December 2023 arXiv papers — page 83

Kolja Junginger, Ioannis Mantas, Evanthia Papadopoulou

We present a generalization of a combinatorial result by Aggarwal, Guibas, Saxe and Shor [Discrete & Computational Geometry, 1989] on a linear-time algorithm that selects a constant fraction of leaves, with pairwise disjoint neighborhoods, from a binary tree embedded in the plane. This result of Aggarwal et al. is essential to the linear-time framework, whic

Marcelo Orenes-Vera, Esin Tureci, Margaret Martonosi, David Wentzlaff

The design space exploration of scaled-out manycores for communication-intensive applications (e.g., graph analytics and sparse linear algebra) is hampered due to either lack of scalability or accuracy of existing frameworks at simulating data-dependent execution patterns. This paper presents MuchiSim, a novel parallel simulator designed to address these cha

Tyler E. Maltba, Hongli Zhao, D. Adrian Maldonado

We introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in cl

I-Chi Chen, Harshdeep Singh, V L Anukruti, Brian Quanz

Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models. We re-implement a key represen

Aasim Jan, Deborah Ferguson, Jacob Lange, Deirdre Shoemaker

As gravitational wave detectors improve in sensitivity, signal-to-noise ratios of compact binary coalescences will dramatically increase, reaching values in the hundreds and potentially thousands. Such strong signals offer both exciting scientific opportunities and pose formidable challenges to the template waveforms used for interpretation. Current waveform

Youwei Liang, Junfeng He, Gang Li, Peizhao Li

Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However, many generated images still suffer from issues such as artifacts/implausibility, misalignment with text descriptions, and low aesthetic quality. Inspired by the success of Re

Ian Stewart Joyce, Grant Erdmann, Kirk P. Gardner, Ryan Kramer

Topological data analysis is an emerging field that applies the study of topological invariants to data. Perhaps the simplest of these invariants is the number of connected components or clusters. In this work, we explore a topological framework for cluster analysis and show how it can be used as a basis for explainability in unsupervised data analysis. Our

Albert Cohen, Jimmy Risk

This paper presents a new framework for player valuation in European football, by fusing principles from financial mathematics and network theory. The valuation model leverages a "passing matrix" to encapsulate player interactions on the field, utilizing centrality measures to quantify individual influence. Unlike traditional approaches, such as regressing o

Weisong Yang, Rafael Poyiadzi, Niall Twomey, Raul Santos Rodriguez

In supervised learning, automatically assessing the quality of the labels before any learning takes place remains an open research question. In certain particular cases, hypothesis testing procedures have been proposed to assess whether a given instance-label dataset is contaminated with class-conditional label noise, as opposed to uniform label noise. The e

Paul K. Mandal

In the era of rapidly advancing medical technologies, the segmentation of medical data has become inevitable, necessitating the development of privacy preserving machine learning algorithms that can train on distributed data. Consolidating sensitive medical data is not always an option particularly due to the stringent privacy regulations imposed by the Heal

João Barata, Jean-Paul Blaizot, Yacine Mehtar-Tani

We study the time evolution of the density matrix of a high energy quark in the presence of a dense QCD background that is modeled as a stochastic Gaussian color field. At late times, we find that only the color singlet component of the quark's reduced density matrix survives the in-medium evolution and that the density matrix becomes asymptotically diagonal

Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham

Data-driven reduced-order models of the dynamics of complex flows are important for tasks related to design, understanding, prediction, and control. Many flows obey symmetries, and the present work illustrates how these can be exploited to yield highly efficient low-dimensional data-driven models for chaotic flows. In particular, incorporating symmetries bot

Anna Guo, David Benkeser, Razieh Nabi

Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed covariates, it fails in the presence of unmeasured confounding. The front-door criterion offers an alternative by leveraging variables that fully mediate the treatment effect and ar

Sohail Reddy

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to their full potential, such as quantum algorithms. To achieve such a goal, systematic search for an optimal control for any given experiment is essential. Design of optimal control pulses require accurate numerical models, and therefore, accurate characterization

Dan Hooper, Elena Pinetti, Anastasia Sokolenko

Pulsars are observed to emit bright and spatially extended emission at multi-TeV energies. Although such "TeV halos" appear to be an approximately universal feature of middle-aged pulsars, there remains much to be understood about these systems. In this paper, we project the ability of the Cherenkov Telescope Array (CTA) to measure the properties of TeV halo

Francisco J. Vielma-Leal, Miguel A. D. R. Palma, Miguel Montenegro-Concha

The aim of this work is to study the effect of diffusion on the stability of the equilibria in a general two-components reaction-diffusion system with Neumann boundary conditions in the space of continuous functions. As by product, we establish sufficient conditions on the diffusive coefficients and other parameters for such a reaction-diffusion model to exh

Minjae Cho, Chuangchuang Sun

Despite remarkable achievements in artificial intelligence, the deployability of learning-enabled systems in high-stakes real-world environments still faces persistent challenges. For example, in safety-critical domains like autonomous driving, robotic manipulation, and healthcare, it is crucial not only to achieve high performance but also to comply with gi

Alejandro Corichi, Juan D. Reyes, Tatjana Vukasinac

The Hamiltonian description of classical gauge theories is a very well studied subject. The two best known approaches, namely the covariant and canonical Hamiltonian formalisms have received a lot of attention in the literature. However, a full understanding of the relation between them is not available, specially when the gauge theories are defined over reg

D. J. Heilman, K. L. Sauer, D. W. Prescott, C. Z. Motamedi

We describe one of the largest radio-frequency RF atomic magnetometers presently operating. A total atomic volume of 128 $\mathrm{cm^3}$, with correspondingly large number of $^{87}$Rb atoms, can reduce atom noise. A total of 44 passes of the probe beam reduces photon-shot noise. The atomic vapor is divided between two chambers allowing for pumping of the ce

Haozhe Yang, Beatriz Martín-García, Jozef Kimák, Eva Schmoranzerová

Angle-twisting engineering has emerged as a powerful tool for modulating electronic properties in van der Waals heterostructures. Recent theoretical works have predicted the modulation of spin texture in graphene-based heterostructures by twist angle, although an experimental verification is missing. Here, we demonstrate the tunability of the spin texture an

Bryan O. Torres Maldonado, Shravan Pradeep, Ranjiangshang Ran, Douglas Jerolmack

Microorganisms are ubiquitous in nature and technology. They inhabit diverse environments ranging from small river tributaries and lakes to oceans, as well as wastewater treatment plants and food manufacturing. In many of these environments, microorganisms coexist with settling particles. Here, we investigate the effects of microbial activity (swimming \text

Wen Wang, Zhenyue Zhao, Tianshu Sun

The advent of Large Language Models (LLMs) has ushered in a new era for design science in Information Systems, demanding a paradigm shift in tailoring LLMs design for business contexts. We propose and test a novel framework to customize LLMs for general business contexts that aims to achieve three fundamental objectives simultaneously: (1) aligning conversat

Sai Krishna Kanth Hari, Ahmed Zamzam, Byron Tasseff, Russell Bent

This research explores the joint expansion planning of power and water distribution networks, which exhibit interdependence at various levels. We specifically focus on the dependency arising from the power consumption of pumps and develop models to seamlessly integrate new components into existing networks. Subsequently, we formulate the joint expansion plan

Zoltán Kovács-Krausz, Dániel Nagy, Albin Márffy, Bogdan Karpiak

The layered van der Waals material ZrTe$_5$ is known as a candidate topological insulator (TI), however its topological phase and the relation with other properties such as an apparent Dirac semimetallic state is still a subject of debate. We employ a semiclassical multicarrier transport (MCT) model to analyze the magnetotransport of ZrTe$_5$ nanodevices at

Sina Taamoli, Bahram Mobasher, Nima Chartab, Behnam Darvish

To study the role of environment in galaxy evolution, we reconstruct the underlying density field of galaxies based on COSMOS2020 (The Farmer catalog) and provide the density catalog for a magnitude limited ($K_{s}<24.5$) sample of $\sim 210 \, k$ galaxies at $0.4<z<5$ within the COSMOS field. The environmental densities are calculated using weighted Kernel

A. González-Jerez, J. M. García-Regaña, I. Calvo, D. Carralero

The first experimental campaigns of Wendelstein 7-X (W7-X) have shown that turbulence plays a decisive role in the performance of neoclassically optimized stellarators. This stresses the importance of understanding microturbulence from the theoretical and experimental points of view. To this end, this paper addresses a comprehensive characterization of the t

Ievgenii Afanasiev, Tatyana Shcherbina

We consider the asymptotic local behavior of the second correlation function of the characteristic polynomials of sparse non-Hermitian random matrices $X_n$ whose entries have the form $x_{jk}=d_{jk}w_{jk}$ with iid complex standard Gaussian $w_{jk}$ and normalised iid Bernoulli$(p)$ $d_{jk}$. It is shown that, as $p\to\infty$, the local asymptotic behavior

Cornelius Brand, Robert Ganian, Subrahmanyam Kalyanasundaram, Fionn Mc Inerney

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computat

J. Wang, N. Chiang, A. Gillette, J. L. Peterson

Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cos

Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers, Martin R. Oswald

The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious self-supervised pre-training paradigms. Nevertheless, temporal information, which is inherent in the LiDAR point cloud sequence, is consistently disregarded. To better utilize this pr

Hang Dong, Jean-Yves Desaules, Yu Gao, Ning Wang

Emerging quantum technologies hold the promise of unraveling difficult problems ranging from condensed matter to high energy physics, while at the same time motivating the search for unprecedented phenomena in their setting. Here we utilize a custom-built superconducting qubit ladder to realize non-thermalizing states with rich entanglement structures in the

Zixuan Wang, Ryan A. DeCrescent, Poolad Imany, Joey T. Bush

Self-assembled InAs quantum dots (QDs) are promising optomechanical elements due to their excellent photonic properties and sensitivity to local strain fields. Microwave-frequency modulation of photons scattered from these efficient quantum emitters has been recently demonstrated using surface acoustic wave (SAW) cavities. However, for optimal performance, a

Md Al Amin, Hemanth Tummala, Seshamalini Mohan, Indrajit Ray

This paper addresses the critical challenge of ensuring healthcare policy compliance in the context of Electronic Health Records (EHRs). Despite stringent regulations like HIPAA, significant gaps in policy compliance often remain undetected until a data breach occurs. To bridge this gap, we propose a novel blockchain-powered, smart contract-based access cont

Michael J Friedel, Massimo Buscema

A novel multiphysics-decision tree learning algorithm is presented for (1) estimating transport properties in the variably saturated subsurface governed by explicitly coupled equations for water, heat, and solute transport; and (2) providing reduced order simulation of time-dependent pressure head, temperature, and concentration with subsurface properties an

Fabian Hinder, Valerie Vaquet, Barbara Hammer

Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the streaming context usually assuming that consecutive data points are independent of each other. To generalize to dependent data, many authors link the notion of concept drift to time s

Daniel Farley

We outline a general procedure that builds classifying spaces for generalized Thompson groups $\Gamma$. The construction depends on a small number of choices: (1) an inverse semigroup $S$ of partial transformations that ``locally determine" $\Gamma$; (2) an equivalence relation on certain pairs $(f,D)$, and (3) an ``expansion" rule $\mathcal{E}$. These choic

Liqiang Jing, Xuemeng Song, Xinxing Zu, Na Zheng

Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e. Sign2Gloss) and then translating the generated gloss sequence into a spoken language sentence (i.e. Gloss2Text). While previous studies have focused on boosting the performance of the Sign2Gloss stage, we emphasize the op

Minxue Niu, Zhaobo Zheng, Kumar Akash, Teruhisa Misu

Humans' internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: long time-series signals are usually associated with a single l

Arefeh Rezaei, Mohammad Javad Ahmadi, Amir Molaei, Hamid. D. Taghirad

This paper aims to present a novel pipeline for automated surgical skill assessment using video data and to showcase the effectiveness of the proposed approach in evaluating surgeon proficiency, its potential for targeted training interventions, and quality assurance in surgical departments. The pipeline incorporates a representation flow convolutional neura

Georgi S. Medvedev, Dmitry E. Pelinovsky

The Swift-Hohenberg equation (SHE) is a partial differential equation that explains how patterns emerge from a spatially homogeneous state. It has been widely used in the theory of pattern formation. Following a recent study by Bramburger and Holzer [2], we consider discrete SHE on deterministic and random graphs. The two families of the discrete models shar

Devon Maywald, Dixon Vimalajeewa

Food safety and quality are paramount concerns worldwide, especially concerning nutritional quality and its impact on human health. Ensuring the accuracy and efficiency of milk quality assessment is vital for maintaining the quality of dairy farm produce. Milk spectral data, Mid-infrared spectra (MIRS) of milk samples, are frequently employed for milk qualit

Chien-Yu Lin, Qichen Fu, Thomas Merth, Karren Yang

Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. I

Taylor Lundy, Narun Raman, Hu Fu, Kevin Leyton-Brown

Mobile gaming is a rapidly growing and incredibly profitable sector; having grown seven-fold over the past 10 years, it now grosses over $100 billion annually. This growth was due in large part to a shift in monetization strategies: rather than charging players an upfront cost ("pay-to-play"), games often request optional microtransactions throughout gamepla

Wesley Calvert, Emma Grunner, Elvira Mayordomo, Daniel Turetsky

Normal numbers were introduced by Borel and later proven to be a weak notion of algorithmic randomness. We introduce here a natural relativization of normality based on generalized number representation systems. We explore the concepts of supernormal numbers that correspond to semicomputable relativizations, and that of highly normal numbers in terms of comp

Rejitha Raveendran, Arun D. Mahindrakar, Umesh Vaidya

Optimization problems emerging in most of the real-world applications are dynamic, where either the objective function or the constraints change continuously over time. This paper proposes projected primal-dual dynamical system approaches to track the primal and dual optimizer trajectories of an inequality constrained time-varying (TV) convex optimization pr

Andrés L. Cook, Mason A. Dearborn, Trevor M. Anderberg, Kavya D. Vaidya

Frontal polymerization (FP) is an approach for thermosetting plastics at lower energy cost than an autoclave. The potential to generate simultaneous propagation of multiple polymerization fronts has been discussed as an exciting possibility. However, FP initiated at more than two points simultaneously has not been demonstrated. Multi-point initiation could e

Tuan Dung Le, Thanh Duong, Thanh Thieu

Objective: Function is increasingly recognized as an important indicator of whole-person health. This study evaluates the ability of publicly available large language models (LLMs) to accurately identify the presence of functioning information from clinical notes. We explore various strategies to improve the performance on this task. Materials and Methods: W

Cheng Peng, Ke Chen, Lidan Shou, Gang Chen

Multi-modal multi-label emotion recognition (MMER) aims to identify relevant emotions from multiple modalities. The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data. Recent studies are mainly devoted to exploring various fusion strategies to integrate multi-modal information into a unified re

Stefanos Ginargiros, Nikolaos Passalis, Anastasios Tefas

Deep Learning (DL) has brought significant advances to robotics vision tasks. However, most existing DL methods have a major shortcoming, they rely on a static inference paradigm inherent in traditional computer vision pipelines. On the other hand, recent studies have found that active perception improves the perception abilities of various models by going b

Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger, Sebastian Trimpe

Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems, but is limited by its computational complexity. We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC sch

Mike Jin, Nicole M Duggan, Varoon Bashyakarla, Maria Alejandra Duran Mendicuti

Accurate and scalable annotation of medical data is critical for the development of medical AI, but obtaining time for annotation from medical experts is challenging. Gamified crowdsourcing has demonstrated potential for obtaining highly accurate annotations for medical data at scale, and we demonstrate the same in this study for the segmentation of B-lines,

Karthik Elamvazhuthi, Matt Jacobs

We consider the optimal transport problem over convex costs arising from optimal control of linear time-invariant(LTI) systems when the initial and target measures are assumed to be supported on the set of equilibrium points of the LTI system. In this case, the probability measures are singular with respect to the Lebesgue measure, thus not considered in pre

Omri Ben-Eliezer, Tomer Grossman, Moni Naor

Suppose you are given a function $f\colon [n] \to [n]$ via (black-box) query access to the function. You are looking to find something local, like a collision (a pair $x \neq y$ s.t. $f(x)=f(y)$). The question is whether knowing the "shape" of the function helps you or not (by shape we mean that some permutation of the function is known). Formally, we invest

Reginald Anderson

The cellular resolution of the diagonal given by Bayer-Popescu-Sturmfels for unimodular projective toric varieties yields a full, strong exceptional collection of line bundles on unimodular projective toric surfaces. The Hanlon-Hicks-Lazarev resolution of the diagonal yields a full, strong exceptional collection of line bundles for 16 of the 18 smooth toric

David C. Jeong, Hongji Liu, Saunder Salazar, Jessie Jiang

While recent two-stage many-to-one deep learning models have demonstrated great success in 3D human pose estimation, such models are inefficient ways to detect 3D key points in a sequential video relative to one-shot and many-to-many models. Another key drawback of two-stage and many-to-one models is that errors in the first stage will be passed onto the sec

Paul Seurin, Koroush Shirvan

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions can be time-consuming. PEARL distinguishes itself from traditional policy-based multi-objective Reinforcement Le

Bartosz Wójcik, Alessio Devoto, Karol Pustelnik, Pasquale Minervini

While transformer models have been highly successful, they are computationally inefficient. We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the "effective" width needed to process a token can vary from layer to layer. Motivated by this observation, we introduce the Adaptive C

Ronaldo N. Araújo, Carlo C. Bellinati, Eric C. Andrade

Inspired by recent experimental studies of local magnetic moments interacting with a metallic quasicrystal, we study the low-temperature fate of spins placed in two-dimensional tilings. In the diluted local moment limit, we calculate the spin relaxation rate $1/T_{1}$, as measured by electron spin resonance, and show that it displays a marked dependence on t

Erez Yosef, Raja Giryes

Image reconstruction from noisy sensor measurements is challenging and many methods have been proposed for it. Yet, most approaches focus on learning robust natural image priors while modeling the scene's noise statistics. In extremely low-light conditions, these methods often remain insufficient. Additional information is needed, such as multiple captures o

Bengu Ozugur Uysal, Onder Pekcan

Hydrogel/inorganic composites have attracted attention in many applications, such as optoelectronic devices, biosensors, catalysis, and energy storage because of their capacity to increase and regulate optical and electronic features. In this study, MoSe$_2$-enhanced polyacrylamide composites were formed using a free radical crosslinking copolymerization pro

Amit Dutta, Thinh T. Doan, Jeffrey H. Reed

We study the problem of Byzantine fault tolerance in a distributed optimization setting, where there is a group of $N$ agents communicating with a trusted centralized coordinator. Among these agents, there is a subset of $f$ agents that may not follow a prescribed algorithm and may share arbitrarily incorrect information with the coordinator. The goal is to

Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov

We introduce WordScape, a novel pipeline for the creation of cross-disciplinary, multilingual corpora comprising millions of pages with annotations for document layout detection. Relating visual and textual items on document pages has gained further significance with the advent of multimodal models. Various approaches proved effective for visual question ans

Nhat-Tan Bui, Dinh-Hieu Hoang, Thinh Phan, Minh-Triet Tran

The electrocardiogram (ECG) is a valuable signal used to assess various aspects of heart health, such as heart rate and rhythm. It plays a crucial role in identifying cardiac conditions and detecting anomalies in ECG data. However, distinguishing between normal and abnormal ECG signals can be a challenging task. In this paper, we propose an approach that lev

Renan Favero, Lily Elefteriadou

Autonomous shuttles (AS) operate in several cities and have shown potential to improve the public transport network. However, there is no car following model that is based on field data and allows decision-makers to assess and plan for AS operations. To fill this gap, this study collected field data from AS, analyzed their driving performance, and suggested

Mingyuan Hu, Gus Schrader, Eric Zaslow

In previous work of the second- and third-named authors with Linhui Shen, cluster theory was used to construct wavefunctions for branes in threespace and conjecturally relate them to open Gromov-Witten invariants. This was done by defining a quantum Lagrangian subvariety of a quantum cluster variety, and mutating a simple solution to the defining equations i

Chunzhuo Wang, T. Sunil Kumar, Walter De Raedt, Guido Camps

Eating speed is an important indicator that has been widely investigated in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eatin

Jiachen Zhao

Knowledge distillation (KD) has been widely employed to transfer knowledge from a large language model (LLM) to a specialized model in low-data regimes through pseudo label learning. However, pseudo labels generated by teacher models are usually noisy and may influence KD performance. This study delves into KD with noisy teachers and uncovers that the studen

John Bulava, Danny Darvish, Andrew D. Hanlon, Ben Hörz

Studies of the $\Delta$ baryon resonance and the $K_0^\ast(700)$ and $a_0(980)$ meson resonances using $N_f=2+1$ lattice QCD for pion masses near 200 MeV are presented. The $s$-wave scattering lengths for both the $I=1/2$ $N \pi$ and $I=3/2$ $N \pi$ channels and properties of the $\Delta$ resonance are identified from the finite-volume energy levels of the l

Irem Dogruoglu, Yagmur Akarsu, Cagla Selen Sen, Onder Pekcan

Microalgae can be used in the packaging sector due to their ability to synthesize bioactive compounds that are favorable in the production of biodegradable packaging. In this work, to create an alternative to plastic packaging, we studied microalgae Chlorella in the presence of k-Carrageenan to form biodegradable packaging using various approaches such as ge

Shrihari D. Pande, Evgeniy Boyko, Ivan C. Christov

We demonstrate the use of the Lorentz reciprocal theorem in obtaining corrections to the steady flow rate due to flow oscillations in rigid channels. Starting from the unsteady Stokes equations, we derive the suitable reciprocity relation, assuming all quantities can be expressed as time-harmonic phasors. The auxiliary problem is the steady Hagen--Poiseuille

Alexander Ukhlov

In this work we consider refined geometric characterizations of mappings generate composition operators on Sobolev spaces. The detailed proofs in the cases $n-1<q<n$ and $n>q$ are given.

Taining Zhang, Adam Goldstein, Michael Levin

The emerging field of Diverse Intelligence seeks to identify, formalize, and understand commonalities in behavioral competencies across a wide range of implementations. Especially interesting are simple systems that provide unexpected examples of memory, decision-making, or problem-solving in substrates that at first glance do not appear to be complex enough

Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma

Deep neural networks have achieved exceptional results across a range of applications. As the demand for efficient and sparse deep learning models escalates, the significance of model compression, particularly pruning, is increasingly recognized. Traditional pruning methods, however, can unintentionally intensify algorithmic biases, leading to unequal predic

Jose L. Ocana-Pujol, Rebecca A. Gallivan, Ramon Camilo Dominguez Ordoñez, Nikolaus Porenta

Molybdenum disulfide, $MoS_2$, is a next-generation semiconductor and is frequently integrated into emergent optoelectronic technologies based on two-dimensional materials. Here, we present a method that provides direct optical feedback on the thickness and crystallinity of sputter-deposited $MoS_2$ down to the few-layer regime. This smart sensing enables tr

Minh Tran, Roochi Shah, Zejun Gong

We present a novel approach in the domain of federated learning (FL), particularly focusing on addressing the challenges posed by modality heterogeneity, variability in modality availability across clients, and the prevalent issue of missing data. We introduce a meta-learning framework specifically designed for multimodal federated tasks. Our approach is mot

Kevin O'Keeffe, Gourab Kumar Sar, Md Sayeed Anwar, Joao U. F. Lizárraga

Swarmalators are oscillators that swarm through space as they synchronize in time. Introduced a few years ago to model many systems which mix synchrony with self-assembly, they remain poorly understood theoretically. Here we obtain the first analytic results on swarmalators moving in two-dimensional (2D) plane by enforcing periodic boundary conditions; this

G. Calistro-Rivera, D. M. Alexander, C. M. Harrison, V. A. Fawcett

We present a comprehensive study of the physical origin of radio emission in optical quasars at redshifts z < 2.5. We focus particularly on the associations between compact radio emission, dust reddening, and outflows identified in our earlier work. Leveraging the deepest low-frequency radio data available to date (LoTSS Deep DR1), we achieve radio detection

Peizhao Li, Junfeng He, Gang Li, Rachit Bhargava

Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes. Yet most prior research has focused on modeling implicit and explicit human behavior in isolation; and often limited to a specific type of visual conte

Litian Liu, Yao Qin

Efficient and effective Out-of-Distribution (OOD) detection is essential for the safe deployment of AI systems. Existing feature space methods, while effective, often incur significant computational overhead due to their reliance on auxiliary models built from training features. In this paper, we propose a computationally-efficient OOD detector without using

Anton Baranov, Yurii Belov

We study Gabor frames in the case when the window function is of hyperbolic secant type, i.e., $g(x) = (e^{ax}+e^{-bx})^{-1}$, ${\rm Re}\,a, {\rm Re}\,b>0$. A criterion for half-irregular sampling is obtained: for a separated $\Lambda\subset\mathbb{R}$ the Gabor system $\mathcal{G}(g, \Lambda \times \alpha\Z)$ is a frame in $L^2(\R)$ if and only if $D^-(\Lam

Xiao Han

We use Hopf algebroids to formulate a notion of a noncommutative and non-cocommutative Hopf 2-algebra. We show how these arise from a bicrossproduct Hopf algebra with Peiffer identities. In particular, we show that for a Hopf algebra $H$ with bijective antipode, the mirror bicrossproduct Hopf algebra $H\triangleright\!\!\!\blacktriangleleft H_{cop}$ is a Hop

Bartek Wydrowski, Robert Kleinberg, Stephen M. Rumble, Aaron Archer

We present Prequal (Probing to Reduce Queuing and Latency), a load balancer for distributed multi-tenant systems. Prequal aims to minimize real-time request latency in the presence of heterogeneous server capacities and non-uniform, time-varying antagonist load. It actively probes server load to leverage the power-of-d-choices paradigm, extending it with asy

Jan Drchal, Herbert Ullrich, Tomáš Mlynář, Václav Moravec

This article presents a pipeline for automated fact-checking leveraging publicly available Language Models and data. The objective is to assess the accuracy of textual claims using evidence from a ground-truth evidence corpus. The pipeline consists of two main modules -- the evidence retrieval and the claim veracity evaluation. Our primary focus is on the ea

Wei Li, Fu-Lin Hsu, Will Bishop, Folawiyo Campbell-Ajala

Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation

Niels Dickson

An ability that underlies human syntactic knowledge is determining which words can appear in the similar structures (i.e. grouping words by their syntactic categories). These groupings enable humans to combine structures in order to communicate complex meanings. A foundational question is how do children acquire this ability underlying syntactic knowledge. I

Jeanna Buldyreva, Ryan P. Brady, Sergei N. Yurchenko, Jonathan Tennyson

To meet burning needs of high-resolution pressure-induced line-shape parameters in the UV/visible regions for hot-temperature industrial and atmospheric applications as well as current and future space missions, phase-shift theory is examined in its historical context, tested and revisited using accurate numerical potentials and advanced trajectory models. F

Khaled Mahmud, Taki Yashir, Ahmed Zubair

Tuberculosis (TB) remains a major global health concern, necessitating the exploration of novel drug delivery systems to combat the challenges posed by conventional approaches. We investigated the potential of monolayer transition metal dichalcogenides (TMDs) as an innovative platform for efficient and targeted delivery of antituberculosis drugs. Specificall

Jessica Churchill, Lipei Du, Bailey Forster, Han Gao

The lepton pair production rate at finite temperature and at next-to-leading-order (NLO) is calculated for quark-gluon plasma at non-zero baryon density. Yields are obtained using a (3+1)D multicomponent simulation capable of reproducing hadronic observables measured in the RHIC Beam Energy Scan. Spectra of intermediate invariant mass dileptons are compared

Yanan Wu, Zhixiang Chi, Yang Wang, Konstantinos N. Plataniotis

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decou

M. L. Rubio Puzzo

The dynamic effects on a magnetic system exposed to a time-oscillating external temperature are studied using Monte Carlo simulations on the classic 2D Ising Model. The time dependence of temperature is defined as $T(t)=T_0 + A \cdot \sin(2\pi t/\tau)$. Magnetization $M(t)$ and period-averaged magnetization $\langle Q\rangle$ are analyzed to characterize out

Xu Liu, Tong Zhou, Yuanxin Wang, Yuping Wang

The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities. Mirroring the transformative impact of foundation models like large language models (LLMs) in natural language processing, visual foundation models (VFMs)

Rui-Cheng Liu, Yang Liu, Alain Goriely

We study the surface wrinkling of a stiff thin elastic film bonded to a compliant graded elastic substrate subject to compressive stress generated either by compression or growth of the bilayer. Our aim is to clarify the influence of the modulus gradient on the onset and surface pattern in this bilayers. Within the framework of finite elasticity, an exact bi

Rupam Samanta, Somadutta Bhatta, Jiangyon Jia, Matthew Luzum

The ATLAS collaboration has recently observed that the variance of the transverse momentum per particle ($[ p_t ]$), when measured as a function of the collision multiplicity ($N_{ch}$) in Pb+Pb collisions, decreases by a factor $2$ for the largest values of $N_{ch}$, corresponding to ultra-central collisions. We show that this phenomenon is naturally explai

Kung-Hsiang Huang, Mingyang Zhou, Hou Pong Chan, Yi R. Fung

Recent advancements in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual content and thus enhancing various applications. One issue with these powerful models is that they sometimes produce texts that are factually inconsistent with the visual input. While there has been some effort t

Priyanka Nagar, Andriette Bekker, Mohammad Arashi, Cor-Jacques Kat

Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases the circular and linear variables are (i) considered in isolation with dependency between variables neglected and (ii) the cyclicity of the circular variables disregarded resulting in erroneous decision making. Given th

Thiago Ferreira, Roberto K. Saito, Dante Minniti, Andrea Mejías

We present the discovery and multi-wavelength characterisation of VVV J1438-6158 AB, a new field wide-binary system consisting of a 4.6(+5.5-2.4) Gyr and Teff = 9500+/-125 K DA white dwarf (WD) and a Teff = 2400+/-50 K M8 ultracool dwarf (UCD). The projected separation of the system is a = 1236.73 au (~13.8"), and although along the line-of-sight towards the

Jonathan Folkerts, Nick Solomey, Brooks Hartsock, Tyler Nolan

Gallium solar neutrino experiments have historically used radiochemical counting to determine the event rate. A detector which directly measures the ejected electron and de-excitation gamma could reduce background counting rates by way of a double-pulse technique. We find this reduction could be as large as 10 orders of magnitude in a 100 ton detector. In th

David Gross, Dominik Hangleiter

Quantum computing devices can now perform sampling tasks which, according to complexity-theoretic and numerical evidence, are beyond the reach of classical computers. This raises the question of how one can efficiently verify that a quantum computer operating in this regime works as intended. In 2008, Shepherd and Bremner proposed a protocol in which a verif

Feng Han, Jingang Yi

External and internal convertible (EIC) form-based motion control is one of the effective designs of simultaneously trajectory tracking and balance for underactuated balance robots. Under certain conditions, the EIC-based control design however leads to uncontrolled robot motion. We present a Gaussian process (GP)-based data-driven learning control for under

Olivia Elias, Ian Farish, Emrys King, Josh Kyei

We introduce $\ell$-leaky positive semidefinite forcing and the $\ell$-leaky positive semidefinite number of a graph, $Z_{(\ell)}^+{G}$, which combines the positive semidefinite color change rule with the addition of leaks to the graph. Furthermore, we determine general properties of $Z_{(\ell)}^+{G}$ and $Z_{(\ell)}^+{G}$ for various graphs, including path