December 2024 arXiv papers — page 146

L. D. Tamang, S. Gurung, R. Zosiamliana, L. Celestine

Hydrogen is considered an alternative source of energy to fossil fuels for the fulfilment of current energy demands. Photocatalysis initiates the hydrogen evolution reaction which is believed to be the greenest approach to produce hydrogen through clean, safe, and environmentally friendly methods. In this Review, we focus mainly on the comprehensive analysis

S M Abdullah Al Mamun, Samaneh Farokhirad

We numerically investigate the collision dynamics and deformation behaviors of double-core compound droplet pairs within confined shear flows using free-energy-based lattice Boltzmann method. While significant research has advanced our understanding of simple droplet pair interactions, the collision behaviors of core-shell compound droplets, where each shell

Zijiang Yang

Natural Language Inference (NLI) is the task of inferring whether the hypothesis can be justified by the given premise. Basically, we classify the hypothesis into three labels(entailment, neutrality and contradiction) given the premise. NLI was well studied by the previous researchers. A number of models, especially the transformer based ones, have achieved

Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu

We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user through a sequence of prompt expansions. Using human raters, we create a novel dataset of sequential preferences, which we leverage, together with large-scale open-source (non-sequ

Zihan Yan

Previously, the Raychaudhuri equation and the focusing theorem in General Relativity were generalised to diffeomorphism-invariant theories of gravity coupled to scalar and vector fields on linearly perturbed Killing horizons. The Wall entropy can be extracted from the generalised focusing equation and it satisfies the first and the second laws of thermodynam

Antonis Vasileiou, Ben Finkelshtein, Floris Geerts, Ron Levie

The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities -- making meaningful predictions beyond the training set -- remain less explored. Current generalization analyses often overlook graph structur

Yansong Xu, Xiaohui Wang, Junlin Li, Xiaoqian Zhang

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestures and executing anthropomorphic grasp p

R. Sengar, M. Bailes, V. Balakrishnan, E. D. Barr

We have conducted a GPU accelerated reprocessing of $\sim 87\%$ of the archival data from the High Time Resolution Universe South Low Latitude (HTRU-S LowLat) pulsar survey by implementing a pulsar search pipeline that was previously used to reprocess the Parkes Multibeam pulsar survey (PMPS). We coherently searched the full 72-min observations of the survey

K. Liu, A. Parthasarathy, M. Keith, C. Tiburzi

Astrometry of pulsars, particularly their distances, serves as a critical input for various astrophysical experiments using pulsars. Pulsar timing is a primary approach for determining a pulsar's position, parallax, and distance. In this paper, we explore the influence of the solar wind on astrometric measurements obtained through pulsar timing, focusing on

Shaobing Gao, Yongjie Li

Color constancy (CC) is an important ability of the human visual system to stably perceive the colors of objects despite considerable changes in the color of the light illuminating them. While increasing evidence from the field of neuroscience supports that multiple levels of the visual system contribute to the realization of CC, how the primary visual corte

Xiao-Lin Li, Ming Gong, Yu-Hao Wang, Li-Chen Zhao

Topological charges are typically manipulated by managing their energy bands in quantum systems. In this work, we propose a new approach to manipulate the topological charges of systems by engineering density zeros of localized wave excitations in them. We demonstrate via numerical simulation and analytical analysis that the winding number of a toroidal Bose

Reza Hadadi

This paper provides a systematic exposition of Lyapunov stability for compact sets in locally compact metric spaces. We explore foundational concepts, including neighborhoods of compact sets, invariant sets, and the properties of dynamical systems, and establish key results on the relationships between attraction, invariance, and stability. The work explores

Jungin E. Kim, Yan Wang

One of the challenging scientific computing problems is topology optimization, where searching through the combinatorially complex configurations and solving the constraints of partial differential equations need to be done simultaneously. In this paper, a novel variational quantum algorithm for constrained topology optimization is proposed, which allows for

Lucas Affonso, Rodrigo Bissacot, Henrique Corsini, Kelvyn Welsch

Following seminal work by J. Fr\"ohlich and T. Spencer on the critical exponent $\alpha=2$, we give a proof via contours of phase transition in the one-dimensional long-range ferromagnetic Ising model in the entire region of decay, where phase transition is known to occur, i.e., polynomial decay $\alpha \in (1,2]$. No assumptions that the nearest-neighbor in

Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang

Stakeholders -- from model developers to policymakers -- seek to minimize the dual-use risks of large language models (LLMs). An open challenge to this goal is whether technical safeguards can impede the misuse of LLMs, even when models are customizable via fine-tuning or when model weights are fully open. In response, several recent studies have proposed me

Shiyue Zhang, David Wan, Arie Cattan, Ayal Klein

How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking the reference summary into subunits and verifying their presence in the system summary, has been widely adopted. However, it suffers from a lack of systematicity in the definition an

Samuel Cure, Florian G. Pflug, Simone Pigolotti

Epidemic models on complex networks have been widely used to study how the social structure of a population affect the spreading of epidemics. However, their numerical simulation can be computationally heavy, especially for large networks. In this paper, we introduce NEXT-Net: a flexible implementation of the next reaction method for epidemic spreading on bo

Fanfei Xu, Shengheng Liu, Zihuan Mao, Shangqing Shi

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy

Joel Daniel Andersson, Rasmus Pagh

In differential privacy, $\textit{continual observation}$ refers to problems in which we wish to continuously release a function of a dataset that is revealed one element at a time. The challenge is to maintain a good approximation while keeping the combined output over all time steps differentially private. In the special case of $\textit{continual counting

David Bryant, Paul Tupper

Diversities are an extension of the concept of a metric space which assign a non-negative value to every finite set of points, rather than just pairs. A general theory of diversities has been developed which exhibits many deep analogies to metric space theory but also veers off in new directions. Just as many of the most important aspects of metric space the

Sebastian Hereu, Qianfei Hu

Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) with great success. DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. A common critique of the use of DCGANs in creative appli

Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligenc

Peter Frankl, Jian Wang

Given a family $\mathcal{F}\subset 2^{[n]}$ and $1\leq i\neq j\leq n$, we use $\mathcal{F}(\bar{i},j)$ to denote the family $\{F\setminus \{j\}\colon F\in \mathcal{F},\ F\cap \{i,j\}=\{j\}\}$. The sturdiness of $\mathcal{F}$ is defined as the minimum $|\mathcal{F}(\bar{i},j)|$ over all $i,j\in [n]$ with $i\neq j$. It has a very natural algebraic definition a

Weixiang Sun, Han Shen, Biwei Jiang, Xiaowei Liu

Using nearly 230,000 red clump (RC) stars selected from LAMOST and Gaia, we conduct a comprehensive analysis of the stellar age-velocity dispersion relations (AVRs) for various disk populations, within 5.0 $\leq$ $R$ $\leq$ 15.0 kpc and $|Z|$ $\leq$ 3.0 kpc. The AVRs of the whole RC sample stars are accurately described as $\sigma_{v}$ = $\sigma_{v,0}$ ($\ta

Shengheng Liu, Tianqi Zhang, Ningning Fu, Yongming Huang

AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facil

Benjamin Siegel, Gadi Afek, Cecily Lowe, Jiaxiang Wang

Levitated optomechanical systems are rapidly becoming leading tools for precision sensing of forces and accelerations acting on particles in the femtogram to nanogram mass range. These systems enable a high level of control over the sensor's center-of-mass motion, rotational degrees of freedom, and electric charge state. For many sensing applications, extend

Keita Ikeda, Yiyang Chen, Peng Wang, Yoshiyuki Miyamoto

Solid-state quantum emitters are an important platform for quantum information processing. The fabrication of the emitters with stable photon frequency and narrow linewidth is a fundamental issue, and it is essential to understand optical conditions under which the emitter keeps a bright charge state or transitions to a dark state. For these purposes, in thi

Torben Amtoft, Anindya Banerjee

When proving the correctness of a method for slicing probabilistic programs, it was previously discovered by the authors that for a fixed point iteration to work one needs a non-standard starting point for the iteration. This paper presents and explores this technique in a general setting; it states the lemmas that must be established to use the technique to

Harini Hapuarachchi, Jesse A. Vaitkus, Jared H. Cole

When a quantum emitter (QE) is placed in close proximity to a plasmonic metal nanoparticle (MNP) within an external optical field, a dipole-dipole coupling arises, resulting in a highly tunable hybrid nanosystem that surpasses the optical manipulation capabilities of the individual components. These hybrid systems enable the exploration and manipulation of o

Peter Lalor, Areg Danagoulian

To combat the risk of nuclear smuggling, radiography systems are deployed at ports to scan cargo containers for concealed illicit materials. Dual energy radiography systems enable a rough elemental analysis of cargo containers due to the Z-dependence of photon attenuation, allowing for improved material detection. This work presents our initial experimental

Kazuhiro Hada, Keiichi Asada, Masanori Nakamura, Motoki Kino

Over the past decades, there has been significant progress in our understanding of accreting supermassive black holes (SMBHs) that drive active galactic nuclei (AGNs), both from observational and theoretical perspectives. As an iconic target for this area of study, the nearby giant elliptical galaxy M87 has received special attention thanks to its proximity,

Olaoluwayimika Olugbenle, Logan Drake, Naveenkumar G. Venkataswamy, Arfina Rahman

We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate,

Junhua Chen, Lorenz Richter, Julius Berner, Denis Blessing

An effective approach for sampling from unnormalized densities is based on the idea of gradually transporting samples from an easy prior to the complicated target distribution. Two popular methods are (1) Sequential Monte Carlo (SMC), where the transport is performed through successive annealed densities via prescribed Markov chains and resampling steps, and

Qiang Qu, Xiaoming Chen, Yuk Ying Chung, Yiran Shen

Event-stream representation is the first step for many computer vision tasks using event cameras. It converts the asynchronous event-streams into a formatted structure so that conventional machine learning models can be applied easily. However, most of the state-of-the-art event-stream representations are manually designed and the quality of these representa

Qiang Qu, Xiaoming Chen, Vera Chung, Zhibo Chen

In multimedia broadcasting, no-reference image quality assessment (NR-IQA) is used to indicate the user-perceived quality of experience (QoE) and to support intelligent data transmission while optimizing user experience. This paper proposes an improved no-reference light field image quality assessment (NR-LFIQA) metric for future immersive media broadcasting

Wangli Yang, Jie Yang, Yi Guo, Johan Barthelemy

The field of textual adversarial defenses has gained considerable attention in recent years due to the increasing vulnerability of natural language processing (NLP) models to adversarial attacks, which exploit subtle perturbations in input text to deceive models. This paper introduces the Defensive Dual Masking (DDM) algorithm, a novel approach designed to e

Donggeun Kim, Yujin Jo, Myungjoo Lee, Taesup Kim

The advancement of vision-language models, particularly the Contrastive Language-Image Pre-training (CLIP) model, has revolutionized the field of machine learning by enabling robust zero-shot learning capabilities. These capabilities allow models to understand and respond to previously unseen data without task-specific training. However, adapting CLIP to int

Marc Aaron F. Julian, Mark Lexter D. De Lara, Krizal John C. Espacio, Micko Jay S. Bajamundi

In this article, we defined a knotted subgroup of a Lie group and considered a geometric notion of equivalence among them. We characterized these knotted subgroups in terms of one-parameter subgroups and provided examples in the case of SU(2) and SU(3). Infinitesimal elements that give rise to knotted subgroups of SU(n) and SO(n) are characterized as well. C

Saud Alghumayjan, Ming Yi, Bolun Xu

This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and uncertainty of real-time electricity prices, which create substantial risks of financial losses for energy storage

Yiyan Ma, Bo Ai, Guoyu Ma, Akram Shafie

In this letter, we propose a novel channel transfer function (CTF) estimation approach for orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, that leverages the stationary properties of the delay-Doppler domain channel spreading function (CSF). First, we develop a CSF estimation model for OFDM systems that relies solely on

Opal Issan, Oleksandr Chapurin, Oleksandr Koshkarov, Gian Luca Delzanno

Kinetic simulations of collisionless plasmas are computationally challenging due to phase space mixing and filamentation, resulting in fine-scale velocity structures. This study compares three methods developed to reduce artifacts related to limited velocity resolution in Hermite-based Vlasov-Poisson simulations: artificial collisions, filtering, and nonloca

Akash Kumar, Sirshapan Mitra, Yogesh Singh Rawat

In this work, we focus on semi-supervised learning for video action detection. Video action detection requires spatiotemporal localization in addition to classification, and a limited amount of labels makes the model prone to unreliable predictions. We present Stable Mean Teacher, a simple end-to-end teacher-based framework that benefits from improved and te

Daryna Bukatova, Ivan O. Starodub, Yaroslav Zolotaryuk

In this paper theoretical analysis of the ladder-like multirow array of inductively coupled Josephson junctions is presented. An external dc current is applied at the top to each of the columns of the array and is extracted at the bottom of that column. The density of states of the Josephson plasma waves has a $\delta$-function term due to the flat band and

Benedetta Vulcani, Tommaso Treu, Matthew Malkan, Thomas S. -Y Lai

We measure the spectral properties of a sample of 20 galaxies at z~0.35 selected for having surprisingly red JWST/NIRCAM F200W-F444W colors. 19 galaxies were observed with JWST/NIRSpec in the PRISM configuration, while one galaxy was observed with the high resolution gratings. 17/20 galaxies in our sample exhibit strong 3.3 $\mu m$ polycyclic aromatic hydroc

Peter Lalor, Henry Adams, Alex Hagen

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In

Zachary Atkins, Zack Li, David Alonso, J. Richard Bond

The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complic

Yinsicheng Jiang, Yao Fu, Yeqi Huang, Ping Nie

The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory resources. These factors jointly affect system Cost, Accuracy, and Performance (CAP), making trade-offs inevitable. Existing benchmarks often fail to capture these trade-offs accurat

Peter Henderson, Mark A. Lemley

Artificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforceable tools for preventing misuse, particu

Hamza Afzaal, Usman Alim

Surface visualizations are essential in analyzing three-dimensional spatiotemporal phenomena. Given its ability to provide enhanced spatial perception and scene maneuverability, virtual reality (VR) is an essential medium for surface visualization and interaction tasks. Such tasks primarily rely on visual cues that require an unoccluded view of the surface r

John D. Gibbon, Dario Vincenzi

Shell models allow much greater scale separations than those presently achievable with direct numerical simulations of the Navier-Stokes equations. Consequently, they are an invaluable tool for testing new concepts and ideas in the theory of fully developed turbulence. They also successfully display energy cascades and intermittency in homogeneous and isotro

Weihang Chen, Cheng Yang, Jie Ren, Zhiqiang Li

Real-life deployment of federated Learning (FL) often faces non-IID data, which leads to poor accuracy and slow convergence. Personalized FL (pFL) tackles these issues by tailoring local models to individual data sources and using weighted aggregation methods for client-specific learning. However, existing pFL methods often fail to provide each local model w

Wade Hann-Caruthers, Minghao Pan, Omer Tamuz

We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with

Xingyu Zhou, Roberto Armellin, Dong Qiao, Xiangyu Li

The directional state transition tensor (DSTT) reduces the complexity of state transition tensor (STT) by aligning the STT terms in sensitive directions only, which provides comparable accuracy in orbital uncertainty propagation. The DSTT assumes the sensitive directions to be constant during the integration and only works at a predefined epoch. This paper p

Haitam Ben Yahia, Denis Korzhenkov, Ioannis Lelekas, Amir Ghodrati

Video diffusion models have achieved impressive realism and controllability but are limited by high computational demands, restricting their use on mobile devices. This paper introduces the first mobile-optimized video diffusion model. Starting from a spatio-temporal UNet from Stable Video Diffusion (SVD), we reduce memory and computational cost by reducing

Meznah Aloqalaa, Stian Soiland-Reyes, Carole Goble

The reproducibility of computational pipelines is an expectation in biomedical science, particularly in critical domains like human health. In this context, reporting next generation genome sequencing methods used in precision medicine spurred the development of the IEEE 2791-2020 standard for Bioinformatics Analyses Generated by High Throughput Sequencing (

Jaka Vodeb, Vid Eržen, Timotej Hrga, Janez Povh

This paper investigates the performance of quantum, classical, and hybrid solvers on the NP-hard Max-Cut and QUBO problems, examining their solution quality relative to the global optima and their computational efficiency. We benchmark the new fast annealing D-Wave quantum processing unit (QPU) and D-Wave Hybrid solver against the state-of-the-art classical

Thiziri Aissaoui, Anil Murani, Raphaël Lescanne, Alain Sarlette

DC-voltage-biased Josephson junctions have been recently employed in superconducting circuits for Hamiltonian engineering, demonstrating microwave amplification, single photon sources and entangled photon generation. Compared to more conventional approaches based on parametric pumps, this solution typically enables larger interaction strengths. In the contex

Julia Dima, Pablo Gómez, Sandor Kruk, Peter Kretschmar

Reflected or scattered light produce artefacts in astronomical observations that can negatively impact the scientific study. Hence, automated detection of these artefacts is highly beneficial, especially with the increasing amounts of data gathered. Machine learning methods are well-suited to this problem, but currently there is a lack of annotated data to t

Adeline Lo, Santiago Olivella, Kosuke Imai

Many networks in political and social research are bipartite, with edges connecting exclusively across two distinct types of nodes. A common example includes cosponsorship networks, in which legislators are connected indirectly through the bills they support. Yet most existing network models are designed for unipartite networks, where edges can arise between

Amna Gillani, Beatriz Lorenzo, Majid Ghaderi, Fikadu Dagefu

Nodes in contemporary radio networks often have multiple interfaces available for communication: WiFi, cellular, LoRa, Zigbee, etc. This motivates understanding both link and network configuration when multiple communication modalities with vastly different capabilities are available to each node. In conjunction, covertness or the hiding of radio communicati

Soumik Ghosh, Dominik Hangleiter, Jonas Helsen

Graph states are fundamental objects in the theory of quantum information due to their simple classical description and rich entanglement structure. They are also intimately related to IQP circuits, which have applications in quantum pseudorandomness and quantum advantage. For us, they are a toy model to understand the relation between circuit connectivity,

Yichen Li, Chicheng Zhang

Imitation learning (IL) is a paradigm for learning sequential decision making policies from experts, leveraging offline demonstrations, interactive annotations, or both. Recent advances show that when annotation cost is tallied per trajectory, Behavior Cloning (BC) which relies solely on offline demonstrations cannot be improved in general, leaving limited c

Matthias Franz

In this note we show that in the simplicial setting, the classifying space construction converts short exact sequences of groups not just to homotopy fibrations, but in fact to fibre bundles.

Erik Bahnson, Leonidas Daskalakis, Abbas Dohadwala, Ish Shah

We establish pointwise convergence for nonconventional ergodic averages taken along $\lfloor p^c\rfloor$, where $p$ is a prime number and $c\in(1,4/3)$ on $L^r$, $r\in(1,\infty)$. In fact, we consider averages along more general sequences $\lfloor h(p)\rfloor$, where $h$ belongs in a wide class of functions, the so-called $c$-regularly varying functions. We

John McDermid, Yan Jia, Ibrahim Habli

Traditional safety engineering assesses systems in their context of use, e.g. the operational design domain (road layout, speed limits, weather, etc.) for self-driving vehicles (including those using AI). We refer to this as downstream safety. In contrast, work on safety of frontier AI, e.g. large language models which can be further trained for downstream t

Esme Rosen

Recently, Allen, Grove, Long, and Tu proposed an explicit Hypergeometric-Modularity method which gives a concrete link between certain hypergeometric objects and modular forms. The theory is exemplified by a collection of 199 weight 3 modular forms. Among other properties their process shows that the $L$-value of such a modular form at 1 is an explicit multi

Nazim Khelifa

We derive a new bound on the dimension of images of period maps of global pure polarized integral variations of Hodge structures with generic Hodge datum of level at least 3. When the generic Mumford-Tate domain of the variation is a period domain parametrizing Hodge structures with given Hodge numbers, we prove that the new bound is at worst linear in the H

Emily Sageser, Yao-Yuan Mao, Ekta Patel

The correlations between dark matter halo properties and subhalo abundance, or occupation, have been studied extensively; however, existing studies have mainly focused on subhalos within the virial radius of the host halo. In this work, we quantify the correlation between host halo properties and the abundance of neighboring halos that reside right outside o

Wan He, Tina Eliassi-Rad, Samuel V. Scarpino

Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional alignment-based models rely on genome similar

Sydney Anuyah, Mallika K Singh, Hope Nyavor

The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the ap

Zizhao Hu, Xiaolin Zhou, Mohammad Rostami

The success of vision transformers is widely attributed to the expressive power of their dynamically parameterized multi-head self-attention mechanism. We examine the impact of substituting the dynamic parameterized key with a static key within the standard attention mechanism in Vision Transformers. Our findings reveal that static key attention mechanisms c

Antoine Poulin

We prove that measure-class-preserving non-amenable treeable equivalence relations of type III, meaning not preserving any equivalent $\sigma$-finite measure, are induced by free actions of non-abelian free groups of any given number of generators, including infinitely generated free groups, with the additional property that no ends of the induced Schreier g

Simon Bartlmae, Paul J. Jünger, Elmar Langetepe

The Euclidean Steiner Tree Problem (EST) seeks a minimum-cost tree interconnecting a given set of terminal points in the Euclidean plane, allowing the use of additional intersection points. In this paper, we consider two variants that include an additional straight line $\gamma$ with zero cost, which must be incorporated into the tree. In the Euclidean Stein

Tim Gorichanaz

Human-centered design, a theoretical ideal, is sometimes compromised in industry practice. Technology firms juggle competing priorities, such as adopting new technologies and generating shareholder returns, which may conflict with human-centered design values. This study sought to identify the types of workplace situations that present barriers for human-cen

Ivan Beldiev, Dmitry Timashev

An algebraic variety $X$ is called a homogeneous space if there exists a transitive regular action of an algebraic group on $X$. We prove inequalities between the dimension of a homogeneous space of a linear algebraic group and its Picard number.

John Harrison, Richard Anantua

We analytically determine neutrino transitional probabilities and abundance ratios at various distances from the source of creation in several astrophysical contexts, including the Sun, supernovae and cosmic rays. In doing so, we determine the probability of a higher-order transition state from $\nu_\tau\rightarrow\nu_\lambda$, where $\nu_\lambda$ represents

Mahdi Ahmadi, Neda Khosh Kheslat, Adebola Akintomide

The rapid advancement of Generative AI (Gen AI) technologies, particularly tools like ChatGPT, is significantly impacting the labor market by reshaping job roles and skill requirements. This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected from major job platforms between May and December

Weidong Zhu, Xueqian Li, Longwei Wang, Zheng Zhang

Cognitive radio networks (CRNs) have traditionally focused on utilizing idle channels to enhance spectrum efficiency. However, as wireless networks grow denser, channel-centric strategies face increasing limitations. This paper introduces a paradigm shift by exploring the underutilized potential of idle spatial dimensions, termed idle space, in co-channel tr

Samuel Stocksieker, Denys Pommeret, Arthur Charpentier

Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper, we introduce a novel method aimed at en

Robin Hwang

Traditional accelerators, while effective, suffer from extensive spatial and financial demands, necessitating the exploration of compact alternatives like PWFA, which significantly reduces the necessary accelerator length by utilizing the wake generated by a high-speed pulse traveling through plasma. Our research focuses on mitigating instabilities, particul

Diego F. Uribe, Mateo Londoño, Julio C. Arce

We propose a multilevel scheme to coherently transfer the population of a diatomic molecule from a rovibrational level to a target rovibrational level of the same electronic state or another. It involves a linear chain of N rovibrational levels alternating between the initial electronic state and a second electronic state, conveniently selected according to

Adrita Samanta, Henry Han, Darby Huye, Lan Liu

Distributed systems are comprised of many components that communicate together to form an application. Distributed tracing gives us visibility into these complex interactions, but it can be difficult to reason about the system's behavior, even with traces. Systems collect large amounts of tracing data even with low sampling rates. Even when there are pattern

Orlando Quaranta, Nunzia Coppola, Lisa Gades, Alice Galdi

This study investigates the temperature-induced hysteretic behavior of resistivity and magnetoresistance in electrodeposited bismuth films, with a focus on their application as absorbers in transition-edge sensors (TESs) for X-ray detection. Through a series of resistance versus temperature measurements from room temperature to a few Kelvins, we explore the

Naira Abdou Mohamed, Zakarya Erraji, Abdessalam Bahafid, Imade Benelallam

If today some African languages like Swahili have enough resources to develop high-performing Natural Language Processing (NLP) systems, many other languages spoken on the continent are still lacking such support. For these languages, still in their infancy, several possibilities exist to address this critical lack of data. Among them is Transfer Learning, w

Olivia R. Young, Timothy E. Dolch, Joseph F. Helmboldt, Christopher Mentrek

Low-frequency solar radio emission is sourced in the solar corona, with sub-100 MHz radio emission largely originating from the $\sim$10$^{5}$\,$\mathrm{K}$ plasma around 2 optical radii. However, the region of emission has yet to be constrained at 35--45\,MHz due to both instrumentation limitations and the rarity of astronomical events, such as total solar

Nathaniel S. Woodward, Sang Eon Park, Gaia Grosso, Jeffrey Krupa

Physical data are representations of the fundamental laws governing the Universe, hiding complex compositional structures often well captured by hierarchical graphs. Hyperbolic spaces are endowed with a non-Euclidean geometry that naturally embeds those structures. To leverage the benefits of non-Euclidean geometries in representing natural data we develop m

Indranil Biswas, Atanu Bhunia, Subrata Bera, Indrani Chattopadhyay

Quantifying multipartite entanglement poses a significant challenge in quantum information theory, prompting recent advancements in methodologies to assess it. We introduce the notion of \enquote{Volume of Assistance} (VoA), which computes the geometric mean of entanglement of assistance across all potential parties. We demonstrate the feasibility of VoA for

Jens Ludwig, Sendhil Mullainathan, Ashesh Rambachan

Large language models (LLMs) enable researchers to analyze text at unprecedented scale and minimal cost. Researchers can now revisit old questions and tackle novel ones with rich data. We provide an econometric framework for realizing this potential in two empirical uses. For prediction problems -- forecasting outcomes from text -- valid conclusions require

Amirhossein Abaskohi, Spandana Gella, Giuseppe Carenini, Issam H. Laradji

Multimodal multihop question answering (MMQA) requires reasoning over images and text from multiple sources. Despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets. Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications like interpreting edu

Christopher G. Brinton, Mung Chiang, Kwang Taik Kim, David J. Love

We provide a taxonomy of a dozen enabling network architectures, protocols, and technologies that will define the evolution from 5G to 6G. These technologies span the network protocol stack, different target deployment environments, and various perceived levels of technical maturity. We outline four areas of societal focus that will be impacted by these tech

Regner Trampedach, Werner Däppen

The first order effect of Coulomb forces between the charged particles of a plasma is the well-known Debye-H\"uckel-term. It is a negative contribution to the pressure and energy of the gas, that at high densities will overwhelm the ideal gas contributions and make the gas implode into a black hole. Nature obviously constrains this term, avoiding this fate,

Nishanth Nakshatri, Shamik Roy, Rajarshi Das, Suthee Chaidaroon

Constrained decoding with lookahead heuristics (CDLH) is a highly effective method for aligning LLM generations to human preferences. However, the extensive lookahead roll-out operations for each generated token makes CDLH prohibitively expensive, resulting in low adoption in practice. In contrast, common decoding strategies such as greedy decoding are extre

Ming Fan, Zezhong Zhang, Dan Lu, Guannan Zhang

We introduce GenAI4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. GenAI4UQ leverages a generative artificial intelligence (AI) based conditional modeling framework to address the limitations of traditional inverse modeling techniques, such as Marko

Mahir Hadzic, Matias Moreno

We consider space-periodic and inhomogeneous steady states of the one-dimensional electrostatic Vlasov-Poisson system, known as the Bernstein-Greene-Kruskal (BGK) waves. We prove that there exists a large class of fixed background ion densities and spatial periods, so that the corresponding linearised operator around the associated BGK-equilibria has no embe

N. H. Gonzalez-Gutierrez, Rodolfo Casana, André C. Santos

We investigate the existence of compact self-dual solitons in the restricted gauged baby Skyrme model in the presence of an external magnetic field. The consistent implementation of the Bogomol'nyi-Prasad-Sommerfield (BPS) formalism depends on the relative size between the compacton and the effective region occupied by the external magnetic field. To address

Aristeidis Voulgaris, Christophoros Mouratidis, Andreas Vossinakis

Our research is focused on the missing, but important and necessary Draconic gearing of the Antikythera Mechanism. The three Lunar cycles Sidereal, Synodic and Anomalistic are represented on the Mechanism by correlating the Fragments A and C (part of the Front plate), whereas the fourth Lunar cycle Draconic results after correlating the unplaced Fragment D w

Longwei Wang, Xueqian Li, Zheng Zhang

The resilience of convolutional neural networks against input variations and adversarial attacks remains a significant challenge in image recognition tasks. Motivated by the need for more robust and reliable image recognition systems, we propose the Dense Cross-Connected Ensemble Convolutional Neural Network (DCC-ECNN). This novel architecture integrates the

Navyansh Mahla, Sunny Gupta, Amit Sethi

Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted for parameter efficient federated fine-tuning, recent theoretical and empirical studies highlight its suboptimal performance in the federated learning context. In response, we pro

Lucia McCallum, David Schunck, Jamie McCallum, Tiege McCarthy

This paper introduces a new instrument enabling a novel combination of Earth measuring techniques: direct observations with the radio astronomical instruments to satellites of the global navigation satellite systems. Inter-technique biases are a major error source in the terrestrial reference frame. Combining two major space-geodetic techniques, GNSS and VLB

Bohan Jiang, Dawei Li, Zhen Tan, Xinyi Zhou

Measuring the relative impact of CTs is important for prioritizing responses and allocating resources effectively, especially during crises. However, assessing the actual impact of CTs on the public poses unique challenges. It requires not only the collection of CT-specific knowledge but also diverse information from social, psychological, and cultural dimen