future urban overhea

Alessandro Ascani Orsini, Manan Bhatt, Pierce L. Perkins, Siyu Wang

Histology has long been a foundational technique for studying anatomical structures through tissue slicing. Advances in computational methods now enable three dimensional (3D) reconstruction of organs from histology images, enhancing the analysis of structural and functional features. Here, we present a novel multimodal computational method to reconstruct ro

Sumanta Bhattacharyya, Pedram Rooshenas

Large language models (LLMs) encode a diverse range of linguistic features within their latent representations, which can be harnessed to steer their output toward specific target characteristics. In this paper, we modify the internal structure of LLMs by training sparse autoencoders to learn a sparse representation of the query embedding, allowing precise c

Jaime Redondo-Yuste, Alejandro Cárdenas-Avendaño

Some black hole mimickers, as well as black strings and other higher-dimensional spacetimes, exhibit stable light rings-regions where light or high-frequency gravitational waves can be trapped. In these regions, linear perturbations decay slowly, raising the possibility of nonlinear instability mechanisms. In this work, we study the cubic nonlinear wave equa

Lindy Comstock, Priyanshu Sharma, Mikhail Belov

This paper illustrates how the overall sentiment of a text may be shifted in translation and the implications for automated sentiment analyses, particularly those that utilize machine translation and assess findings via semantic similarity metrics. While human and machine translation will produce more lemmas that fit the expected frequency of sentiment in th

Zhuoran Lu, Qian Zhou, Yi Wang

Generative AI significantly enhances player agency in interactive narratives (IN) by enabling just-in-time content generation that adapts to player actions. While delegating generation to AI makes IN more interactive, it becomes challenging for authors to control the space of possible narratives - within which the final story experienced by the player emerge

Jingying Wang, Jingjing Zhang, Juana Nicoll Capizzano, Matthew Sigakis

eXplainMR is a Mixed Reality tutoring system designed for basic cardiac surface ultrasound training. Trainees wear a head-mounted display (HMD) and hold a controller, mimicking a real ultrasound probe, while treating a desk surface as the patient's body for low-cost and anywhere training. eXplainMR engages trainees with troubleshooting questions and provides

Markus Bertl, Alan Mott, Salvatore Sinno, Bhavika Bhalgamiya

The digitization of healthcare presents numerous challenges, including the complexity of biological systems, vast data generation, and the need for personalized treatment plans. Traditional computational methods often fall short, leading to delayed and sometimes ineffective diagnoses and treatments. Quantum Computing (QC) and Quantum Machine Learning (QML) o

Emil Albrychiewicz, Andrés Franco Valiente, Ori Ganor

The Witten index of the $(2,0)$-theory compactified on spaces of the form $S^3/\Gamma\times S^2$, with a freely acting group $\Gamma$, and with external string sources implemented via timelike surface operator insertions, is expressed in terms of Ray-Singer torsion of $S^3/\Gamma$ and characters of irreducible representations of $\Gamma$. We compute it expli

Chenhao Chu, Yuhao Mao, Hua Wang

In this study, we introduce an innovative methodology for the design of mm-Wave passive networks that leverages knowledge transfer from a pre-trained synthesis neural network (NN) model in one technology node and achieves swift and reliable design adaptation across different integrated circuit (IC) technologies, operating frequencies, and metal options. We p

Matthew Barker, Andrew Bell, Evan Thomas, James Carr

While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters and hyperparameters that must be made or tuned. This includes the LLM, embedding, and ranker models themselves, as well as hyperparameters governing individual RAG components. Yet,

Marie-Christine Düker, Adam Waterbury

Many scientific problems involve data exhibiting both temporal and cross-sectional dependencies. While linear dependencies have been extensively studied, the theoretical analysis of regression estimators under nonlinear dependencies remains scarce. This work studies a kernel-based estimation procedure for nonlinear dynamics within the reproducing kernel Hilb

Li Wang, Lei-Hong Zhang, Ren-Cang Li

A novel feature selection model via orthogonal canonical correlation analysis with the $(2,1)$-norm regularization is proposed, and the model is solved by a practical NEPv approach (nonlinear eigenvalue problem with eigenvector dependency), yielding a feature selection method named OCCA-FS. It is proved that OCCA-FS always produces a sequence of approximatio

Jonathan Ruhman

This comment critically examines the claims made in arXiv papers arXiv:2501.17230 and arXiv:2502.00103, which argue that a multiplicity of polar optical phonons can generate a long-range attractive interaction via a generalized Frohlich coupling. I identify a fundamental flaw in their derivation, showing that their result relies on an unphysical assumption--

Milan Brož, Vladimír Sedláček

This paper concisely summarizes the XTS block encryption mode for storage sector-based encryption applications and clarifies its limitations. In particular, we aim to provide a unified basis for constructive discussions about the newly introduced key scope change to the IEEE 1619 standard. We also reflect on wide modes that could replace XTS in the future.

Hazar Çakır, Richard M. Milbradt, Christian B. Mendl

This research introduces an improved framework for constructing matrix product operators (MPOs) and tree tensor network operators (TTNOs), crucial tools in quantum simulations. A given (Hamiltonian) operator typically has a known symbolic "sum of operator strings" form that can be translated into a tensor network structure. Combining the existing bipartite-g

Ilknur Gezer, Gábor Marton, Julia Roquette, Marc Audard

We performed a large-scale spectral energy distribution (SED) fitting analysis for young stellar objects (YSOs) in the Orion star formation complex (OSFC) to derive key physical parameters; temperature, luminosity, mass, and age, using SED models. Our goal is to establish a statistically robust characterization of the stellar population and its evolutionary

Nicholas Chiem

We consider an Arnold's Cat Map generated $C^1$ bounded potential with the directional derivative in the unstable direction bounded away from zero. We show that the Lyapunov exponent for the associated Shr\"odinger Operator is uniformly positive for all energies provided the coupling is sufficiently large.

Zlata Tabachová, Christian Diem, Johannes Stangl, András Borsos

On the way towards carbon neutrality, climate stress testing provides estimates for the physical and transition risks that climate change poses to the economy and the financial system. Missing firm-level CO2 emissions data severely impedes the assessment of transition risks originating from carbon pricing. Based on the individual emissions of all Hungarian f

Ruokai Yin, Yuhang Li, Priyadarshini Panda

Weight-only quantization has been widely explored in large language models (LLMs) to reduce memory storage and data loading overhead. During deployment on single-instruction-multiple-threads (SIMT) architectures, weights are stored in low-precision integer (INT) format, while activations remain in full-precision floating-point (FP) format to preserve inferen

Fabio Matti, Haoze He, Daniel Kressner, Hei Yin Lam

Stochastic trace estimation is a well-established tool for approximating the trace of a large symmetric matrix $\boldsymbol{B}$. Several applications involve a matrix that depends continuously on a parameter $t \in [a,b]$, and require trace estimates of $\boldsymbol{B}(t)$ for many values of $t$. This is, for example, the case when approximating the spectral

Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff

Using data from a live trading experiment on the Binance Bitcoin perpetual, we examine the effects of (i) basic order book mechanics and (ii) the persistence of price changes from immediate to short timescales, revealing the interplay between returns, queue sizes, and orders' queue positions. We document a fundamental trade-off: a negative correlation betwee

Mauro Bisson, Massimo Bernaschi, Massimiliano Fatica, Nikolaos G. Fytas

We present high-performance implementations of the two-dimensional Ising and Blume-Capel models for large-scale, multi-GPU simulations. Our approach takes full advantage of the NVIDIA GB200 NVL72 system, which features up to $72$ GPUs interconnected via high-bandwidth NVLink, enabling direct GPU-to-GPU memory access across multiple nodes. By utilizing Fabric

Ayana Moshruba, Shay Snyder, Hamed Poursiami, Maryam Parsa

As machine learning models increasingly process sensitive data, understanding their vulnerability to privacy attacks is vital. Membership inference attacks (MIAs) exploit model responses to infer whether specific data points were used during training, posing a significant privacy risk. Prior research suggests that spiking neural networks (SNNs), which rely o

Matthias Thamm, Harini Radhakrishnan, Hatem Barghathi, C. M. Herdman

We present a controlled numerical study of the Berezinskii-Kosterlitz-Thouless (BKT) transition in the one-dimensional Bose-Hubbard model at unit filling, providing evidence of the characteristic logarithmic finite-size scaling of the BKT transition. Employing density matrix renormalization group and quantum Monte Carlo simulations under periodic boundary co

David Grund

Recent measurements of J/$\psi$ photoproduction based on data from ultra-peripheral Pb-Pb and p-Pb collisions recorded by the ALICE experiment during Run 2 of the LHC are presented. Photoproduction as a photon-induced process is sensitive to the structure of hadrons and the results are of great importance for a better understanding of how gluon saturation an

Miguel Herencia García del Castillo, Ricardo Moya Garcia, Manuel Jesús Cerezo Mazón, Ekaitz Arriola Garcia

In this article, we present a Latent Diffusion Model (LDM) for the generation of brain Magnetic Resonance Imaging (MRI), conditioning its generation based on pathology (Healthy, Glioblastoma, Sclerosis, Dementia) and acquisition modality (T1w, T1ce, T2w, Flair, PD). To evaluate the quality of the generated images, the Fr\'echet Inception Distance (FID) and M

Raphael Eichhorn, Felix Hermann, Marco Seiler

We study a variant of the voter model on a coevolving network in which interactions of two individuals with differing opinions only lead to an agreement on one of these opinions with a fixed probability $q$. Otherwise, with probability $1-q$, both individuals become offended in the sense that they never interact again, i.e. the corresponding edge is removed

Chenxi Hu, Vijay Gopal Thirupakuzi Vangipuram, Christopher Chae, Ilteris K. Turan

MgGeN2 films were synthesized using metal-organic chemical vapor deposition on GaN/c-sapphire templates and c-plane sapphire substrates. Energy-dispersive X-ray spectroscopy was used to estimate the cation composition ratios. To mitigate magnesium evaporation, the films were grown at pyrometer temperature 745 {\deg}C with a wafer rotation speed of 1000 rpm.

Maria Naumcheva, Sophie Ebersold, Jean-Michel Bruel, Bertrand Meyer

In industrial practice, requirements are an indispensable element of any serious software project. In the academic study of software engineering, requirements are one of the heavily researched subjects. And yet requirements engineering, as practiced in industry, makes shockingly sparse use of the concepts propounded in the requirements literature. The presen

Subrata Jana, Abhishek Bhattacharjee, Suman Mahakal, Szymon Smiga

We propose a range-separated hybrid exchange-correlation functional to calculate solid-state material properties. The functional mixes Hartree-Fock exchange with the semilocal exchange of the meta-generalized gradient approximation (meta-GGA) and the fraction of Hartree-Fock exchange is determined from the dielectric function. First-principles calculations a

Georgios Kamaras, Subramanian Ramamoorthy

We present an integrated (or end-to-end) framework for the Real2Sim2Real problem of manipulating deformable linear objects (DLOs) based on visual perception. Working with a parameterised set of DLOs, we use likelihood-free inference (LFI) to compute the posterior distributions for the physical parameters using which we can approximately simulate the behaviou

Morteza Behrooz, Preetham Kolari, Fred Zaw, Lindsay Kenzig

Besides the utilitarian aspects of online shopping, hedonic motivations play a significant role in shaping the shopping behavior of online users. With the increased popularity of voice-enabled devices, online shopping platforms have attempted to drive online shopping on voice. However, we explain why voice might be more suitable for the hedonic aspects of sh

Weronika Golletz, Krzysztof Sacha

We study discrete time crystal formation in a system driven periodically by an oscillating atomic mirror, consisting of two distinct ultracold atomic clouds in the presence of a gravitational field. The intra-species interactions are weak and attractive, while the inter-species interactions are infinitely strong and repulsive. The clouds are arranged in a on

Ismail Alkhouri, Evan Bell, Avrajit Ghosh, Shijun Liang

In recent years, deep learning methods have been extensively developed for inverse imaging problems (IIPs), encompassing supervised, self-supervised, and generative approaches. Most of these methods require large amounts of labeled or unlabeled training data to learn effective models. However, in many practical applications, such as medical image reconstruct

Xiaohan Zhu, Mesrob I. Ohannessian, Nathan Srebro

We provide finite sample upper and lower bounds on the Binomial tail probability which are a direct application of Sanov's theorem. We then use these to obtain high probability upper and lower bounds on the minimum of i.i.d. Binomial random variables. Both bounds are finite sample, asymptotically tight, and expressed in terms of the KL-divergence.

C. Chambers, M. P. Reiter, A. T. Gallant, M. Yavor

Isotopes in the region of the nuclear chart below $^{68}\mathrm{Ni}$ have been the subject of intense experimental and theoretical effort due to the potential onset of a new ``island of inversion'' when crossing the harmonic oscillator subshell closure at $N = 40$. We have measured the masses of $^{64-68}\textrm{Mn}$ using TITAN's multiple-reflection time-of

Anichur Rahman, Shahariar Hossain Mahir, Md Tanjum An Tashrif, Airin Afroj Aishi

Nowadays, DeepSeek, ChatGPT, and Google Gemini are the most trending and exciting Large Language Model (LLM) technologies for reasoning, multimodal capabilities, and general linguistic performance worldwide. DeepSeek employs a Mixture-of-Experts (MoE) approach, activating only the parameters most relevant to the task at hand, which makes it especially effect

Randy Kuang, Dafu Lou

We propose a groundbreaking random number generator that achieves truly uniform, independent, and identically distributed (IID) randomness by integrating Quantum Permutation Pads (QPP) with system jitter--derived entropy, herein called IID-based QPP-RNG. Unlike conventional RNGs that use raw timing variations, our design uses system jitter solely to generate

Shayleen Reynolds, Hengzhi He, Dung Daniel T. Ngo, Saheed Obitayo

In recent years, LLM watermarking has emerged as an attractive safeguard against AI-generated content, with promising applications in many real-world domains. However, there are growing concerns that the current LLM watermarking schemes are vulnerable to expert adversaries wishing to reverse-engineer the watermarking mechanisms. Prior work in breaking or ste

Heinz Frelijj, Danilo González-Díaz, Douglas Geisler, Sandro Villanova

As part of the bulge Cluster APOgee Survey (CAPOS), high-resolution, high Signal-to-Noise Ratio Near-Infrared spectroscopy, we aim to conduct the most robust chemical study to date for NGC 6316, deriving abundances for a number of elements with a variety of nucleosynthetic origins, most of which have never been studied before in this cluster. We use the Brus

José Antonio Carrillo, Shuchen Guo

We consider the Kac model for the space-homogeneous Landau equation with the Coulomb potential. We show that the Fisher information of the Liouville equation for the unmodified $N$-particle system is monotonically decreasing in time. The monotonicity ensures the compactness to derive a weak solution of the Landau hierarchy.

Jerome Ku, Eric Nguyen, David W. Romero, Garyk Brixi

We introduce convolutional multi-hybrid architectures, with a design grounded on two simple observations. First, operators in hybrid models can be tailored to token manipulation tasks such as in-context recall, multi-token recall, and compression, with input-dependent convolutions and attention offering complementary performance. Second, co-designing convolu

Brian Hu Zhang, Ioannis Anagnostides, Emanuel Tewolde, Ratip Emin Berker

Variational inequalities (VIs) encompass many fundamental problems in diverse areas ranging from engineering to economics and machine learning. However, their considerable expressivity comes at the cost of computational intractability. In this paper, we introduce and analyze a natural relaxation -- which we refer to as expected variational inequalities (EVIs

Grace Guinan, Addison Salvador, Michelle A. Smeaton, Andrew Glaws

What does materials science look like in the "Age of Artificial Intelligence?" Each materials domain-synthesis, characterization, and modeling-has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent a

James H. Zhang, Rohith Mittapally, Abimbola Oluwade, Gang Chen

Evaporation rates from porous evaporators under sunlight have been reported to exceed the solar-thermal limit, determined by relating the incoming solar energy to the latent and sensible heat of water, for applications in desalination and brine pond drying. Although flat two-dimensional (2D) evaporators exceeding the solar limit implies a non-thermal process

Uri Itai, Asael Bar Ilan, Teddy Lazebnik

Detecting out-of-distribution (OOD) data is a critical task for maintaining model reliability and robustness. In this study, we propose a novel anomaly detection algorithm that leverages the convex hull (CH) property of a dataset by exploiting the observation that OOD samples marginally increase the CH's volume compared to in-distribution samples. Thus, we e

Silei Xu, Wenhao Xie, Lingxiao Zhao, Pengcheng He

Large Language Models (LLMs) have demonstrated remarkable performance in solving complex reasoning tasks through mechanisms like Chain-of-Thought (CoT) prompting, which emphasizes verbose, step-by-step reasoning. However, humans typically employ a more efficient strategy: drafting concise intermediate thoughts that capture only essential information. In this

Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian

We present the results of a search for nonlinear gravitational wave memory in the NANOGrav 15-year data set. We find no significant evidence for memory signals in the dataset, with a maximum Bayes factor of 3.1 in favor of a model including memory. We therefore place upper limits on the strain of potential gravitational wave memory events as a function of sk

Ning Qi, Bolun Xu

This paper proposes a novel method to generate bid bounds that can serve as offer caps for energy storage in electricity markets to help reduce system costs and regulate potential market power exercises. We derive the bid bounds based on a tractable multi-period economic dispatch chance-constrained formulation that systematically incorporates the uncertainty

Rikako Yamamoto, Luke Alexander Turnbull, Marcus Schmidt, José Claudio Corsaletti Filho

With many candidate altermagnetic materials, MnTe has emerged as one of the most promising systems, with growing experimental evidence for altermagnetic phenomena. So far, the majority of measurements have been performed on thin-films, or have involved surface measurements. However, the question of altermagnetic order in the bulk system - in the absence of s

Vardan Gyurjyan, Graham Heyes, Christopher Larrieu, David Lawrence

The JIRIAF (JLab Integrated Research Infrastructure Across Facilities) framework is designed to streamline resource management and optimize high-performance computing (HPC) workloads across heterogeneous environments. Central to JIRIAF is the JIRIAF Resource Manager (JRM), which effectively leverages Kubernetes and Virtual Kubelet to manage resources dynamic

Ziruo Zhang, Cheng Peng

Motivated by SYK-like models describing near-BPS black holes in string/M-theory, we consider gauging the U$(1)$ symmetry of the complex SYK model in the presence of a Wilson line with charge $k$. At a fixed background gauge field, solutions to the Schwinger-Dyson equations display vastly different properties from those at a fixed real chemical potential. In