November 2024 arXiv papers — page 26

Zhenyu Yu, Jinnian Wang, Mohd Yamani Idna Idris

The forest serves as the most significant terrestrial carbon stock mechanism, effectively reducing atmospheric CO2 concentrations and mitigating climate change. Remote sensing provides high data accuracy and enables large-scale observations. Optical images facilitate long-term monitoring, which is crucial for future carbon stock estimation studies. This stud

Jason Bell, Lucas Buzaglo

Let $\Bbbk$ be a field of characteristic zero. Motivated by the fundamental question of whether it is possible for the universal enveloping algebra of an infinite-dimensional Lie algebra to be noetherian, we study Lie algebras of derivations of associative algebras. The main result of this paper is that the universal enveloping algebra of the Lie algebra of

Vorashil Farzaliyev

In this paper, our main focus is expressing real numbers on the non-integer bases. We denote those bases as $\beta$'s, which is also a real number and $\beta \in (1,2)$. This project has 3 main parts. The study of expansions of real numbers in such bases and algorithms for generating them will contribute to the first part of the paper. In this part, firstly,

Seungyeon Kim, Wheesung Lee, Sung-Ho Ahn, Do-Eun Lee

Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) to predict blood flow and pressure in pre

Y. -C. Hu, J. Tian

Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be quantitatively characterized at the atomic scale. This greatly deepens our understanding of Nature and provides incredible ins

Yi-Han Zhao, Ning-Chen Sun, Junjie Wu, Zexi Niu

To date, SN 2017ein is the only Type Ic supernova with a directly identified progenitor candidate. This candidate points to a very massive ($>$45 $M_\odot$) Wolf-Rayet progenitor, but its disappearance after the explosion of SN 2017ein remains unconfirmed. In this work, we revisit SN 2017ein in late-time images acquired by the Hubble Space Telescope (HST) at

Spencer P. Alliston, Chris Dames, Matthew J. Powell-Palm

Predictive synthesis of aqueous organic solutions with desired liquid-solid phase equilibria could drive progress in industrial chemistry, cryopreservation, and beyond, but is limited by the predictive power of current solution thermodynamics models. In particular, few analytical models enable accurate liquidus and eutectic prediction based only on bulk ther

Guangli Li, Canbiao Wu, Zhehao Zhou, Tuo Sun

Electroencephalogram (EEG) signals serve as a powerful tool in affective Brain-Computer Interfaces (aBCIs) and play a crucial role in affective computing. In recent years, the introduction of deep learning techniques has significantly advanced the development of aBCIs. However, the current emotion recognition methods based on deep transfer learning face the

Ramez Kouzy, Roxanna Attar-Olyaee, Michael K. Rooney, Comron J. Hassanzadeh

Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an adapted framework from QuaLLM into QuaLLM-Health for extracting clinically relevant quantitative data from Reddit discussions about glucagon-like peptide-1 (GLP-1) receptor agonist

Chengyue Wang, Xiaofan Zhang, Jason Cong, James C. Hoe

As AI systems grow increasingly specialized and complex, managing hardware heterogeneity becomes a pressing challenge. How can we efficiently coordinate and synchronize heterogeneous hardware resources to achieve high utilization? How can we minimize the friction of transitioning between diverse computation phases, reducing costly stalls from initialization,

Shuli Jiang, Qiuyi, Zhang, Gauri Joshi

We study a classical problem in private prediction, the problem of computing an $(m\epsilon, \delta)$-differentially private majority of $K$ $(\epsilon, \Delta)$-differentially private algorithms for $1 \leq m \leq K$ and $1 > \delta \geq \Delta \geq 0$. Standard methods such as subsampling or randomized response are widely used, but do they provide optimal

Tsz Fung Heung, Marcel Franz

The notion of topological order (TO) can be defined through the characteristic ground state degeneracy of a system placed on a manifold with non-zero genus $g$, such as a torus. This ground state degeneracy has served as a key tool for identifying TOs in theoretical calculations but it has never been possible to probe experimentally because fabricating a dev

Nanyi Zheng, Daniel Hayes, Andrew Christlieb, Jing-Mei Qiu

High-order semi-Lagrangian methods for kinetic equations have been under rapid development in the past few decades. In this work, we propose a semi-Lagrangian adaptive rank (SLAR) integrator in the finite difference framework for linear advection and nonlinear Vlasov-Poisson systems without dimensional splitting. The proposed method leverages the semi-Lagran

Lingxiao Li, Kaixuan Fan, Boqing Gong, Xiangyu Yue

Few-shot image generation aims to generate diverse and high-quality images for an unseen class given only a few examples in that class. A key challenge in this task is balancing category consistency and image diversity, which often compete with each other. Moreover, existing methods offer limited control over the attributes of newly generated images. In this

Yutong Xie, Yijun Pan, Hua Xu, Qiaozhu Mei

Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI methods in driving broad scientific discovery. Existing efforts in identifying and bridging this gap have often re

Wei-Lin Wu, Shi-Lin Zhu

We conduct the first comprehensive P-wave four-body dynamical calculations of the fully charmed tetraquark systems within the quark potential model. We apply the Gaussian expansion method to solve the four-body Schr\"odinger equation, incorporating both dimeson and diquark-antidiquark spatial configurations. The matrix elements of P-wave states are calculate

Xiaojie Yu, Haibo Zhang, Lizhi Peng, Fengyang Sun

ReduNet is a deep neural network model that leverages the principle of maximal coding rate \textbf{redu}ction to transform original data samples into a low-dimensional, linear discriminative feature representation. Unlike traditional deep learning frameworks, ReduNet constructs its parameters explicitly layer by layer, with each layer's parameters derived ba

Xinyu Shi, Dina Ali Abdelhamid, Thomas Ilsche, Saeideh Alinezhad Chamazcoti

Main memory's rising energy consumption has emerged as a critical challenge in modern computing architectures, particularly in large-scale systems, driven by frequent access patterns, growing data volumes, and insufficient power management strategies. Accurate modeling of DRAM power consumption is essential to address this challenge and optimize energy effic

Tian Ye, Rajgopal Kannan, Viktor Prasanna

Adversarial training has emerged as an effective approach to train robust neural network models that are resistant to adversarial attacks, even in low-label regimes where labeled data is scarce. In this paper, we introduce a novel semi-supervised adversarial training approach that enhances both robustness and natural accuracy by generating effective adversar

Asma Enayet, John Heidemann

Measuring Internet outages is important to allow ISPs to improve their services, users to choose providers by reliability, and governments to understand the reliability of their infrastructure. Today's active outage detection provides good accuracy with tight temporal and spatial precision (around 10 minutes and IPv4 /24 blocks), but cannot see behind firewa

Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batches. To address these challenges, we introduce DiffVax, a sc

Yuheng Liang, Sedigheh Abedini, Nona Farbehi, Hamid Alinejad-Rokny

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent structural alterations that influence genomic diversity and disease susceptibility. While coding region variants have been extensively studied, non-coding and regulatory variants present significant challenges du

Guo-Li Wang, Tai-Fu Feng, Ya-Qian Wang

In this article, {we solve the instantaneous Bethe-Salpeter equation with Cornell potential and Coulomb potential} and conduct a meticulous study of the mass spectrum and wave function of toponium. Our investigation reveals that, owing to the exceedingly heavy mass of the top quark, the mass splitting between singlet and triplet states, as well as within the

Afnan A. Aldosri, Michael H. Meylan, Ben Wilks

We consider the scattering of linear waves in two dimensions by a rectangular region at the junction of four waveguides. A solution to the frequency domain problem is obtained by exploiting reflective symmetry to reduce the full problem to sub-problems defined on one quadrant of the junction. These sub-problems are solved using the eigenfunction matching met

Alireza F. Behbahani, Friederike Schmid

We present an extensive analysis of the relaxation dynamics of entangled linear polymer melts via long-time molecular dynamics simulations of a generic bead-spring model. We study the mean-squared displacements, the autocorrelation function of the end-to-end vector, $P(t)$, the single-chain dynamic structure factor, $S(q,t)$, and the linear viscoelastic prop

Krishna Shende, Kavita Dorai, Arvind

The work done when a system at thermal equilibrium is externally driven by a unitary control parameter leads to irreversible entropy production. The entropy produced can be thought of as a combination of coherence generation and a population mismatch between the target equilibrium state and the actually achieved final state. We experimentally explored this o

Mohammad Zubair Khan, David Li

This paper presents a novel approach to binary classification using dynamic logistic ensemble models. The proposed method addresses the challenges posed by datasets containing inherent internal clusters that lack explicit feature-based separations. By extending traditional logistic regression, we develop an algorithm that automatically partitions the dataset

He Zhang, Haibo Chen, Shuai Yao, Juntao Sun

We investigate normalized solutions for a class of nonlinear Schr\"{o}dinger (NLS) equations with potential $V$ and inhomogeneous nonlinearity $g(|u|)u=|u|^{q-2}u+\beta |u|^{p-2}u$ on a bounded domain $\Omega$. Firstly, when $2+\frac{4}{N}<q<p\leq2^*:=\frac{2N}{N-2}$ and $\beta=-1$, under an explicit smallness assumption on $V$, we prove the existence of a g

Shreyas Malpathak, Sangeeth Das Kallullathil, Artur F. Izmaylov

Bosonic quantum devices, which utilize harmonic oscillator modes to encode information, are emerging as a promising alternative to conventional qubit-based quantum devices, especially for the simulation of vibrational dynamics and spectroscopy. We present a framework for digital quantum simulation of vibrational dynamics under anharmonic potentials on these

Yuchao Gu, Yipin Zhou, Yunfan Ye, Yixin Nie

Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances. To address this limitation, this work enhances diffusion models by introducing regional instance control, where each in

Prathapasinghe Dharmawansa, Saman Atapattu, Jamie Evans, Merouane Debbah

This paper investigates the signal detection problem in colored noise with an unknown covariance matrix. In particular, we focus on detecting a non-random signal by capitalizing on the leading eigenvalue (a.k.a. Roy's largest root) of the whitened sample covariance matrix as the test statistic. To this end, the whitened sample covariance matrix is constructe

Zhuolin Qu, Tong Wu

Wolbachia is a naturally occurring bacterium that can infect Aedes mosquitoes and reduce the transmission of mosquito-borne diseases, including dengue fever, Zika, and chikungunya. Field trials have been conducted worldwide to suppress local epidemics. We introduce a novel partial differential equation model to simulate the spread of Wolbachia infection in m

Arushi Arora, David Eppstein, Randy Le Huynh

The Schulze voting method aggregates voter preference data using maxmin-weight graph paths, achieving the Condorcet property that a candidate who would win every head-to-head contest will also win the overall election. Once the voter preferences among $m$ candidates have been arranged into an $m\times m$ matrix of pairwise election outcomes, a previous algor

Alessandro Ciattoni

We develop a general approach to describe the scattering of quantum light by a lossy macroscopic object placed in vacuum with no restrictions on both its dispersive optical response and its spatially inhomogeneous composition. Our analysis is based on the modified Langevin noise formalism, a recently introduced version of macroscopic quantum electrodynamics

Shivam Pal, Aishwarya Gupta, Saqib Sarwar, Piyush Rai

Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement of data ever leaving the clients. Recent works on FL have advocated taking a Bayesian approach to FL as it offers a principled way to account for the model and predictive uncertain

Jitender Singh

In this article, we obtain upper bounds on the number of irreducible factors of some classes of polynomials having integer coefficients, which in particular yield some of the well known irreducibility criteria. For devising our results, we use the information about prime factorization of the values taken by such polynomials at sufficiently large integer argu

Dipayan Chakraborty, Florent Foucaud, Diptapriyo Majumdar, Prafullkumar Tale

We investigate structural parameterizations of two identification problems: LOCATING-DOMINATING SET and TEST COVER. In the first problem, an input is a graph $G$ on $n$ vertices and an integer $k$, and one asks if there is a subset $S$ of $k$ vertices such that any two distinct vertices not in $S$ are dominated by distinct subsets of $S$. In the second probl

José Reina-Valero, Alejandro Díaz-Morcillo, José Gadea-Rodríguez, Benito Gimeno

We present the first analysis of Dark Matter axion detection applying neural networks for the improvement of sensitivity. The main sources of thermal noise from a typical read-out chain are simulated, constituted by resonant and amplifier noises. With this purpose, an advanced modal method employed in electromagnetic modal analysis for the design of complex

Sankalp Sinha, Mohammad Sadil Khan, Muhammad Usama, Shino Sam

Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 million 3D assets aggregated from seven major 3D datasets. Our

Sergei Shmakov, Glasha Osipycheva, Peter B. Littlewood

Non-reciprocal systems can be thought of as disobeying Newtons third law - an action does not cause an equal and opposite reaction. In recent years there has been a dramatic rise in interest towards such systems. On a fundamental level, they can be a basis of describing non-equilibrium and active states of matter, with applications ranging from physics to so

Saman Sarraf

Generative AI (GenAI) has revolutionized content generation, offering transformative capabilities for improving language coherence, readability, and overall quality. This manuscript explores the application of qualitative, quantitative, and mixed-methods research approaches to evaluate the performance of GenAI models in enhancing scientific writing. Using a

Yash Kumar, Raghu Bollapragada, Benjamin D. Leibowicz

Optimal camera placement plays a crucial role in applications such as surveillance, environmental monitoring, and infrastructure inspection. Even highly abstracted versions of this problem are NP-hard due to the high-dimensional continuous domain of camera configurations (i.e., positions and orientations) and difficulties in efficiently and accurately calcul

Yuanyuan Qi, Jueqing Lu, Xiaohao Yang, Joanne Enticott

The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while also accounting for the inherited label correlation. Existing studies either require substantial computational resources to leverage correlations or fail to fully explore label depe

Alex Elzenaar

Homeomorphism types of compression bodies form the vertices of a graph where two vertices are joined by an edge if one compression body is obtained by gluing a $2$-handle onto the other. Motivated by earlier work of Lackenby and Purcell on geodesicity of unknotting tunnels for hyperbolic links, we show that it is possible to realise all of the edges in the g

Ryan Marin

In this work we extend the proof of Ryan Unger and Christoph Kehle's work, "Gravitational collapse to extremal black holes and the third law of black hole thermodynamics", to construct examples of black hole formation from regular, one-ended asymptotically flat Cauchy data for the Einstein-Maxwell charged scalar field system in maximally-symmetric 3+1 dimens

Shu Wan, Reepal Shah, Qi Deng, John Sabo

Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establishing connections across multiple physical processes, such as rainfall and runoff. These data, inherently connected both spatially and temporally, possess intrinsic causal relation

Jadelynn Dao, Ruixiao Liu, Sarah Solomon, Samuel Solomon

Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety, a transient emotional response, linked to adverse cardiovascular and long-term health outcomes. This research explores the potential of non-invasive wearable technology to enhance the real-time monitoring of physiological responses associated with st

V. Gupta, G. R. Araujo, M. Babicz, L. Baudis

Liquid argon detectors rely on wavelength shifters for efficient detection of scintillation light. The current standard is tetraphenyl butadiene (TPB), but it is challenging to instrument on a large scale. Poly(ethylene 2,6-naphthalate) (PEN), a polyester easily manufactured as thin sheets, could simplify the coverage of large surfaces with wavelength shifte

Benyamin Beyzaei, Saghar Talebipour, Ghazal Rafiei, Nenad Medvidovic

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating and maintaining such tests can be costly for developers. Since many apps share similar functionalities beneath diverse UIs

Alan Oursland

We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of internal activations in trained models, finding that both architectures are highly sensitive to small distance-based perturbations while maintaining robust perf

Jubin Abhishek Soni, Amit Anand, Rajesh Kumar Pandey, Aniket Abhishek Soni

While the Web has become a global platform for communication, malicious actors, including hackers and hacktivist groups, often disseminate ideological content and coordinate activities through the "Dark Web", an obscure counterpart of the conventional web. Presently, challenges such as information overload and the fragmented nature of cyber threat data imped

Pietro Corvaja, Davide Lombardo, Umberto Zannier

We consider families of smooth projective curves of genus 2 with a single point removed and study their integral points. We show that in many such families there is a dense set of fibres for which the integral points can be effectively determined. Our method is based on the construction of degree-3 \'etale covers of such curves of genus 2 and the study of th

Xiaoran Liu

We consider the Oberbeck-Boussinesq system with gravitational force on the whole space. We prove the non-uniqueness of the system applying the unstable profile of Navier-Stokes equation.

Antonio Andrea Gargiulo, Donato Crisostomi, Maria Sofia Bucarelli, Simone Scardapane

Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is susceptible to task interference. In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular v

Xiangcheng Hu, Jin Wu, Mingkai Jia, Hongyu Yan

Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluation frameworks. We present MapEval, an open-source framework for comprehensive quality assessment of point cloud maps, specifically addressing SLAM scenarios where ground truth map

Xiao Lin, Mingjie Li, Yisen Wang

Graph Neural Networks (GNNs) have garnered significant attention from researchers due to their outstanding performance in handling graph-related tasks, such as social network analysis, protein design, and so on. Despite their widespread application, recent research has demonstrated that GNNs are vulnerable to backdoor attacks, implemented by injecting trigge

Conor O'Brien, Daniel Rodriguez-Cardenas, Alejandro Velasco, David N. Palacio

The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exhibit behaviors not observed in their smaller counterparts. Understanding these emergent capabilities is essential for advancing LLM development and improving their interpretability

Oliver Biggar, Iman Shames

We propose a design for a functional programming language for autonomous agents, built off the ideas and motivations of Behavior Trees (BTs). BTs are a popular model for designing agents behavior in robotics and AI. However, as their growth has increased dramatically, the simple model of BTs has come to be limiting. There is a growing push to increase the fu

Rémi Garcia, Paolo Modesti

To develop trustworthy distributed systems, verification techniques and formal methods, including lightweight and practical approaches, have been employed to certify the design or implementation of security protocols. Lightweight formal methods offer a more accessible alternative to traditional fully formalised techniques by focusing on simplified models and

Felix M. Lev

As shown in our publications, quantum theory based on a finite ring of characteristic $p$ (FQT) is more general than standard quantum theory (SQT) because the latter is a degenerate case of the former in the formal limit $p\to\infty$. One of the main differences between SQT and FQT is the following. In SQT, elementary objects are described by irreducible rep

Abhiram Gorle

Imagine a group of oscillators, each endowed with their own rhythm or frequency, be it the ticking of a biological clock, the swing of a pendulum, or the glowing of fireflies. While these individual oscillators may seem independent of one another at first glance, the true magic lies in their ability to influence and synchronize with one another, like a group

Daniel Weitekamp, Erik Harpstead, Kenneth Koedinger

AI2T is an interactively teachable AI for authoring intelligent tutoring systems (ITSs). Authors tutor AI2T by providing a few step-by-step solutions and then grading AI2T's own problem-solving attempts. From just 20-30 minutes of interactive training, AI2T can induce robust rules for step-by-step solution tracking (i.e., model-tracing). As AI2T learns it ca

Dare Bartelt, Megan Weiner Mansfield, Michael R. Line, Vivien Parmentier

Measuring the abundances of carbon- and oxygen-bearing molecules has been a primary focus in studying the atmospheres of hot Jupiters, as doing so can help constrain the carbon-to-oxygen (C/O) ratio. The C/O ratio can help reveal the evolution and formation pathways of hot Jupiters and provide a strong understanding of the atmospheric composition. In the las

Joseph Bramante, Katherine Mack, Nirmal Raj, Lijing Shao

Neutron stars provide a compelling testing ground for gravity, nuclear dynamics, and physics beyond the Standard Model, and so it will be useful to locate the neutron stars nearest to Earth. To that end, we revisit pulsar distance estimates extracted from the dispersion measure of pulsar radio waves scattering on electrons. In particular, we create a new ele

Hugo Resende, Isabela Borlido, Victor Sundermann, Eduardo B. Neto

Tropical forests play an essential role in the planet's ecosystem, making the conservation of these biomes a worldwide priority. However, ongoing deforestation and degradation pose a significant threat to their existence, necessitating effective monitoring and the proposal of actions to mitigate the damage caused by these processes. In this regard, initiativ

Alejandro Hnilo

Several arguments demonstrate the incompatibility between Quantum Mechanics and classical Physics. Bell's inequalities and Greenberger-Horne-Zeilinger (GHZ) arguments apply to specific non-classical states. The Kochen-Specker (KS) one, instead, is especially appealing for it applies to any state. Nevertheless, in spite of the incompatibility, quantum predict

Wouter Meulemans, Arjen Simons, Kevin Verbeek

We study the visual complexity of animated transitions between point sets. Although there exist many metrics for point set similarity, these metrics are not adequate for this purpose, as they typically treat each point separately. Instead, we propose to look at translations of entire subsets/groups of points to measure the visual complexity of a transition b

Kashimul Hossain

Perovskite solar cells (PSCs) are the fastest-growing photovoltaic (PV) technology in the solar cell community and have reached an efficiency close to that of commercial silicon (Si) solar cells. The organic-inorganic halide perovskite solar cell is an emerging PV technology and grabbed much attention due to its low cost, high efficiency, and ease of fabrica

Raimundo Bastos, Luis Mendonça

A group element is called generalized torsion if a finite product of its conjugates is equal to the identity. We show that in a finitely generated abelian-by-finite group, an element is generalized torsion if and only if its image in the abelianization is torsion. We also prove a quantitative version with sharp bounds to the generalized exponent of these gro

Ketevan Kotorashvili, Eric G. Blackman

Very low-mass main-sequence stars reveal some curious trends in observed rotation period distributions that require abating the spin-down that standard rotational evolution models would otherwise imply. By dynamically coupling magnetically mediated spin-down to tidally induced spin-up from close orbiting substellar companions, we show that tides from sub-ste

Riddho R. Haque, Anh L. Mai, Matteo Brucato, Azza Abouzied

Decision making under uncertainty often requires choosing packages, or bags of tuples, that collectively optimize expected outcomes while limiting risks. Processing Stochastic Package Queries (SPQs) involves solving very large optimization problems on uncertain data. Monte Carlo methods create numerous scenarios, or sample realizations of the stochastic attr

Soheila Sadeghi

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project perfo

Vincent Capol, Yuxi Liu, Haibo Xiu, Jun Yang

Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data generators often fail to capture the complexity and unpredictable nature of real data, while most real-worl

Wanyi Chen, Meng-Wen Su, Nafisa Mehjabin, Mary L. Cummings

As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planning scenarios in various settings and with various difficulties. Our experiments showed that all LLMs made numerous errors in all scenarios, revealing that they

Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen, Nhien-An Le-Khac

In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists style imitation, raising questions about ethical and security concerns. In this survey, we provide a comprehensive review and comparison of passive DF detection across multiple modalities, including image, video,

Youngho Bae, Chanmin Kim, Fenglei Wang, Qi Sun

In epidemiological research, causal models incorporating potential mediators along a pathway are crucial for understanding how exposures influence health outcomes. This work is motivated by integrated epidemiological and blood biomarker studies, investigating the relationship between long-term adherence to a Mediterranean diet and cardiometabolic health, wit

Tai-Hsuan Chung

We formulate a stable reduction conjecture that extends Deligne-Mumford's stable reduction to higher dimensions and provide a simple proof that it holds in large characteristic, assuming two standard conjectures of the Minimal Model Program. As a result, we recover the Hacon-Kov\'acs theorem on the properness of the moduli stack $\overline{\mathscr{M}}_{2,v,

Allen Lobo, Vinod Kumar Sayal

In this work, coalescence of phase-space holes of collision-less, one-dimensional plasmas is studied using kinetic simulation techniques. Phase-space holes are well-known Bernstein-Greene-Kruskal waves known for exhibiting coalescence, are numerically simulated and their coalescence is observed. Relations between the hole speed, potential, phase-space vortic

Denis Ilin, Alexander S. Solntsev, Ivan Iorsh

We investigate the impact of the quantized mechanical motion of optically trapped atoms, arranged in proximity to a one-dimensional waveguide, on the propagation of polariton modes. Our study identifies a regime of resonant phonon-assisted mixing between lower and upper polaritons, resulting in a pronounced polaron effect. This effect is characterized by the

E. Ballarin, D. A. Chisholm, A. Smirne, M. Paternostro

We model and study the processes of excitation, absorption, and transfer in various networks. The model consists of a harmonic oscillator representing a single-mode radiation field, a qubit acting as an antenna, a network through which the excitation propagates, and a qubit at the end serving as a sink. We investigate how off-resonant excitations can be opti

Raju S. Khatiwada, N. P. Adhikari, C. Ortiz

This study examines dissipative forces in photon-medium interactions through time-independent perturbation theory, with a specific focus on single Helium-4 atoms. Utilizing a Hamiltonian framework, energy corrections induced by dissipative gravitational frictional effects in low-density systems are derived and analyzed as a function of inter-atomic distance.

Maria Vasilyeva, Ben S. Southworth, Shubin Fu

We consider a nonlinear mixed-dimensional model for simulating gas transport in shale formation. The mathematical model consists of a coupled system of nonlinear equations, where flow within fractures is represented using a lower-dimensional representation. For the numerical solution of the coupled transport problem, we construct an unstructured mesh that re

Tyler S. Wilson, Wil Thomason, Zachary Kingston, Lydia E. Kavraki

Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations are local motion validation and querying the nearest neighbour

Chloe Herman, Evan Bolyen, Anthony Simard, Liz Gehret

We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Chimeric Asymmetric Community Coalescence,

Suyeol Yun

Developing effective quantitative trading strategies using reinforcement learning (RL) is challenging due to the high risks associated with online interaction with live financial markets. Consequently, offline RL, which leverages historical market data without additional exploration, becomes essential. However, existing offline RL methods often struggle to c

Marina Martinelli, André Tosi Furtado

This paper is a comprehensive exploring of technology capability in 5G/6G TIS, explicitly focusing on the potential of remote surgery globally and in Germany. The paper's main contribution is its ability to anticipate new debates on the interplay between TIS and contexts, with particular emphasis on the national and international levels. Our findings, derive

Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit

Classic supervised learning involves algorithms trained on $n$ labeled examples to produce a hypothesis $h \in \mathcal{H}$ aimed at performing well on unseen examples. Meta-learning extends this by training across $n$ tasks, with $m$ examples per task, producing a hypothesis class $\mathcal{H}$ within some meta-class $\mathbb{H}$. This setting applies to ma

Muhammad Waseem Akram, Marco Vannucci, Giorgio Buttazzo, Valentina Colla

The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obta

Konstantinos Patsalos, Christos Saroglou

The first goal of this paper is to improve some of the results in \cite{BCPR}. Namely, we establish the $L_p$-Brunn-Minkwoski inequality for intrinsic volumes for origin-symmetric convex bodies that are close to the ball in the $C^2$ sense for a certain range of $p<1$ (including negative values) and we prove that this inequality does not hold true in the ent

Hui Hu, Jia Wang, Xia-Ji Liu

We calculate the exact spectral function of a single impurity repulsively interacting with a bath of fermions in one-dimensional lattices, by deriving the explicit expression of the form factor for both regular Bethe states and the irregular spin-flip state and $\eta$-pairing state, based on the exactly solvable Lieb-Wu model. While at low impurity momentum

Christophe Ponsard, Bérengère Nihoul, Mounir Touzani

Designing sustainable systems involves complex interactions between environmental resources, social impacts, and economic issues. In a constrained world, the challenge is to achieve a balanced design across those dimensions while avoiding several barriers to adoption. This paper explores the concept of fairness in sociotechnical system design, including its

Simon Maenaut, Gregorio Carullo, Pablo A. Cano, Anna Liu

Quasinormal modes of rapidly rotating black holes were recently computed in a generic effective-field-theory extension of general relativity with higher-derivative corrections. We exploit this breakthrough to perform the most complete search for signatures of new physics in black hole spectra to date. We construct a template that describes the post-merger gr

Kalina P. Slavkova, Melanie Traughber, Oliver Chen, Robert Bakos

Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but ther

Bryce Morrow

In this paper, we present a concise development of the well-studied theory of trace class operators on infinite dimensional (separable) Hilbert spaces suitable for an advanced undergraduate, as well as a construction of the inverse Laplacian on closed manifolds. With these developments acting as prerequisite, we present original trace computations involving

Adam Bartoš, Wiesław Kubiś

We study the existence of uncountable first-order structures that are homogeneous with respect to their finitely generated substructures. In many classical cases this is either well-known or follows from general facts, for example, if the language is finite and relational then ultrapowers provide arbitrarily large such sturctures. On the other hand, there ar

Christopher Harvey-Hawes, Marco Galoppo

Disc galaxies represent a promising laboratory for the study of gravitational physics, including alternatives to dark matter, owing to the possibility of coupling rotation curves' dynamical data with strong gravitational lensing observations. In particular, Euclid, DES and LSST are predicted to observe hundreds of thousands of gravitational lenses. Here, we

Anastasios Zouzias, William F. McColl

We present a parallel scan (prefix sum) algorithm in the Tensor Core Unit (TCU) model of computation. The TCU model assumes that multiplication between two square matrices of constant size $s$ is a basic operation. In the $(s^2, \ell)$-TCU model, we show that for inputs of size $n$, the algorithm has depth at most $2\lfloor \log_s (n)\rfloor$ and runs in $O(

Meng Wang, Zach Noonan, Pnina Gershon, Bruce Mehler

Understanding the context of crash occurrence in complex driving environments is essential for improving traffic safety and advancing automated driving. Previous studies have used statistical models and deep learning to predict crashes based on semantic, contextual, or vehicle kinematic features, but none have examined the combined influence of these factors

F. Botler, Y. S. Couto, C. G. Fernandes, E. F. de Figueiredo

Chernyshev, Rauch, and Rautenbach proved that every connected graph on $n$ vertices with less than $\frac{11}{5}n-\frac{18}{5}$ edges has a vertex cut that induces a forest, and conjectured that the same remains true if the graph has less than $3n-6$ edges. We improve their result by proving that every connected graph on $n$ vertices with less than $\frac{9}

Tejahni Desire, Alejandro Cárdenas-Avendaño, Andrew Chael

Images of black holes encode both astrophysical and gravitational properties. Detecting highly-lensed features in images can differentiate between these two effects. We present an accretion disk emission model coupled to the Adaptive Analytical Ray Tracing (AART) code that allows a fast parameter space exploration of black hole photon ring images produced fr