February 2025 arXiv papers — page 38

Xiongbin Gui, Hanlin Lv, Xiao Wang, Longting Lv

Background: Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpreting semantically complex criteria. Traditional manual screening methods are time-consuming and prone to errors. While AI-powered pre-screening offers potential solutions, challenges remain regarding accuracy, eff

Jasper Roe, Mike Perkins, Klaire Somoray, Dan Miller

Advances in deepfake technologies, which use generative artificial intelligence (GenAI) to mimic a person's likeness or voice, have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders perceive and intend to use these tools. This study investigated higher education stakeholder perceptions and i

Jonas K. König, Jamie M. Fitzgerald, Ermin Malic

Layered 2D organic-inorganic perovskite semiconductors support strongly confined excitons that offer significant potential for ultrathin polaritonic devices due to their tunability and huge oscillator strength. The application of a magnetic field has proven to be an invaluable tool for investigating the exciton fine structure observed in these materials. Yet

Istiaq Ahmed Fahad, Abdullah Ibne Hanif Arean, Nazmus Sakib Ahmed, Mahmudul Hasan

Automatic Vehicle Detection (AVD) in diverse driving environments presents unique challenges due to varying lighting conditions, road types, and vehicle types. Traditional methods, such as YOLO and Faster R-CNN, often struggle to cope with these complexities. As computer vision evolves, combining Convolutional Neural Networks (CNNs) with Transformer-based ap

Leon Furze, Mike Perkins, Jasper Roe, Jason MacVaugh

The rapid adoption of Generative Artificial Intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices, and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. This paper presents the findings of a p

Yasaman Torabi, Shahram Shirani, James P. Reilly

Auscultation provides a rich diversity of information to diagnose cardiovascular and respiratory diseases. However, sound auscultation is challenging due to noise. In this study, a modified version of the affine non-negative matrix factorization (NMF) approach is proposed to blindly separate lung and heart sounds recorded by a digital stethoscope. This metho

Nuannuan Xiang, Yuki Shiraito

Evolutionary societal changes often prompt a debate. The positions of the two major political parties in the United States on civil rights issues underwent a reversal in the 20th century. The conventional view holds that this shift was a structural break in the 1960s, driven by party elites, while recent studies argue that the change was a more gradual proce

Tobias Rohner, Franziska Weber

We consider vanishing viscosity approximations to solutions of the stochastic incompressible Euler equations in two space dimensions with additive noise. We identify sufficient and necessary conditions under which martingale solutions of the stochastic Euler equations satisfy an exact energy balance in mean. We find that the tightness of the laws of the appr

Giacomo Case

This study presents a comparative analysis of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods in the context of derivative pricing, emphasizing convergence rates and the curse of dimensionality. After a concise overview of traditional Monte Carlo techniques for evaluating expectations of derivative securities, the paper introduces quasi-Monte Carlo meth

Hao Cui, Taha Yasseri

As AI becomes more embedded in workplaces, it is shifting from a tool for efficiency to an active force in organizational decision-making. Whether due to anthropomorphism or intentional design choices, people often assign human-like qualities, including gender, to AI systems. However, how AI managers are perceived in comparison to human managers and how gend

Hannah Yang, Sohyeon Kim, Saeyeon Kim, Jiyoung Lee

Video compression plays a pivotal role in managing and transmitting large-scale display data, particularly given the growing demand for higher resolutions and improved video quality. This paper proposes an optimized memory system architecture for Video Electronics Standards Association (VESA) Display Compression-M (VDC-M) decoder, characterized by its substa

Mahsa Salmani, Ilya Soloveychik

The rapid development of the Transformer-based Large Language Models (LLMs) in recent years has been closely linked to their ever-growing and already enormous sizes. Many LLMs contain hundreds of billions of parameters and require dedicated hardware resources for training and inference. One of the key challenges inherent to the Transformer architecture is th

Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli

The remarkable success of deep learning in recent years has prompted applications in medical image classification and diagnosis tasks. While classification models have demonstrated robustness in classifying simpler datasets like MNIST or natural images such as ImageNet, this resilience is not consistently observed in complex medical image datasets where data

Keon Ju Maverick Lee, Jeff Ens, Sara Adkins, Pedro Sarmento

The Musical Instrument Digital Interface (MIDI), introduced in 1983, revolutionized music production by allowing computers and instruments to communicate efficiently. MIDI files encode musical instructions compactly, facilitating convenient music sharing. They benefit Music Information Retrieval (MIR), aiding in research on music understanding, computational

Venkat Padmasola, Zhaotong Li, Rupak Chatterjee, Wesley Dyk

We study the application of emerging photonic and quantum computing architectures to solving the Traveling Salesman Problem (TSP), a well-known NP-hard optimization problem. We investigate several approaches: Simulated Annealing (SA), Quadratic Unconstrained Binary Optimization (QUBO-Ising) methods implemented on quantum annealers and Optical Coherent Ising

Rajat Subhra Hazra, Frank den Hollander, Azadeh Parvaneh

In Hazra, den Hollander and Parvaneh (2025) we analysed the friendship paradox for sparse random graphs. For four classes of random graphs we characterised the empirical distribution of the friendship biases between vertices and their neighbours at distance $1$, proving convergence as $n\to\infty$ to a limiting distribution, with $n$ the number of vertices,

Alex Ziyu Jiang, Abel Rodriguez

The order flow in high-frequency financial markets has been of particular research interest in recent years, as it provides insights into trading and order execution strategies and leads to better understanding of the supply-demand interplay and price formation. In this work, we propose a semiparametric multivariate Hawkes process model that relies on (mixtu

Ants Remm, Nathan Lacroix, Lukas Bödeker, Elie Genois

High-fidelity decoding of quantum error correction codes relies on an accurate experimental model of the physical errors occurring in the device. Because error probabilities can depend on the context of the applied operations, the error model is ideally calibrated using the same circuit as is used for the error correction experiment. Here, we present an expe

Xiangwen Wang, Yibo Jacky Zhang, Zhoujie Ding, Katherine Tsai

Compound AI systems, comprising multiple interacting components such as LLMs, foundation models, and external tools, have demonstrated remarkable improvements compared to single models in various tasks. To ensure their effective deployment in real-world applications, aligning these systems with human preferences is crucial. However, aligning the compound sys

Yuxuan Li, Hirokazu Shirado

Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simu

Valeriy Vasilyev, Timo Reinhold, Alexander I. Shapiro, Theodosios Chatzistergos

The light curves of old G-dwarfs obtained in the visible and near-infrared wavelength ranges are highly irregular. This significantly complicates the detectability of the rotation periods of stars similar to the Sun in large photometric surveys, such as Kepler and TESS. In this study, we show that light curves collected in the ultraviolet wavelength range ar

Andreas Basse-O'Connor, David Kramer-Bang

In this paper, we establish explicit quantitative Berry-Esseen bounds in the hyper-rectangle distance $d_R$, the convex distance $d_{\mathscr{C}}$ and the $1$-Wasserstein distance $d_W$ for high-dimensional, non-linear functionals of Gaussian processes, allowing for strong dependence between variables. Our main result demonstrates that, under a smoothness as

Guanlin Liu, Anand Ramachandran, Tanmay Gangwani, Yan Fu

Knowledge distillation is used, in generative language modeling, to train a smaller student model using the help of a larger teacher model, resulting in improved capabilities for the student model. In this paper, we formulate a more general framework for knowledge distillation where the student learns from the teacher during training, and also learns to ask

Benjamin Ritz, Aleksandar Karakaš, Denis Helic

Commits often involve refactorings -- behavior-preserving code modifications aiming at software design improvements. Refactoring operations pose a challenge to code reviewers, as distinguishing them from behavior-altering changes is often not a trivial task. Accordingly, research on automated refactoring detection tools has flourished over the past two decad

Zhe Liu, Taekyu Kang, Haoyu Wang, Seyed Hossein Alavi

Generating diverse follow-up questions that uncover missing information remains challenging for conversational agents, particularly when they run on small, locally hosted models. To address this, we develop an information-gap-driven knowledge distillation pipeline in which a teacher LLM generates a comprehensive answer, contrasts it with the initial answer t

Anna Ravera, Cristina Gena

Generative AI systems are transforming content creation, but their usability remains a key challenge. This paper examines usability factors such as user experience, transparency, control, and cognitive load. Common challenges include unpredictability and difficulties in fine-tuning outputs. We review evaluation metrics like efficiency, learnability, and sati

Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir, Xenofon Koutsoukos

This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducing complexity in learning algorithms. We refer to these sparse graphs as "learning backbones". Our approach leverages the zero-forcing (ZF) phenomenon, a dynamic process on graphs w

Rhea Palak Bakshi, Benjamin A. Burton, Huizheng Guo, Dionne Ibarra

Yasutaka Nakanishi formulated the following conjecture in 1981: every link is 3-move equivalent to a trivial link. While the conjecture was proved for several specific cases, it remained an open question for over twenty years. In 2002, Mieczys{\l}aw D{\c a}bkowski and the last author showed that it does not hold, in general. In this article, we prove the Mon

Akhila Yerukola, Saadia Gabriel, Nanyun Peng, Maarten Sap

Gestures are an integral part of non-verbal communication, with meanings that vary across cultures, and misinterpretations that can have serious social and diplomatic consequences. As AI systems become more integrated into global applications, ensuring they do not inadvertently perpetuate cultural offenses is critical. To this end, we introduce Multi-Cultura

Yu Zhou, Bingxuan Li, Mohan Tang, Xiaomeng Jin

Large multimodal models (LMMs) often struggle to recognize novel concepts, as they rely on pre-trained knowledge and have limited ability to capture subtle visual details. Domain-specific knowledge gaps in training also make them prone to confusing visually similar, commonly misrepresented, or low-resource concepts. To help LMMs better align nuanced visual f

ByungKoo Kim, Saki Kuzushima, Yuki Shiraito

Social scientists analyze citation networks to study how documents influence subsequent work across various domains such as judicial politics and international relations. However, conventional approaches that summarize document attributes in citation networks often overlook the diverse semantic contexts in which citations occur. This paper develops the parag

Mathieu Baillif

In this long note, we investigate various purely topological aspects of non-Hausdorff manifolds (NH-manifolds for short). Our emphasis is on manifolds which exhibit homogeneity or weakenings thereof, in particular being everywhere non-Hausdorff. Homogeneous NH-manifolds and everywhere non-Hausdorff manifolds are respectively called HNH- and ENH-manifolds. We

Jungseok Hong, Sakshi Singh, Junaed Sattar

We present an image blending pipeline, \textit{IBURD}, that creates realistic synthetic images to assist in the training of deep detectors for use on underwater autonomous vehicles (AUVs) for marine debris detection tasks. Specifically, IBURD generates both images of underwater debris and their pixel-level annotations, using source images of debris objects,

Gustavo L. Kohlrausch, Thiago Dias, Sebastian Gonçalves

Wealth transactions are central to economic activity, and their particularities shape macroeconomic outcomes. We propose an agent-based model to investigate how homophily influences economic inequality. The model simulates wealth exchanges in a dynamic network composed of two groups, $A$ and $B$, differentiated by a homophily parameter $\delta$, which increa

Beria Chingnabe Kalpelbe, Angel Gabriel Adaambiik, Wei Peng

With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradigm shifts. This survey provides an extensive review of recent advancements in Medical Vision-Language Models (Med-VLMs), which integrate visual and textual data to enhance healthcare outcomes. We discuss the foun

Tamal K. Dey, Tao Hou, Dmitriy Morozov

Given a zigzag filtration, we want to find its barcode representatives, i.e., a compatible choice of bases for the homology groups that diagonalize the linear maps in the zigzag. To achieve this, we convert the input zigzag to a levelset zigzag of a real-valued function. This function generates a Mayer-Vietoris pyramid of spaces, which generates an infinite

Jason R. C. Nurse

As technology has become more embedded into our society, the security of modern-day systems is paramount. One topic which is constantly under discussion is that of patching, or more specifically, the installation of updates that remediate security vulnerabilities in software or hardware systems. This continued deliberation is motivated by complexities involv

Pavlos Kazakopoulos, Aris L. Moustakas

Transmission through optical fibers offers ultra-fast and long-haul communications. However, the search for its ultimate capacity limits in the presence of distributed amplifier noise is complicated by the competition between wave dispersion and non-linearity. In this paper, we exploit the integrability of the Nonlinear Schr\"{o}dinger Equation, which accura

Ruxiao Chen, Chenguang Wang, Yuran Sun, Xilei Zhao

Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different indi

J. L. Rodríguez-Sánchez, G. García-Jiménez, H. Alvarez-Pol, M. Feijoo-Fontán

Large-scale computations of fission properties play a crucial role in nuclear reaction network calculations simulating rapid neutron-capture process (r-process) nucleosynthesis. Due to the large number of fissioning nuclei contributing to the r-process, a description of particle-induced fission reactions is computationally challenging. In this work, we use t

Krti Tallam

Both parasites in biological systems and adversarial forces in cybersecurity are often perceived as threats: disruptive elements that must be eliminated. However, these entities play a critical role in revealing systemic weaknesses, driving adaptation, and ultimately strengthening resilience. This paper draws from environmental epidemiology and cybersecurity

Albert Rico

We construct both nonlinear and linear entanglement witnesses, by tensoring and partial tracing existing states and witnesses. We show that little shared quantum resources allow to employ decomposable witnesses to obtain larger ones detecting locally undistillable states, and find analytic witnesses for multipartite entangled states that are undistillable ac

Xiaohang Tang, Sangwoong Yoon, Seongho Son, Huizhuo Yuan

Self-play-based policy optimization has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference optimization as a two-player game. However, the regularization with respect to the reference policy, which is crucial for mitigating over-optimization, has been insufficiently investigated in self-play alignment. To st

M. H. M. Rashid

This paper establishes several new inequalities for the $A$-norm and $A$-numerical radius of operator sums in semi-Hilbertian spaces, significantly advancing the existing theory. We present two fundamental refinements of the generalized triangle inequality for operator norms, providing sharper estimates than previously known results. Our investigation yields

Lionel Mulato, Jaroslav Merc, Stéphane Charbonnel, Olivier Garde

Gaia DR3, released in June 2022, included low-resolution XP spectra that have been used for the classification of various types of emission-line objects through machine-learning techniques. The Gaia Extended Stellar Parametrizer for Emission-Line Stars (ESP-ELS) algorithm identified 565 sources as potential Wolf-Rayet (WR) stars. Over half of them were alrea

Hongming Zhang, Ruixin Hong, Dong Yu

Autoregressive decoding algorithms that use only past information often cannot guarantee the best performance. Recently, people discovered that looking-ahead algorithms such as Monte Carlo Tree Search (MCTS) with external reward models (RMs) can significantly improve models' output by allowing them to think ahead and leverage future outputs and associated re

Lei Zhao, Lin Cai, Wu-Sheng Lu

In decentralized financial systems, robust and efficient Federated Learning (FL) is promising to handle diverse client environments and ensure resilience to systemic risks. We propose Federated Risk-Aware Learning with Central Sensitivity Estimation (FRAL-CSE), an innovative FL framework designed to enhance scalability, stability, and robustness in collabora

Garrett Wilson, Geoffrey Goh, Yan Jiang, Ajay Gupta

Detecting phishing, spam, fake accounts, data scraping, and other malicious activity in online social networks (OSNs) is a problem that has been studied for well over a decade, with a number of important results. Nearly all existing works on abuse detection have as their goal producing the best possible binary classifier; i.e., one that labels unseen example

Conny Aerts, Timothy Van Reeth, Joey S. G. Mombarg, Daniel Hey

We combined Gaia DR3 and TESS photometric light curves to estimate the internal physical properties of 2,497 gravity-mode pulsators. We relied on asteroseismic properties of Kepler $\gamma\,$Dor and SPB stars to derive the near-core rotation frequency, $f_{\rm rot}$, of the Gaia-discovered pulsators from their dominant prograde dipole gravito-inertial pulsat

Lenny Jones

Suppose that $f(x)=x^4+Ax^3+Bx^2+Ax+1\in {\mathbb Z}[x]$. We say that $f(x)$ is monogenic if $f(x)$ is irreducible over ${\mathbb Q}$ and $\{1,\theta,\theta^2,\theta^3\}$ is a basis for the ring of integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. For each possible Galois group $G$ that can occur in the two cases of $A\ne 0$ with $B=0$, and $AB\ne 0$,

Zhixin Lu, Łukasz Kuśmierz, Stefan Mihalas

Inferring stochastic dynamics from data is central across the sciences, yet in many applications only unordered, non-sequential measurements are available-often restricted to limited regions of state space-so standard time-series methods do not apply. We introduce DyNoSeD, a first-principles framework that identifies unknown dynamical parameters from such no

Eva Palafox, Víctor Manuel Patiño-Álvarez, Vahram Chavushyan, Andrei Lobanov

3C 454.3 is a flat spectrum radio quasar (FSRQ) known for its high variability across the electromagnetic spectrum, showing structural and flux variability in its pc-scale jet, and correlated variability among frequency bands. This study aims to identify the structure, dynamics, and radiative processes common to the innermost regions of the blazar 3C 454.3.

Mohammad Bajelani, Klaske van Heusden

Control Barrier Functions (CBFs) offer a framework for ensuring set invariance and designing constrained control laws. However, crafting a valid CBF relies on system-specific assumptions and the availability of an accurate system model, underscoring the need for systematic data-driven synthesis methods. This paper introduces a data-driven approach to synthes

Abhishek Adimurthi, Peter Sternberg

For $\Omega$ a perturbation of the unit ball in $\mathbb{R}^3$, we establish the existence of a sequence of local minimizers for the vector Allen-Cahn energy. The sequence converges in $L^1$ to a partition of $\Omega$ whose skeleton is given by a tetrahedral cone and thus contains a quadruple point. This is accomplished by proving that the partition is an is

Neal Bushaw, Sean English, Emily Heath, Daniel P. Johnston

The forbidden subgraph problem is among the oldest in extremal combinatorics -- how many edges can an $n$-vertex $F$-free graph have? The answer to this question is the well-studied extremal number of $F$. Observing that every extremal example must be maximally $F$-free, a natural minimization problem is also studied -- how few edges can an $n$-vertex maxima

Debdeep Bhattacharya, Tyler P. Evans, Andrej Cherkaev

This paper examines various ways of improving the impact resilience of protective structures. Such structures' purpose is to dissipate an impact's energy while avoiding cracking and failure. We have tested the reaction of plane elastic-brittle lattices to an impulse. Four topologies are compared: periodic triangular, square, and hexagonal topologies, and ape

Jiacheng Wang, Xin Gao

Motivated by dynamic biologic network analysis, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all dynamic edge and vertex parameters. We further develop statistical inference p

Karl Pierce, Miguel Morales

We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver which leverages a precomputed THC factorization of an order-$4$ tensor to efficiently optimize the order-$4$ CPD with $\mathcal{O}(NR^2)$ scaling. With the matrix-free THC-based opt

Agustin G. Bonifacio

In the problem of fully allocating a social endowment of perfectly divisible commodities among a group of agents with multidimensional single-peaked preferences, we study strategy-proof rules that are not Pareto-dominated by other strategy-proof rules. Specifically, we: (i) establish a sufficient condition for a rule to be Pareto-undominated strategy-proof;

Krystal Ruiz-Rocha, Anjali B. Yelikar, Jacob Lange, William Gabella

Over a hundred gravitational-wave (GW) detections and candidates have been reported from the first three observing runs of the Advanced LIGO-Virgo-KAGRA (LVK) detectors. Among these, the most intriguing events are binary black hole mergers that result in a 'lite' intermediate-mass black hole (IMBH) of ${\sim}10^2~\mathrm{M}_\odot$, such as GW170502 and GW190

Kartheik G. Iyer, Camilla Pacifici, Gabriela Calistro-Rivera, Christopher C. Lovell

The spectral energy distribution (SED) of a galaxy represents the distribution of electromagnetic radiation emitted across all wavelengths, from radio waves to gamma rays. The galaxy SED is akin to its fingerprint, and serves as a fundamental tool in modern astrophysics. It enables researchers to determine crucial properties of galaxies, including their star

Ruizhe Zhang, Jooyoung Kong, Dylan S. Small, William Bekerman

Adverse childhood experiences (ACEs) have been linked to a wide range of negative health outcomes in adulthood. However, few studies have investigated what specific combinations of ACEs most substantially impact mental health. In this article, we provide the protocol for our observational study of the effects of combinations of ACEs on adult depression. We u

D. Anbajagane, C. Chang, A. Drlica-Wagner, C. Y. Tan

We present cosmological constraints from the Dark Energy Camera All Data Everywhere (DECADE) cosmic shear analysis. This work uses shape measurements for 107 million galaxies measured through Dark Energy Camera (DECam) imaging of $5,\!412$ deg$^2$ of sky that is outside the Dark Energy Survey (DES) footprint. We derive constraints on the cosmological paramet

D. Anbajagane, C. Chang, N. Chicoine, L. F. Secco

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous acr

D. Anbajagane, A. Alarcon, R. Teixeira, C. Chang

We present the photometric redshift characterization and calibration for the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The redshifts are estimated from a combination of wide-field photometry, deep-field photometry with associ

D. Anbajagane, C. Chang, Z. Zhang, C. Y. Tan

We present the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. This catalog was assembled from public DECam data including survey and standard observing programs. These data were consistently processed with the Dark Energy Survey D

Haoyu Yang, Sanjoy Dey, Pablo Meyer

Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multi

Boyang Deng, Yuzhen Lu

Robust weed detection remains a challenging task in precision weeding, requiring not only potent weed detection models but also large-scale, labeled data. However, the labeled data adequate for model training is practically difficult to come by due to the time-consuming, labor-intensive process that requires specialized expertise to recognize plant species.

Jinwoo Choi, Siming Deng, Nathan Justus, Noah J. Cowan

Motion planning for locomotion systems typically requires translating high-level rigid-body tasks into low-level joint trajectories-a process that is straightforward for car-like robots with fixed, unbounded actuation inputs but more challenging for systems like snake robots, where the mapping depends on the current configuration and is constrained by joint

Ronald DeVore, Robert D. Nowak, Rahul Parhi, Guergana Petrova

A fundamental problem in statistics and machine learning is to estimate a function $f$ from possibly noisy observations of its point samples. The goal is to design a numerical algorithm to construct an approximation $\hat f$ to $f$ in a prescribed norm that asymptotically achieves the best possible error (as a function of the number $m$ of observations and t

Alexandru Craevschi, Sarah Babinski, Chundra Cathcart

A large body of research on morphological paradigms makes the prediction that irregular morphological patterns of allomorphy are more likely to emerge and persist when they serve to mark important functional distinctions. More specifically, it has been observed that in some Germanic languages in which narrative past tense is expressed by the past participle,

Bushi Xiao, Michael Bennie, Jayetri Bardhan, Daisy Zhe Wang

Structural priming is a cognitive phenomenon where exposure to a particular syntactic structure increases the likelihood of producing the same structure in subsequent utterances. While humans consistently demonstrate structural priming effects across various linguistic contexts, it remains unclear whether multimodal large language models (MLLMs) exhibit simi

Bryan Shaddy, P. Alex Greaney, Bhargav Rallabandi

The transport of sea ice over the polar oceans plays an important role in climate. This transport is driven predominantly by turbulent winds, leading to stochastic motion of ice floes. Observed diffusivities and velocity distributions of sea ice deviate by orders of magnitude from Brownian models, making it challenging to predict ice transport. We fully reso

Axel Álvarez, Sebastián Donoso

We propose and develop an approach to study nilsystems and their proximal extensions using cube structures associated with the universal minimal system. We provide alternative proofs for results regarding saturation properties of factor maps to maximal nilfactors in cubes, as well as new results and applications of independent interest to the structural theo

Xi Liu, Yujun Zhao, Chun Yu Wan, Yang Zhang

We introduce Effective Field Neural Networks (EFNNs), a new architecture based on continued functions -- mathematical tools used in renormalization to handle divergent perturbative series. Our key insight is that neural networks can implement these continued functions directly, providing a principled approach to many-body interactions. Testing on three syste

Tsan Tsai Chan, Xin Tong, Thi Thu Uyen Hoang, Barbare Tepnadze

Multilingual large language models (LLMs) are known to more frequently generate non-faithful output in resource-constrained languages (Guerreiro et al., 2023 - arXiv:2303.16104), potentially because these typologically diverse languages are underrepresented in their training data. To mitigate unfaithfulness in such settings, we propose using computationally

Garima Joshi, Sayak Bose, Troy Carter, Daniel Wolf Savin

We have studied the propagation of inertial Alfv\'en waves through parallel gradients in the Alfv\'en speed using the Large Plasma Device at the University of California, Los Angeles. The reflection and transmission of Alfv\'en waves through inhomogeneities in the background plasma is important for understanding wave propagation, turbulence, and heating in s

Cornelis Jacobus van Diepen, Vasiliki Angelopoulou, Oliver August Dall'Alba Sandberg, Alexey Tiranov

Waveguide quantum electrodynamics (QED) has opened a new frontier in quantum optics, which enables the radiative coupling of distantly located emitters via the spatially extended waveguide mode. This coupling leads to modified emission dynamics and previous work has reported the observation of increased intensity correlations (an antidip) when probing the re

Yuhan Yao, Ryan Chornock, Charlotte Ward, Erica Hammerstein

Tidal disruption events (TDEs) that are spatially offset from the nuclei of their host galaxies offer a new probe of massive black hole (MBH) wanderers, binaries, triples, and recoiling MBHs. Here we present AT2024tvd, the first off-nuclear TDE identified through optical sky surveys. High-resolution imaging with the \textit{Hubble Space Telescope} shows that

Daniel Lisak, Vittorio D'Agostino, Szymon Wójtewicz, Agata Cygan

We introduce a spectroscopic approach to primary gas thermometry, harnessing precise optical cavity resonance frequencies and ab initio molecular line intensity calculations. By utilizing CO (3-0) vibrational band lines and cavity mode dispersion spectroscopy, we achieve an uncertainty of 82 ppm (24 mK at 296 K) in line-intensity-ratio thermometry (LRT) - ov

Tomas Nieponice, Veronica Valeros, Sebastian Garcia

We introduce ARACNE, a fully autonomous LLM-based pentesting agent tailored for SSH services that can execute commands on real Linux shell systems. Introduces a new agent architecture with multi-LLM model support. Experiments show that ARACNE can reach a 60\% success rate against the autonomous defender ShelLM and a 57.58\% success rate against the Over The

Nicolas Rotaru, Patrick Del Vecchio, Oussama Moutanabbir

Germanium (Ge) has emerged as a contender for scalable solid-state spin qubits. This interest stems from the numerous attractive properties of hole spin in Ge low-dimensional systems and their compatibility with the standards of silicon processing. Herein, we show that the controlled incorporation of Sn into the Ge lattice enables hole spin quantum dots that

Farshad Dizani, Azam Ghanbari, Joshua Kalyanapu, Darsh Asher

The rise of on-chip accelerators signifies a major shift in computing, driven by the growing demands of artificial intelligence (AI) and specialized applications. These accelerators have gained popularity due to their ability to substantially boost performance, cut energy usage, lower total cost of ownership (TCO), and promote sustainability. Intel's Advance

Xinyi Song, Lina Lee, Kexin Xie, Xueying Liu

The coding capabilities of large language models (LLMs) have opened up new opportunities for automatic statistical analysis in machine learning and data science. However, before their widespread adoption, it is crucial to assess the accuracy of code generated by LLMs. A major challenge in this evaluation lies in the absence of a benchmark dataset for statist

Prasad P. Iyer, Mihir Pendharkar, Anchal Agrawal, Humberto Foronda

Enhancing light-matter interactions at the nanoscale is foundational to nanophotonics, with epsilon near zero (ENZ) materials demonstrating significant potential.High-quality (Q) factor resonances maximizing these interactions are typically realized in photonic crystals requiring sub-50 nm precision nanofabrication over large areas, limiting scalability and

Hong Wang, Joshua Zahl

We study sets of $\delta$ tubes in $\mathbb{R}^3$, with the property that not too many tubes can be contained inside a common convex set $V$. We show that the union of tubes from such a set must have almost maximal volume. As a consequence, we prove that every Kakeya set in $\mathbb{R}^3$ has Minkowski and Hausdorff dimension 3.

Francesco Pisani, Usama Iqbal, Laure Tailpied, Baptiste Fix

The ability to confine photons into structures with highly sub-wavelength volumes is extremely interesting for many applications such as sensing, nonlinear optics, and strong light-matter interactions. However, their realization is increasingly difficult as the wavelength becomes shorter, due to fabrication challenges and increased metal losses. In this work

Sara Zain, Jannik Mähn, Stefan Köpsell, Sebastian Ertel

Remote attestation (RA) is the foundation for trusted execution environments in the cloud and trusted device driver onboarding in operating systems. However, RA misses a rigorous mechanized definition of its security properties in one of the strongest models, i.e., the semantic model. Such a mechanization requires the concept of StateSeparating Proofs (SSP).

Alice Borghese, Francesco Coti Zelati

Discovered over fifty years ago, neutron stars exhibit a remarkable variety of behaviors depending on their age, magnetic field strength, rotational dynamics, emission mechanisms, and surrounding environments. This diversity in their observational manifestations has led astronomers to classify neutron stars into numerous categories, much like wandering throu

Bingxuan Li, Yiwei Wang, Jiuxiang Gu, Kai-Wei Chang

Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has great potential to empower the automatic professional report generation in financial analysis, research presentation, education, and healthcare. In this work, we build a vision-language model (VLM) based multi-agent

Sameer Neupane, Poorvesh Dongre, Denis Gracanin, Santosh Kumar

We use a duoethnographic approach to study how wearable-integrated LLM chatbots can assist with personalized stress management, addressing the growing need for immediacy and tailored interventions. Two researchers interacted with custom chatbots over 22 days, responding to wearable-detected physiological prompts, recording stressor phrases, and using them to

Yuhao Wang, Hai-Chau Nguyen, Zhiyi Yuan, T. Thu Ha Do

We propose and experimentally demonstrate the hybridization of radiating topological interface states, analogous to Jackiw-Rebbi states but in gain media with radiation fields. This hybridization not only modifies energy levels under a strong coupling scheme but also significantly reshapes far-field radiation characteristics. The bonding mode exhibits sub-ra

Lei Cheng, Lihao Guo, Tianya Zhang, Tam Bang

Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real

Y. J. Sun, F. Yang, L. Q. Chen

The D'yakonov-Perel' (DP) spin-relaxation mechanism has traditionally been associated with either relativistic spin-orbit coupling, which breaks space-inversion symmetry, or inhomogeneous magnetization, which breaks both time-reversal and translational symmetries. Here, we investigate spin relaxation mechanism in altermagnetic systems which possess novel mag

Hiya Bhatt, Sahil, Karthik Vaidhyanathan, Rahul Biju

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital

Susobhan Ghosh, Pei-Yao Hung, Lara N. Coughlin, Erin E. Bonar

Cannabis use among emerging adults is increasing globally, posing significant health risks and creating a need for effective interventions. We present an exploratory analysis of the MiWaves pilot study, a digital intervention aimed at supporting cannabis use reduction among emerging adults (ages 18-25). Our findings indicate the potential of self-monitoring

Sean Lynch, Jacob LaMountain, Bo Fan, Jie Bu

While machine learning (ML) has found multiple applications in photonics, traditional "black box" ML models typically require prohibitively large training data sets. Generation of such data, as well as the training processes themselves, consume significant resources, often limiting practical applications of ML. Here we demonstrate that embedding Maxwell's eq

Sangwon Seo, Bing Han, Rayan E. Harari, Roger D. Dias

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-

Somya Mehra, Peter G. Taylor

Plasmodium vivax malaria is a mosquito-borne disease of significant public health importance. A defining feature of the within-host biology of P. vivax is the accrual of a hypnozoite reservoir, comprising a bank of quiescent parasites in the liver that are capable of causing relapsing blood-stage infections upon activation. Superinfection, characterised by c

Matthew Dobson, David Masse

This paper uses Cahn-Hilliard equations as a mesoscale model of the motion of active colloids. The model attempts to capture the driving mechanisms and qualitative behavior of the isotropic colloids originally proposed by J. Decayeaux in 2021. We compare our model against the single colloid behavior presented in that work, as well as against multi-colloid sy