May 2024 arXiv papers — page 89

Austin Han, Olivier Desjardins

The mesh-dependency of the breakup of liquid films, including their breakup length scales and resulting drop size distributions, has long been an obstacle inhibiting the computational modeling of large-scale spray systems. With the aim of overcoming this barrier, this work presents a framework for the prediction and modeling of subgrid-thickness liquid film

Wei Li, Tianhong Wang, Tai-Fu Feng, Guo-Li Wang

Based on the instantaneous Bethe-Salpeter equation method, we calculate the average values $\overline{|\vec{q}|^n}\equiv q^n$ and speed powers $\overline{|\vec{v}|^n} \equiv v^n$ ($n=1,2,3,4$) of a heavy quark inside $S$ wave and $P$ wave heavy-light mesons, where $\vec{q}$ and $\vec{v}$ are the three dimensional momentum and velocity of the heavy quark, res

Taihei Oki, Shinsaku Sakaue

M${}^{\natural}$-concave functions, a.k.a. gross substitute valuation functions, play a fundamental role in many fields, including discrete mathematics and economics. In practice, perfect knowledge of M${}^{\natural}$-concave functions is often unavailable a priori, and we can optimize them only interactively based on some feedback. Motivated by such situati

Ruyuan Wan, Simret Gebreegziabhe, Toby Jia-Jun Li, Karla Badillo-Urquiola

In recent years, there has been a growing interest in employing intelligent agents in writing. Previous work emphasizes the evaluation of the quality of end product-whether it was coherent and polished, overlooking the journey that led to the product, which is an invaluable dimension of the creative process. To understand how to recognize human efforts in co

Feinan Lu, Tao Wang, Ying Lu, Jie Chen

In the last two decades, single-arm trials (SATs) have been effectively used to study anticancer therapies in well-defined patient populations using durable response rates as an objective and interpretable clinical endpoints. With a growing trend of regulatory accelerated approval (AA) requiring randomized controlled trials (RCTs), some confusions have arise

Martin Nisser

Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex glob

Toufik Mansour, Mark Shattuck

Let $\mathcal{C}_n$ denote the set of words $w=w_1\cdots w_n$ on the alphabet of positive integers satisfying $w_{i+1}\leq w_i+1$ for $1 \leq i \leq n-1$ with $w_1=1$. The members of $\mathcal{C}_n$ are known as Catalan words and are enumerated by the $n$-th Catalan number $C_n$. The problem of finding the cardinality of various avoidance classes of $\mathca

Yonghao Liu, Mengyu Li, Di Liang, Ximing Li

Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information,

Sudhir Agarwal, Anu Sreepathy, David H. Alonso, Prarit Lamba

Recent availability of Large Language Models (LLMs) has led to the development of numerous LLM-based approaches aimed at providing natural language interfaces for various end-user tasks. These end-user tasks in turn can typically be accomplished by orchestrating a given set of APIs. In practice, natural language task requests (user queries) are often incompl

He Sun, Lipeng Zhu, Weidong Mei, Rui Zhang

In this paper, we study an IRS-assisted coverage enhancement problem for a given region, aiming to optimize the passive reflection of the IRS for improving the average communication performance in the region by accounting for both deterministic and random channels in the environment. To this end, we first derive the closed-form expression of the average rece

Igor Mikolasek, Saeedeh Ghanadbashi, Nima Afraz, Fatemeh Golpayegani

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of d

Jian-Fu Zhang, Shi-Min Liang, Hua-Ping Xiao

Magnetic reconnection is a fundamental process for releasing magnetic energy in space physics and astrophysics. At present, the usual way to investigate the reconnection process is through analytical studies or first-principles numerical simulations. This paper is the first to understand the turbulent magnetic reconnection process by exploring the nature of

Felipe Navarete, Pedro Ticiani dos Santos, Alex Cavaliéri Carciofi, André Luiz Figueiredo

H$\alpha$ emission is a clear indicator of circumstellar activity in Be stars, historically employed to assess the classical Be star (CBe) population in young open clusters (YOCs). The YOC NGC330 in the Small Magellanic Cloud exhibits a large known fraction of {CBe} stars and was selected for a pilot study to establish a comprehensive methodology for identif

Gamaliel Cerda-Morales

In \cite{Go}, G\"okba\c{s} defined a new type of number sequence called Leonardo-Alwyn sequence. In this paper, we consider the generalized Leonardo-Alwyn hybrid numbers and investigate some of their properties. We also give some applications related to the generalized Leonardo-Alwyn hybrid numbers in matrices.

Talles E. M. Marques, Yuri H. Isayama, Felipe M. F. Teixeira, Fabiano C. Santana

The strong coupling of plasmonic resonance modes in conductive pyramidal nanoparticles leads to an increase in the density of free charges on the surface. By ensuring plasmonic coupling in the pyramidal nanoparticle lattice, the achieved field intensity is potentiated. At the same time, a strong coupling between resonant modes is guaranteed, which results in

Samrah Arif, Muhammad Arif Khan, Sabih Ur Rehman

In the rapidly growing development of the Internet of Things (IoT) infrastructure, achieving reliable wireless communication is a challenge. IoT devices operate in diverse environments with common signal interference and fluctuating channel conditions. Accurate channel estimation helps adapt the transmission strategies to current conditions, ensuring reliabl

Bardia Nadimi, Hao Zheng

This paper proposes a novel method for automatically inferring message flow specifications from the communication traces of a system-on-chip (SoC) design that captures messages exchanged among the components during a system execution. The inferred message flows characterize the communication and coordination of components in a system design for realizing var

Zongda Li, Yiqing Xu, Stéphane Coen, Stuart G. Murdoch

Phase-coherent pulsed driving of passive optical fiber resonators enable the generation of ultrashort dissipative Raman solitons with durations well below 100~fs. The existence and characteristics of such solitons critically depends on the desynchronisation between the pulsed driving source and the resonator roundtrip time, yet the full mechanism through whi

Fan Shi, Chong Zhang, Takahiro Miki, Joonho Lee

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally investigat

Sarra Ahallal, Fedoua Sghiouer, Ali Kacha

In the present paper and as an application of Roth's theorem concerning the rational approximation of algebraic numbers, we give a sufficient condition that will assure us that a sum, product and quotient of some series of positive rational terms are transcendental numbers. We recall that all the infinite series that we are going to treat are Liouville numbe

Wera M Schmerer

PCR-based analysis of DNA is utilized in a wide variety of fields, including Forensic Science. Aside from the more common ample sources, material analyzed here can refer to specimen excavated from a soil environment, or a sampling of the soil itself to recover DNA leached into the soil from decomposing human remains or from body fluids intermingled with the

Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar, Pablo A. Parrilo

Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential decision-making problems based on observed human demonstrations and feedback. Most prior work in reward learning has relied on prior knowledge

Boqian Li, Xuan Li, Ying Jiang, Tianyi Xie

Traditional 3D garment creation is labor-intensive, involving sketching, modeling, UV mapping, and texturing, which are time-consuming and costly. Recent advances in diffusion-based generative models have enabled new possibilities for 3D garment generation from text prompts, images, and videos. However, existing methods either suffer from inconsistencies amo

Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Milad Cheraghalikhani

We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike the conventional method of random masking, our technique utilizes a teacher-student model to focus on intricate areas within the data,

Moumanti Podder, Anish Sarkar

We study a learning model in which an agent is stationed at each vertex of $\mathbb{T}_{m}$, the rooted tree in which each vertex has $m$ children. At any time-step $t \in \mathbb{N}_{0}$, they are allowed to select one of two available technologies: $B$ and $R$. Let the technology chosen by the agent at vertex $v\in\mathbb{T}_{m}$, at time-step $t$, be $C_{

Teun A. J. van Schijndel, Anthony P. McFadden, Aaron N. Engel, Jason T. Dong

Motivated by recent advancements highlighting Ta as a promising material in low-loss superconducting circuits and showing long coherence times in superconducting qubits, we have explored the effect of cryogenic temperatures on the growth of Ta and its integration in superconducting circuits. Cryogenic growth of Ta using a low temperature molecular beam epita

Mozhgan Mohammadpour, Shayne Waldron

The decomposition of the polynomials on the quaternionic unit sphere in $\Hd$ into irreducible modules under the action of the quaternionic unitary (symplectic) group and quaternionic scalar multiplication has been studied by several authors. Typically, these abstract decompositions into ``quaternionic spherical harmonics'' specify the irreducible representa

Guangyu Wu, Panagiotis Tsiotras, Anders Lindquist

Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of directly modifying (i.e., ``steering'') the

Itai Ashlagi, Süleyman Kerimov, Omer Tamuz, Geng Zhao

In economies without monetary transfers, token systems serve as an alternative to sustain cooperation, alleviate free riding, and increase efficiency. This paper studies whether a token-based economy can be effective in marketplaces with thin exogenous supply. We consider a marketplace in which at each time period one agent requests a service, one agent prov

C. M. Downey, Terra Blevins, Dhwani Serai, Dwija Parikh

The "massively-multilingual" training of multilingual models is known to limit their utility in any one language, and they perform particularly poorly on low-resource languages. However, there is evidence that low-resource languages can benefit from targeted multilinguality, where the model is trained on closely related languages. To test this approach more

Spencer Young, Riley Sinema, Cole Edgren, Andrew Hall

While significant progress has been made in specifying neural networks capable of representing uncertainty, deep networks still often suffer from overconfidence and misaligned predictive distributions. Existing approaches for measuring this misalignment are primarily developed under the framework of calibration, with common metrics such as Expected Calibrati

Álvaro Martínez-Sánchez, Gonzalo Arranz, Adrián Lozano-Durán

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogeno

J. Zak, H. M. J. Boffin, E. Sedaghati, A. Bocchieri

Planetary systems in mean motion resonances hold a special place among the planetary population. They allow us to study planet formation in great detail as dissipative processes are thought to have played an important role in their existence. Additionally, planetary masses in bright resonant systems may be independently measured both by radial velocities (RV

Jinhao Liu, Jun Yang, Jianliang Mao, Tianqi Zhu

A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, the

Vincent Hickl, Abid Khan, René M. Rossi, Bruno F. B. Silva

The spread of microbial infections is governed by the self-organization of bacteria on surfaces. Limitations of live imaging techniques make collective behaviors in clinically relevant systems challenging to quantify. Here, novel experimental and image analysis techniques for high-fidelity single-cell segmentation of bacterial colonies are developed. Machine

Edward Hallé-Hannan, Charles Audet, Youssef Diouane, Sébastien Le Digabel

Heterogeneous datasets emerge in various machine learning and optimization applications that feature different input sources, types or formats. Most models or methods do not natively tackle heterogeneity. Hence, such datasets are often partitioned into smaller and simpler ones, which may limit the generalizability or performance, especially when data is limi

Petr Marek

The main bottleneck for universal quantum computation with traveling light is the preparation of Gottesman-Kitaev-Preskill states of sufficient quality. This is an extremely challenging task, experimental as well as theoretical, also because there is currently no single easily computable measure of quality for these states. We introduce such measure, GKP squ

Lalit Yadav, Afonso Rufino, Rabindranath Bag, Matthew Ennis

Geometrically frustrated magnetic systems, such as those based on the Shastry-Sutherland lattice (SSL), offer a rich playground for exploring unconventional magnetic states. The delicate balance between competing interactions in these systems leads to the emergence of novel phases. We present the characterization of Er2Be2GeO7, an SSL compound with Er3+ ions

Titouan Duston, Zhen Tao, Xuezhi Bian, Mansi Bhati

We show empirically that a phase-space non-Born-Oppenheimer electronic Hamiltonian approach to quantum chemistry (where the electronic Hamiltonian is parameterized by both nuclear position and momentum, (H(R,P)) is both a practical and accurate means to recover vibrational circular dichroism spectra. We further hypothesize that such a phase space approach ma

Tomás Ahumada, Shreya Anand, Michael W. Coughlin, Vaidehi Gupta

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance

Yui Hayashi, Yuya Tanizaki

Magnetic excitations play a crucial role in understanding the color confinement of $4$d Yang-Mills theory, and we have the monopole and the center vortex as plausible candidates to explain its mechanism. Under suitable compactified setups of $4$d Yang-Mills theory, we can achieve different weakly-coupled descriptions of confinement phenomena: The monopole me

Oscar Smee, Fred Roosta

We consider solving large scale nonconvex optimisation problems with nonnegativity constraints. Such problems arise frequently in machine learning, such as nonnegative least-squares, nonnegative matrix factorisation, as well as problems with sparsity-inducing regularisation. In such settings, first-order methods, despite their simplicity, can be prohibitivel

Edier Paredes Cruz, Antonio O. Bouzas, F. Larios

We study the consequences for top-quark physics of having electron and positron beams available at the LHeC and FCC-he, as was the case in HERA. We show that the asymmetry between top production in $pe^+$ collisions and antitop production in $pe^-$ reactions is sensitive to $|V_{td}|$. By means of detailed parton-level Monte Carlo simulations of single $t$ a

Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto

World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model environment dynamics. However, this compression into a compact discrete representation may ignore visual details that are important for rein

Jason Chun Lok Li, Steven Tin Sui Luo, Le Xu, Ngai Wong

Coordinate network or implicit neural representation (INR) is a fast-emerging method for encoding natural signals (such as images and videos) with the benefits of a compact neural representation. While numerous methods have been proposed to increase the encoding capabilities of an INR, an often overlooked aspect is the inference efficiency, usually measured

Hoang Ky Nguyen, Francisco S. N. Lobo

This Letter aims to advance unexplored properties of a new class of Closed Timelike Curves recently discovered in scalar-tensor gravity, reported in Universe 9, 467 (2023) and Eur.$\,$Phys.$\,$J.$\,$C 83, 626 (2023). Therein, it was shown that when the Weak Energy Condition is violated, the topology of spacetime in scalar-tensor gravity is altered, enabling

Mario Fuentes

In an arbitrary complete differential graded Lie algebra, we construct a group operation $\bullet$ on $L_1$ such that the differential of the product of two elements is the Baker-Campbell-Hausdorff product of their differentials, i.e., $d(x\bullet y)=dx\ast dy$. We study some properties of this new structure and some applications, especially in homotopy theo

Hao Wang, Milinda Randeniya, Austin Houston, Gerd Duscher

Access to intrinsic properties of a 2D material is challenging due to the absence of a bulk that would dominate over surface contamination, and this lack of bulk also precludes effective conventional cleaning methods that are almost always sacrificial. Suspended graphene and carbon contaminants represent the most salient challenge. This work has achieved ult

Leon K. Harding, Jonathan W. Arenberg, Benjamin Donovan, Dave Oberg

We describe the space observatory architecture and mission design of the SALTUS mission, a NASA Astrophysics Probe Explorer concept. SALTUS will address key far-infrared science using a 14-m diameter <45 K primary reflector (M1) and will provide unprecedented levels of spectral sensitivity for planet, solar system, and galactic evolution studies, and cosmic

Samuel Kováčik, Juraj Tekel, Matej Hrmo

In our previous contribution, we introduced a matrix formulation of a three-dimensional quantum space named the fuzzy onion. The novel part of the construction is the radial derivative term, which has been defined to recover the correct continuum limit. Here, we describe a numerical simulation of the scalar field theory in this space and test some physical p

Benjamin Bakker, Ananth N Shankar, Jacob Tsimerman

We prove that Shimura varieties admit integral canonical models for sufficiently large primes. In the case of abelian-type Shimura varieties, this recovers work of Kisin-Kottwitz for sufficiently large primes. We also prove the existence of integral canonical models for images of period maps corresponding to geometric families. We deduce several consequences

Antonin Sulc, Annika Eichler, Tim Wilksen

High-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL. Exchanging these high-power components takes time and effort, thus it is necessary to minimize maintenance and downtime and at the same time maximize the device's operation. In an a

Eliuvish Cuicizion

Principal curve is a well-known statistical method oriented in manifold learning using concepts from differential geometry. In this paper, we propose a novel metric-based principal curve (MPC) method that learns one-dimensional manifold of spatial data. Synthetic datasets Real applications using MNIST dataset show that our method can learn the one-dimensiona

Samuel Kováčik

Two interesting hypotheses about black holes have been proposed. The older one states that microscopic black holes can be accountable for the observed dark matter density. The newer one states that black holes are coupled to the expansion of the universe. Here, we combine those ideas and investigate the behaviour of expanding microscopic black holes. We obse

Sayle Sigarreta, Hugo Cruz-Suarez

In this manuscript, we delve into the exploration of the first and second Zagreb connection indices of both polyomino chains and random polyomino chains. Our methodology relies on the utilization of Markov chain theory. Within this framework, the article thoroughly examines precise formulas and investigates extreme values. Leveraging the derived formulas, we

Brad M. S. Hansen, Tze-Yeung Yu, Yasuhiro Hasegawa

We describe the evolution of low mass planets in a dispersing protoplanetary disk around a Solar mass star. The disk model is based on the results of Yu, Hansen & Hasegawa (2023), which describes a region of the inner disk where the direction of the migration torque is outwards due to the diffusion of the stellar magnetic field into the disk and the resultan

Zonghao Chen, Ruocheng Guo, Jean-François Ton, Yang Liu

Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenarios. Predicting potential outcomes along with its uncertainty in a counterfactual world poses the foundamental

Sisi Shao, Junhyung Park, Weng Kee Wong

General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents Particle Swarm Optimization (PSO), as an alternative to many of the current algorithms used in statistics. We find that PSO can not only reproduce the same results as the above rou

Gabriel J. Selzer, Curtis T. Rueden, Mark C. Hiner, Edward L. Evans

Many scientific software platforms provide plugin mechanisms that simplify the integration, deployment, and execution of externally developed functionality. One of the most widely used platforms in the imaging space is Fiji, a popular open-source application for scientific image analysis. Fiji incorporates and builds on the ImageJ and ImageJ2 platforms, whic

Zhen Huang, Amy Aumpansub

Deep learning has been shown to be a promising tool in detecting software vulnerabilities. In this work, we train neural networks with program slices extracted from the source code of C/C++ programs to detect software vulnerabilities. The program slices capture the syntax and semantic characteristics of vulnerability-related program constructs, including API

Yulong Pan, Per-Olof Persson

We introduce the concept of half-closed nodes for nodal discontinuous Galerkin (DG) discretisations. Unlike more commonly used closed nodes in DG, where on every element nodes are placed on all of its boundaries, half-closed nodes only require nodes to be placed on a subset of the element's boundaries. The effect of using different nodes on DG operator spars

Jayaprakash Sundararaj

Automatic summarization is the process of reducing a text document in order to generate a summary that retains the most important points of the original document. In this work, we study two problems - i) summarizing a text document as set of keywords/caption, for image recommedation, ii) generating opinion summary which good mix of relevancy and sentiment wi

Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh

Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner when compared to typical neural networks. Many recent advancements in reservoir computing, in particular quantum reservoir computing, make use of reservoirs that are inherently stochastic. However, the theoretical ju

Mirko Residori, Suvendu Mandal, Axel Voigt, Christina Kurzthaler

We study low-Reynolds-number fluid flow through a two-dimensional porous medium modeled as a Lorentz gas. Using extensive finite element simulations we fully resolve the flow fields for packing fractions approaching the percolation threshold. Near the percolation transition, we find a power-law scaling of the flow rate versus the pressure drop with an expone

Alanna 'Lanie' Leung, Alexander V. Mironenko

The independent atom ansatz of density functional theory yields an accurate analytical expression for dynamic correlation energy in the H$_{2}$ molecule: $E_{c} = 0.5(1 - \sqrt{2})(ab|ba)$ for the atom-additive self-consistent density $\rho = |a|^{2} + |b|^{2}$. Combined with exact atomic self-exchange, it recovers more than 99.5 % of nearly exact SCAN excha

Youngkyu Lee, Shanqing Liu, Zongren Zou, Adar Kahana

Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator~(DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain \textit{without scatterers} but at differen

Amit Kundu, Debasis Sarkar

Network Nonlocality is an advanced study of quantum nonlocality that comprises network structure beyond Bell's theorem. The development of quantum networks has the potential to bring a lot of technological applications in sevaral quantum information processing tasks. Here, we are focusing on how the role of the independence of the measurement choices of the

Ulysse Chabaud, Roohollah Ghobadi, Salman Beigi, Saleh Rahimi-Keshari

Quantum kernel methods are a proposal for achieving quantum computational advantage in machine learning. They are based on a hybrid classical-quantum computation where a function called the quantum kernel is estimated by a quantum device while the rest of computation is performed classically. Quantum advantages may be achieved through this method only if the

Feilong Jiang, Xiaonan Hou, Min Xia

Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable success in predicting RUL, challenges such as low prediction accuracy and interpretability pose significant challenges, hindering their practical implementation. In this work, we in

Chao Pang, Yu-Hao Deng, Ezat Kheradmand, Luis Moreno Hagelsieb

Silicon photonics faces a persistent challenge in extending photodetection capabilities beyond the 1.6 um wavelength range, primarily due to the lack of appropriate epitaxial materials. Colloidal quantum dots (QDs) present a promising solution here, offering distinct advantages such as infrared wavelength tunability, cost-effectiveness, and facile deposition

Yu-Hsiu Lin, Jose L. Mendoza-Cortes

In the quest for better energy storage solutions, the role of designing effective electrodes is crucial. Previous research has shown that using materials like single-side fluorinated graphene can improve the stability of ion insertion in few-layer graphene (FLG), which is vital as we move beyond lithium-ion batteries. Alternatives such as sodium and potassiu

Nyumbu Chishwashwa, Vance Faber, Noah Streib

Efficiency of routing on a regular digraph often involves finding opitmal properties of the graph. For example, the diameter of a digraph is the maximum distance between any two vertices. We show how we can study these problems algebraically in terms of quasigroups, 1-factors, and permutation groups. Our investigation originated from the study of graphs as t

M. Ruggeri, D. Wolverson, V. Romano, G. Cerullo

1$T$-TaSe$_2$ is a prototypical charge density wave (CDW) material for which electron-phonon coupling and associated lattice distortion play an important role in driving and stabilizing the CDW phase. Here, we investigate the lattice dynamics of bulk 1$T$-TaSe$_2$ using angle-resolved ultralow wavenumber Raman spectroscopy down to 10 cm$^{-1}$. Our high-reso

Jonathan Vasquez, Carlotta Domeniconi, Huzefa Rangwala

Machine Learning algorithms (ML) impact virtually every aspect of human lives and have found use across diverse sectors including healthcare, finance, and education. Often, ML algorithms have been found to exacerbate societal biases present in datasets leading to adversarial impacts on subsets/groups of individuals and in many cases on minority groups. To ef

Koren Ishlach, Itzhak Ben-David, Michael Fire, Lior Rokach

Embedding news articles is a crucial tool for multiple fields, such as media bias detection, identifying fake news, and making news recommendations. However, existing news embedding methods are not optimized to capture the latent context of news events. Most embedding methods rely on full-text information and neglect time-relevant embedding generation. In th

Vincent A. Cicirello

We present algorithms for generating small random samples without replacement. We consider two cases. We present an algorithm for sampling a pair of distinct integers, and an algorithm for sampling a triple of distinct integers. The worst-case runtime of both algorithms is constant, while the worst-case runtimes of common algorithms for the general case of s

Callan M. Wood, James C. A. Miller-Jones, Arash Bahramian, Steven J. Tingay

Multi-wavelength polarimetry and radio observations of Swift J1727.8-1613 at the beginning of its recent 2023 outburst suggested the presence of a bright compact jet aligned in the north-south direction, which could not be confirmed without high angular resolution images. Using the Very Long Baseline Array and the Long Baseline Array, we imaged Swift J1727.8

Rong Liu, Rui Xu, Yue Hu, Meida Chen

3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density control might lead to sub-optimal results; it can sometimes yield noisy geometry and blurry artifacts due to prioritizing

Megha Thukral, Sourish Gunesh Dhekane, Shruthi K. Hiremath, Harish Haresamudram

Human activity recognition (HAR) using ambient sensors in smart homes has numerous applications for human healthcare and wellness. However, building general-purpose HAR models that can be deployed to new smart home environments requires a significant amount of annotated sensor data and training overhead. Most smart homes vary significantly in their layouts,

Zheyuan Zhang, Elif Keles, Gorkem Durak, Yavuz Taktak

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep le

S. D. Campos

In the present work, one analyzes two systems trying to obtain physical conditions where some properties attributed to negative mass can be mimicked by positive mass particles. The first one is the well-known 1/2-spin system described by the Dirac equation in the presence of an external electromagnetic field. Assuming some physical restrictions, one obtains

Douglas A. Torrance

We introduce the ForeignFunctions package for Macaulay2, which uses libffi to provide the ability to call functions from external libraries without needing to link against them at compile time. As examples, we use the library FFTW to multiply polynomials using fast Fourier transforms, call a LAPACK function to solve a general Gauss-Markov linear model proble

Jinwei Lin

The Text Extraction of the Audio from the Video plays an important role in multimedia editing and processing. As a popular open source toolkit, Whisper performs fast in human voice recognition. However, the recognition performance is dependent on the computing resource, which makes the low computing memory running Whisper become difficult. Our paper presents

Vatsal Raina, Mark Gales

Enterprise retrieval augmented generation (RAG) offers a highly flexible framework for combining powerful large language models (LLMs) with internal, possibly temporally changing, documents. In RAG, documents are first chunked. Relevant chunks are then retrieved for a user query, which are passed as context to a synthesizer LLM to generate the query response

Vanshree Bhalotia, Daniel Huber, Jennifer L. van Saders, Travis S. Metcalfe

Stellar spin down is a critical yet poorly understood component of stellar evolution. In particular, results from the Kepler Mission imply that mature age, solar-type stars have inefficient magnetic braking, resulting in a stalled spin down rate. However, a large number of precise asteroseismic ages are needed for mature ($\geq$ 3Gyr) stars in order to probe

Henri Plana, Vitor G. Alves, Maiara S. Carvalho

Blue Compact Galaxies (BCGs), also known as \HII\ galaxies, are dwarf, star-forming objects with relatively simple dynamics, which allows for the investigation of star formation mechanisms in a cleaner manner compared to late-type objects. In this study, we have examined various characteristics of the interstellar medium, in connection with the kinematics an

Ouri Cohen, Soudabeh Kargar, Sungmin Woo, Alberto Vargas

Introduction: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. Methods: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial

Elias Nadi, Hua Zhang

This paper proposes a new coil configuration that uses solenoid ferrites on the receiver side instead of planar ferrite coils that are employed in existing wireless charging systems.The solenoid ferrites are used in the drone legs to help mount the receiver on a moving truck while the two parts of the transmitter are placed on the truck.To validate this idea

Cristian Riveros, Benjamin Scheidt, Nicole Schweikardt

We present an index structure, called the color-index, to boost the evaluation of acyclic conjunctive queries (ACQs) over binary schemas. The color-index is based on the color refinement algorithm, a widely used subroutine for graph isomorphism testing algorithms. Given a database $D$, we use a suitable version of the color refinement algorithm to produce a

Di Xu, Xin Miao, Hengjie Liu, Jessica E. Scholey

Purpose: 4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Networ

Pinchen Xie, Yunrui Qiu, Weinan E

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical

Kleanthis Marios Papadopoulos, Tania Stathaki

The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for this task utilize a ResNet50 backbone pre-trained on natural images. By leveraging recenetly released histopathological foundation models such as UNI and Hibou, the predictive prowe

Nathaniel Hamilton, Kyle Dunlap, Kerianne L. Hobbs

For many space applications, traditional control methods are often used during operation. However, as the number of space assets continues to grow, autonomous operation can enable rapid development of control methods for different space related tasks. One method of developing autonomous control is Reinforcement Learning (RL), which has become increasingly po

Michael Kölle, Timo Witter, Tobias Rohe, Gerhard Stenzel

Quantum Computing aims to streamline machine learning, making it more effective with fewer trainable parameters. This reduction of parameters can speed up the learning process and reduce the use of computational resources. However, in the current phase of quantum computing development, known as the noisy intermediate-scale quantum era (NISQ), learning is dif

Lino Anderson da Silva Grama, Kennerson Nascimento de Sousa Lima

We construct 1-parameter families of well-known solutions to the Yamabe problem defined on Aloff-Wallach Spaces to determine bifurcation instants for these homogeneous spaces by examining changes in the Morse index of these metrics as the parameter varies over the positive real numbers. A bifurcation point for such families is an accumulation point of other

Hasib-Al Rashid, Tinoosh Mohsenin

The advancement of sophisticated artificial intelligence (AI) algorithms has led to a notable increase in energy usage and carbon dioxide emissions, intensifying concerns about climate change. This growing problem has brought the environmental sustainability of AI technologies to the forefront, especially as they expand across various sectors. In response to

Nadia Anwar, Guangya Jiang, Yi Wen, Muqarrab Ahmed

In this review, the current advancements in electrochromic sensors based on two-dimensional (2D) materials with rich chemical and physical properties are critically examined. By summarizing the current trends in and prospects for utilizing multifunctional electrochromic devices (ECDs) in environmental monitoring, food quality control, medical diagnosis, and

Felipe A. Asenjo

Relativistic electromagnetic plasma waves are described by a dynamical equation that can be solved not only in terms of plane waves, but for several different accelerating wavepacket solutions. Depending on the spatial and temporal dependence of the plasma frequency, different kinds of accelerating solution can be obtained, for example, in terms of Airy or W

Cristian Riveros, Markus L. Schmid, Nicole Schweikardt

We present a theoretical framework for the extraction and transformation of text documents. We propose to use a two-phase process where the first phase extracts span-tuples from a document, and the second phase maps the content of the span-tuples into new documents. We base the extraction phase on the framework of document spanners and the transformation pha

Oumar Wone

We study complex analytic projective connections on surfaces in projective n-spaces in terms of the "second" neighborhood of the surface in the ambient space, and in terms of the osculating behavior of the integral curves. We also investigate the action of a remarkable rational transformation on projective connections, and give the geometrical interpretation