April 2024 arXiv papers — page 67

Chenzhuo Li, Danila Zubko, Damien Delespaul, John M. Kolinski

In fracture mechanics, polyacrylamide hydrogels have been widely used as a model material for experiments, benefited from its optical transparency, fracture brittleness, and low Rayleigh wave velocity. To describe the brittle fracture in the hydrogels, linear elastic fracture mechanics comes as the first choice. However, in soft materials such as hydrogels,

Mansoor Hayat, Supavadee Aramvith, Titipat Achakulvisut

SEGSRNet addresses the challenge of precisely identifying surgical instruments in low-resolution stereo endoscopic images, a common issue in medical imaging and robotic surgery. Our innovative framework enhances image clarity and segmentation accuracy by applying state-of-the-art super-resolution techniques before segmentation. This ensures higher-quality in

Jesse Railo

We study Sobolev $H^s(\mathbb{R}^n)$, $s \in \mathbb{R}$, stability of the Fourier phase problem to recover $f$ from the knowledge of $|\hat{f}|$ with an additional Bessel potential $H^{t,p}(\mathbb{R}^n)$ a priori estimate when $t \in \mathbb{R}$ and $p \in [1,2]$. These estimates are related to the ones studied recently by Steinerberger in "On the stabilit

Sergey Stanko, Timur Karimullin, Aleksandr Beznosikov, Alexander Gasnikov

Distributed optimization algorithms have emerged as a superior approaches for solving machine learning problems. To accommodate the diverse ways in which data can be stored across devices, these methods must be adaptable to a wide range of situations. As a result, two orthogonal regimes of distributed algorithms are distinguished: horizontal and vertical. Du

Ukesh Thapa, Bipun Man Pati, Samit Thapa, Dhiraj Pyakurel

The rapid advancement of machine learning techniques has led to their widespread application in various domains including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN), f

Yuri Sato, Kohta Murase, Mukul Bhattacharya, Jose Carpio

Recently, radio emission from tidal disruption events (TDEs) has been observed from months to years after the optical discovery. Some of the TDEs including ASASSN-14ae, ASASSN-15oi, AT 2018hyz, and AT 2019dsg are accompanied by the late-time rebrightening phase characterized by a rapid increase in the radio flux. We show that it can be explained by the off-a

Ignasi Ventura Nadal, Jochen Stiasny, Spyros Chatzivasileiadis

Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a significant increase in the computational cost and complexity of these simulations due to additional degrees of uncertainty, non-linearity and states. Physics-Informed Neural Network

Mattia Dutto, Gabriele Berton, Debora Caldarola, Eros Fanì

Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural networks to extract a global representation, called descriptor, from each image. While the training data for VPR models often originates from diverse, geographically scattered sourc

Wojciech Rudziński, Józef Barnaś, Anna Dyrdał

In this paper we analyze spin waves in bilayers of two-dimensional van der Waals materials, like Vanadium based dichalcogenides, VX$_2$ (X=S, Se, Te) and other materials of similar symmetry. We assume that the materials exhibit Dzialoshinskii- Moriya interaction and in-plane easy-axis magnetic anisotropy due to symmetry breaking induced externally (eg, by st

Claudia M. Peixoto, Diego Marcondes, Mariana P. Melo, Ana C. Maia

The rise in healthcare costs has led to the adoption of cost-sharing devices in health plans. This article explores this discussion by simulating Health Savings Accounts (HSAs) to cover medical and hospital expenses, supported by catastrophic insurance. Simulating 10 million lives, we evaluate the utilization of catastrophic insurance and the balances of HSA

Kunxi Li, Tianyu Zhan, Kairui Fu, Shengyu Zhang

In this study, we focus on heterogeneous knowledge transfer across entirely different model architectures, tasks, and modalities. Existing knowledge transfer methods (e.g., backbone sharing, knowledge distillation) often hinge on shared elements within model structures or task-specific features/labels, limiting transfers to complex model types or tasks. To o

Taeyong Kim, Sang-ri Yi

Resilience has emerged as a crucial concept for evaluating structural performance under disasters because of its ability to extend beyond traditional risk assessments, accounting for a system's ability to minimize disruptions and maintain functionality during recovery. To facilitate the holistic understanding of resilience performance in structural systems,

Haotian Xue, Yongxin Chen

Adversarial examples for diffusion models are widely used as solutions for safety concerns. By adding adversarial perturbations to personal images, attackers can not edit or imitate them easily. However, it is essential to note that all these protections target the latent diffusion model (LDMs), the adversarial examples for diffusion models in the pixel spac

Mattia Coccolo, Jesús M. Seoane, Stefano Lenci, Miguel A. F. Sanjuán

We analyze the nonlinear Helmholtz oscillator in the presence of fractional damping, a characteristic feature in several physical situations. In our specific scenario, as well as in the non-fractional case, for large enough excitation amplitudes, all initial conditions are escaping from the potential well. To address this, we incorporate the phase control te

I. Scott MacKenzie, Janet C. Read, Matthew Horton

Most attendees at CHI conferences will agree that an experiment (user study) is the hallmark of good research in human-computer interaction. But what constitutes an experiment? And how does one go from an experiment to a CHI paper? This course will teach how to pose testable research questions, how to make and measure observations, and how to design and cond

Jiyoun Kim, Junu Kim, Kyunghoon Hur, Edward Choi

The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional centralized approaches, federated learning enables training on data from multiple institutions while preserving patient privacy and complying with regulatory constraints. In pract

Weizhou Cai, Jing-Ning Zhang, Ziyue Hua, Weiting Wang

The discrimination of quantum operations has long been an intriguing challenge, with theoretical research significantly advancing our understanding of the quantum features in discriminating quantum objects. This challenge is closely related to the discrimination of quantum states, and proof-of-principle demonstrations of the latter have already been realized

Namkyeong Cho, Yeoneung Kim

We address the crucial yet underexplored stability properties of the Hamilton--Jacobi--Bellman (HJB) equation in model-free reinforcement learning contexts, specifically for Lipschitz continuous optimal control problems. We bridge the gap between Lipschitz continuous optimal control problems and classical optimal control problems in the viscosity solutions f

Chengyao Tang, Yongpeng Dai, Zhi Li, Yongping Song

Recent years have witnessed the great advance of bioradar system in smart sensing of vital signs (VS) for human healthcare monitoring. As an important part of VS sensing process, VS measurement aims to capture the chest wall micromotion induced by the human respiratory and cardiac activities. Unfortunately, the existing VS measurement methods using bioradar

Shigehiro Yasui, Yutaka Hatakeyama, Yoshiyasu Okuhara

We discuss the stochastic process of creation and annihilation of particles, i.e., the $A^{n} \rightleftarrows B$ process in which $n$ particles $A$s and one particle $B$ are transformed to each other. Considering the case that the stochastic fluctuations are dependent on the numbers of $A$ and $B$, we apply the Langevin equation for the stochastic time-evol

Yefeng Yuan, Yuhong Liu, Liang Cheng

The rapid advancements in generative AI and large language models (LLMs) have opened up new avenues for producing synthetic data, particularly in the realm of structured tabular formats, such as product reviews. Despite the potential benefits, concerns regarding privacy leakage have surfaced, especially when personal information is utilized in the training d

Naoki Seto

Using the proposed space gravitational wave detector LISA, we will be able to measure the geometrical configurations of $\sim 10^4$ close white dwarf binaries in our Galaxy. The obtained data will be an entirely new resource to examine the randomness of their orbital orientations. Partly motivated by a recent reported on the systematic alignments of bulge pl

Ming Cheng, Jun Lin, Xintong Dong, Shaoping Lu

Distributed optical fiber acoustic sensing (DAS) is a rapidly-developed seismic acquisition technology with advantages of low cost, high resolution, high sensitivity, and small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by DAS system due to some factors, including optical fiber damage and inferior coupl

Yangcen Liu, Ziyi Liu, Yuanhao Zhai, Wen Li

Weakly-supervised temporal action localization (WTAL) aims to recognize and localize action instances with only video-level labels. Despite the significant progress, existing methods suffer from severe performance degradation when transferring to different distributions and thus may hardly adapt to real-world scenarios . To address this problem, we propose t

Lingxiao Long, Yunguo Jiang

In $\phi^6$ theory, the resonance scattering structure is triggered by the so-calls delocalized modes trapped between the $\bar{K}K$ pair. The frequencies and configurations of such modes depend on the $\bar{K}K$ half-separation 2$a$, can be derived from the Schr\"{o}dinger-like equation. We propose to use the periodic boundary conditions to connect the loca

Yuling Jiao, Lican Kang, Huazhen Lin, Jin Liu

This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{\"o}dinger bridge diffusion model in latent space as the framework for theoretical exploration in this domain. Our approach commences with the pre-training of an encoder-decoder architecture usin

M Jyothi Kiran, Venkatesh Chebolu, Goutam Das, Raja Datta

The challenge of optimal Routing and Spectrum Assignment (RSA) is significant in Elastic Optical Networks. Integrating adaptive modulation formats into the RSA problem - Routing, Modulation Level, and Spectrum Assignment - broadens allocation options and increases complexity. The conventional RSA approach entails predetermining fixed paths and then allocatin

Soumyadeep Roy, Aparup Khatua, Fatemeh Ghoochani, Uwe Hadler

GPT-4 demonstrates high accuracy in medical QA tasks, leading with an accuracy of 86.70%, followed by Med-PaLM 2 at 86.50%. However, around 14% of errors remain. Additionally, current works use GPT-4 to only predict the correct option without providing any explanation and thus do not provide any insight into the thinking process and reasoning used by GPT-4 o

Yixuan Li, Xuelin Liu, Xiaoyang Wang, Bu Sung Lee

The ability to distinguish whether an image is generated by artificial intelligence (AI) is a crucial ingredient in human intelligence, usually accompanied by a complex and dialectical forensic and reasoning process. However, current fake image detection models and databases focus on binary classification without understandable explanations for the general p

Wenhao Liang, Jiaqi An, Zeyu Li, Yafei Ren

We propose to realize the quantum anomalous Hall effect (QAHE) in two-dimensional compensated antiferromagnets without net spin magnetization.} We consider antiferromagnetic MnBi$_2$Te$_4$ as a concrete example. \textcolor{blue}{By breaking the parity-time ($\mathcal{PT}$) symmetry of even-layer MnBi$_2$Te$_4$, we find that the system can host the QAHE with

Zeinab Dehghan, Rudolf Golubich, Roman Höllwieser, Manfried Faber

Maximal Center Gauge (MCG) aims to detect center vortices by maximizing a gauge functional and then projecting onto the center elements of the respective group. The requirement for unrestricted maximization of the gauge functional has proven to be untenable because it was shown that it leads to an underestimation of the string tension. To counter this proble

Suman Bhattacharyya, Blesson Mathew, Gourav Banerjee, Sindhu G

In our previous study (Bhattacharyya et al., 2022), HD~54786, the optical counterpart of the MAXI J0709-159 system, was identified to be an evolved star, departing from the main sequence, based on comparisons with non-X-ray binary systems. In this paper, using color-magnitude diagram (CMD) analysis for High-Mass X-ray Binaries (HMXBs) and statistical t-tests

Christophe Andrieu, Mauro Camara Escudero, Chang Zhang

Assume interest is in sampling from a probability distribution $\mu$ defined on $(\mathsf{Z},\mathscr{Z})$. We develop a framework for sampling algorithms which takes full advantage of ODE numerical integrators, say $\psi\colon\mathsf{Z}\rightarrow\mathsf{Z}$ for one integration step, to explore $\mu$ efficiently and robustly. The popular Hybrid Monte Carlo

Jingdi Lei, Tianqi Kang, Yuluan Cao, Shiwei Ren

This paper represents an analysis on the momentum of tennis match. And due to Generalization performance of it, it can be helpful in constructing a system to predict the result of sports game and analyze the performance of player based on the Technical statistics. We First use hidden markov models to predict the momentum which is defined as the performance o

Xi Fang, Weigang Wang, Xiaoxin Lv, Jun Yan

The development of Large Language Models (LLM) and Diffusion Models brings the boom of Artificial Intelligence Generated Content (AIGC). It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies. The content generated by AIGC methods is driven by the cr

Julien Monteil, Volodymyr Vaskovych, Wentao Lu, Anirban Majumder

For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance. In thi

C. H. Zhang, Z. Song

Exceptional points (EPs), as an exclusive feature of a non-Hermitian system, support coalescing states to be alternative stable state beyond the ground state. In this work, we explore the influence of non-Hermitian impurities on the dynamic formation of condensate states in one-, two-, and three-dimensional extended Bose-Hubbard systems with strong on-site i

Gevorg Mnatsakanyan

The Malmquist-Takenaka (MT) system is a complete orthonormal system in $H^2(\mathbf{T})$ generated by an arbitrary sequence of points $a_n$ in the unit disk with $\sum_n (1-|a_n|) = \infty$. The point $a_n$ is responsible for multiplying the $n$th and subsequent terms of the system by a M\"obius transform taking $a_n$ to $0$. One can recover the classical tr

Jun Lyu, Shanshan Li, He Zhang, Yang Zhang

Incremental and parallel builds performed by build tools such as Make are the heart of modern C/C++ software projects. Their correct and efficient execution depends on build scripts. However, build scripts are prone to errors. The most prevalent errors are missing dependencies (MDs) and redundant dependencies (RDs). The state-of-the-art methods for detecting

Saeed Haddadi, Mehrdad Ghominejad, Artur Czerwinski

We study the quantumness of gravitational cat states in correlated dephasing channels. Our focus is on exploring how classical correlations between successive actions of a dephasing channel influence the decoherence of two gravitational cats (two qubits) at a thermal regime. The results show that the quantum coherence, local quantum Fisher information, and B

Libor Vojáček, Mairbek Chshiev, Jing Li

Germanium Telluride (GeTe), identified as a ferroelectric Rashba semiconductor, is a promising candidate for future electronic devices in computing and memory applications. However, its ferroelectric switching on a microscopic scale remains to be understood. Here, we propose that the migration of a domain wall can be the mechanism that mediates the ferroelec

Khuyagbaatar Batsuren, Ekaterina Vylomova, Verna Dankers, Tsetsuukhei Delgerbaatar

The popular subword tokenizers of current language models, such as Byte-Pair Encoding (BPE), are known not to respect morpheme boundaries, which affects the downstream performance of the models. While many improved tokenization algorithms have been proposed, their evaluation and cross-comparison is still an open problem. As a solution, we propose a combined

Minghao Yue, Anna-Christina Eilers, Tonima Tasnim Ananna, Christos Panagiotou

Recent James Webb Space Telescope (JWST) observations have revealed a population of compact extragalactic objects at $z\gtrsim4$ with red near-infrared colors, which have been dubbed as ``Little Red Dots" (LRDs). The spectroscopically-selected LRDs exhibit broad H$\alpha$ emission lines, which likely indicates that type-I active galactic nuclei (AGN) are har

Jingqi Kang, Tongtong Wu, Jinming Zhao, Guitao Wang

Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. Traditional ASR systems often overlook the interplay between these events, focusing solely on content, even though the interpretation of dialogue can vary with environmental context. This paper tackles two primary challenges in speech event

Pengcheng Sun, Erwu Liu, Rui Wang

The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference

Zirui Zang, Ahmad Amine, Rahul Mangharam

Estimating ego-pose from cameras is an important problem in robotics with applications ranging from mobile robotics to augmented reality. While SOTA models are becoming increasingly accurate, they can still be unwieldy due to high computational costs. In this paper, we propose to solve the problem by using invertible neural networks (INN) to find the mapping

Jagang Park, Kyungmin Lee, Ruo-Yang Zhang, Hee-Chul Park

Over the last few decades, the predominant strategies for controlling spontaneous emission have involved tailoring the spatial surroundings of quantum emitters or atoms to create resonant or spatially periodic photonic structures. However, the rise of time-varying photonics has prompted a reevaluation of spontaneous emission in dynamically changing environme

Changheon Han, Suhyun Lee, Minsam Ko

In the composition process, selecting appropriate single-instrumental music sequences and assigning their track-role is an indispensable task. However, manually determining the track-role for a myriad of music samples can be time-consuming and labor-intensive. This study introduces a deep learning model designed to automatically predict the track-role of sin

Lucy Jiang

Activism can take a multitude of forms, including protests, social media campaigns, and even public art. The uniqueness of public art lies in that both the act of creation and the artifacts created can serve as activism. Furthermore, public art is often site-specific and can be created with (e.g., commissioned murals) or without permission (e.g., graffiti ar

Soutik Ghosal

The Receiver Operating Characteristic (ROC) curve stands as a cornerstone in assessing the efficacy of biomarkers for disease diagnosis. Beyond merely evaluating performance, it provides with an optimal cutoff for biomarker values, crucial for disease categorization. While diverse methodologies exist for threshold estimation, less attention has been paid to

Soura Sana, Bankim C. Mandal

This paper explores the convergence behavior of two waveform relaxation algorithms, namely the Dirichlet-Neumann and Neumann-Neumann Waveform Relaxation algorithms, for an optimal control problem with a sub-diffusion partial differential equation (PDE) constraint. The algorithms are tested on regular 1D domains with multiple subdomains, and the analysis focu

Guangyin Bao, Qi Zhang, Zixuan Gong, Jialei Zhou

Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailored to each subject. The personalization limits the broader applicability of brain visual decoding in real-wor

Lanxin He, Zheng Wang, Yongming Huang

The Langevin sampling method relies on an accurate score matching while the existing massive multiple-input multiple output (MIMO) Langevin detection involves an inevitable singular value decomposition (SVD) to calculate the posterior score. In this work, a massive MIMO sampling detection strategy that leverages the denoising diffusion model is proposed to n

Viktoriia Bilet, Oleksiy Dovgoshey

Let $\mathbf{X}$ be a class of metric spaces and let $\mathbf{P}_{\mathbf{X}}$ be the set of all $f:[0, \infty)\to [0, \infty)$ preserving $\mathbf{X},$ $(Y, f\circ\rho)\in\mathbf{X}$ whenever $(Y, \rho)\in\mathbf{X}.$ For arbitrary subset $\mathbf{A}$ of the set of all metric preserving functions we show that the equality $\mathbf{P}_{\mathbf{X}}=\mathbf{A}

Yuan Zhou, Rose Qingyang Hu, Yi Qian

Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adversarial attacks. This paper delves into backdoor attacks targe

Ahmadreza Eslaminia, Yuquan Meng, Klara Nahrstedt, Chenhui Shao

Ultrasonic metal welding (UMW) is a key joining technology with widespread industrial applications. Condition monitoring (CM) capabilities are critically needed in UMW applications because process anomalies significantly deteriorate the joining quality. Recently, machine learning models emerged as a promising tool for CM in many manufacturing applications du

Chenxi Yang, Yujia Liu, Dingquan Li, Yan Zhong

Deep neural networks have demonstrated impressive success in No-Reference Image Quality Assessment (NR-IQA). However, recent researches highlight the vulnerability of NR-IQA models to subtle adversarial perturbations, leading to inconsistencies between model predictions and subjective ratings. Current adversarial attacks, however, focus on perturbing predict

Polina M. Sheverdyaeva, Gustav Bihlmayer, Silvio Modesti, Vitaliy Feyer

Bismuth produces different types of ordered superstructures on the InAs(100) surface, depending on the growth procedure and coverage. The (2x1) phase forms at completion of a Bi monolayer and consists of a uniformly oriented array of parallel lines of Bi dimers. Scanning tunneling and core level spectroscopies demonstrate its metallic character, in contrast

Ze Liu, Zhi-Fan Zhang, Zhen-Gang Zhu, Gang Su

The geometric properties of parameter space are mostly described by Berry curvature and quantum metric, which are the imaginary and real part of quantum geometric tensor, respectively. In this work, we calculate the dressed Berry curvature and quantum metric containing eight Feynman diagrams, which are proportional to the leading-order of the concentration o

Mustafa Doga Dogan, Eric J. Gonzalez, Karan Ahuja, Ruofei Du

Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital,

Junpu Wang, Guili Xu, Chunlei Li, Guangshuai Gao

Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing. Although existing reconstruction-based methods have achieved promising results, they still face two problems: poor distinguishable information in image reconstruction and well abnormal regeneration caused by model under-regula

Yunyi Zhao, Zhang Wei, Qingyu Yan, Man-Fai Ng

Battery health monitoring and prediction are critically important in the era of electric mobility with a huge impact on safety, sustainability, and economic aspects. Existing research often focuses on prediction accuracy but tends to neglect practical factors that may hinder the technology's deployment in real-world applications. In this paper, we address th

MJ Johns, Eunsol Sol Choi, Derusha Baskaran

Sustainable food is among the many challenges associated with climate change. The resources required to grow or gather the food and the distance it travels to reach the consumer are two key factors of an ingredient's sustainability. Food that is grown locally and is currently "in-season" will have a lower carbon footprint, but when dining out these details u

Shibsankar Si, Alekha C. Nayak, Pravin Kumar Natwariya

We calculate the $\mu$- and \textit{y}-type spectral distortions of Cosmic Microwave Background (CMB), taking a non-standard interaction between baryons and viscous dark matter. Using the CMB spectral distortion observations, we can constrain any exotic mechanism that may change the energy of the CMB photon, leading to a CMB spectrum distortion. Depending on

Maitreya Shelare, Neha Shigvan, Atharva Satam, Poonam Sonar

The field of remote-sensing image classification has seen immense progress with the rise of convolutional neural networks, and more recently, through vision transformers. These models, with their self-attention mechanism, can effectively capture global relationships and long-range dependencies between the image patches, in contrast with traditional convoluti

Samudra Dasgupta, Travis S. Humble

We investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of non-stationary noise, which is an obstacle to achieving accurate observable estimates. Leveraging Bayesian methods, we design a strategy to enhance PEC stability and accuracy. Our experiments using a 5-qubit implementation of the Bernstein-Vazirani algorithm an

Takaya Kawakatsu

Extracting table contents from documents such as scientific papers and financial reports and converting them into a format that can be processed by large language models is an important task in knowledge information processing. End-to-end approaches, which recognize not only table structure but also cell contents, achieved performance comparable to state-of-

Fang Liu, Bosheng Ding, Chong Guan, Zhang Wei

Adult learning is increasingly recognized as a crucial way for personal development and societal progress. It however is challenging, and adult learners face unique challenges such as balancing education with other life responsibilities. Collecting feedback from adult learners is effective in understanding their concerns and improving learning experiences, a

A. Ravlić, T. Nikšić, Y. F. Niu, P. Ring

Collective nuclear excitations, like giant resonances, are sensitive to nuclear deformation, as evidenced by alterations in their excitation energies and transition strength distributions. A common theoretical framework to study these collective modes, the random-phase approximation (RPA), has to deal with large dimensions spanned by all possible particle-ho

Guohao Wang, Ting Liu, Hongqiang Lyu, Ze Liu

As a prevalent and dynamically regulated epigenetic modification, 5-formylcytidine (f5C) is crucial in various biological processes. However, traditional experimental methods for f5C detection are often laborious and time-consuming, limiting their ability to map f5C sites across the transcriptome comprehensively. While computational approaches offer a cost-e

Weijie Sun, Ryan M. Dewey, Xianzhe Jia, Jim M. Raines

This study investigates the escape of Mercury's sodium-group ions (Na+-group, including ions with m/q from 21 to 30 amu/e) and their dependence on true anomaly angle (TAA), i.e., Mercury's orbital phase around the Sun, using measurements from MESSENGER. The measurements are categorized into solar wind, magnetosheath, and magnetosphere, and further divided in

Xi Wang, Yichen Peng, Heng Fang, Yilin Wang

In controllable generation tasks, flexibly manipulating the generated images to attain a desired appearance or structure based on a single input image cue remains a critical and longstanding challenge. Achieving this requires the effective decoupling of key attributes within the input image data to achieve representations accurately. Previous works have conc

Kewei Yuan, Qiurong Zhao, Yang Xu, Xiao Zhang

In view of the fact that most of the existing machine translation evaluation algorithms only consider the lexical and syntactic information, but ignore the deep semantic information contained in the sentence, this paper proposes a computational method for evaluating the semantic correctness of machine translations based on reference translations and incorpor

Jing Zhang, Sheng Gao, Xin Feng, Hongwei Yang

Unmanned aerial vehicles (UAVs) with flexible deployment contribute to enlarging the distance of information transmission to mobile users (MUs) in constrained environment. However, due to the high mobility of both UAVs and MUs, it is challenging to establish an accurate beam towards the target MU with high beam gain in real-time. In this study, UAV base stat

Xiao-Chuan Xu, Chuan-Fu Yang, Natalia Pavlovna Bondarenko

In this work, we study the inverse spectral problems for the Sturm-Liouville operators on [0,1] with complex coefficients and a discontinuity at $x=a\in(0,1)$. Assume that the potential on (a,1) and some parameters in the discontinuity and boundary conditions are given. We recover the potential on (0,a) and the other parameters from the eigenvalues. This is

Fariba Jafari Horestani, M. Mehdi Owrang O

In this study, we delve into the intricate relationships between diabetes and a range of health indicators, with a particular focus on the newly added variable of income. Utilizing data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), we analyze the impact of various factors such as blood pressure, cholesterol, BMI, smoking habits, and more

Meng Li, Jingjiang Bi, Nan Wang

In this paper, we innovatively develop uniform/variable-time-step weighted and shifted BDF2 (WSBDF2) methods for the anisotropic Cahn-Hilliard (CH) model, combining the scalar auxiliary variable (SAV) approach with two types of stabilized techniques. Using the concept of $G$-stability, the uniform-time-step WSBDF2 method is theoretically proved to be energy-

Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava

Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learn

Zhiqi Shao, Michael G. H. Bell, Ze Wang, D. Glenn Geers

Traffic flow prediction, a critical aspect of intelligent transportation systems, has been increasingly popular in the field of artificial intelligence, driven by the availability of extensive traffic data. The current challenges of traffic flow prediction lie in integrating diverse factors while balancing the trade-off between computational complexity and t

Yutao Wang, Giorgio Adamo, Son Tung Ha, Jingyi Tian

Generation and manipulation of exciton polaritons with controllable spin could deeply impact spintronic applications, quantum simulations, and quantum information processing, but is inherently challenging due to the charge neutrality of the polariton and the device complexity it requires. In this work, we demonstrate electrical generation of spin-polarized e

Mengzhang Fan, Yaohua Wang, Xiao Zhang

In this paper, we study the nonexistence of nontrivial time-periodic solutions of the Dirac equation in Kerr-Newman-(A)dS spacetime. In the non-extreme Kerr-Newman-dS spacetime, we prove that there is no nontrivial $L^p$ integrable Dirac particle for arbitrary $(\lambda,p)\in \mathbb{R}\times[2,+\infty)$. In the extreme Kerr-Newman-dS and extreme Kerr-Newman

Tomoyuki Yamakami

Nonuniform families of polynomial-size finite automata and pushdown automata respectively have strong connections to nonuniform-NL and nonuniform-LOGCFL. We examine the behaviors of unambiguous and co-nondeterministic computations produced by such families of automata operating multiple counters, where a counter is a stack using only a single non-bottom symb

Shuhei Yonehara

The notions of coK\"{a}hler manifolds and 3-cosymplectic manifolds are odd-dimensional analogues of the ones of K\"{a}hler manifolds and hyperK\"{a}hler manifolds, respectively. In this paper, we obtain reduction theorems of coK\"{a}hler manifolds and 3-cosymplectic manifolds. We show that K\"{a}hler and coK\"{a}hler (hyperK\"{a}hler and 3-cosymplectic) redu

Shyam Varahagiri, Aryaman Sinha, Shiv Ram Dubey, Satish Kumar Singh

In recent years, Vision Transformers (ViTs) have shown promising classification performance over Convolutional Neural Networks (CNNs) due to their self-attention mechanism. Many researchers have incorporated ViTs for Hyperspectral Image (HSI) classification. HSIs are characterised by narrow contiguous spectral bands, providing rich spectral data. Although Vi

Dinesh Khurana, T. Y. Lam

A ring element $\,a\in R\,$ is said to be of {\it right stable range one\/} if, for any $\,t\in R$, $\,aR+tR=R\,$ implies that $\,a+t\,b\,$ is a unit in $\,R\,$ for some $\,b\in R$. Similarly, $\,a\in R\,$ is said to be of {\it left stable range one\/} if $\,R\,a+R\,t=R\,$ implies that $\,a+b't\,$ is a unit in $\,R\,$ for some $\,b'\in R$. In the last two de

Sebastián Navarrete, Bryan J. Pinargote, Wladimir E. Banda-Barragán

The interstellar medium (ISM) is a key ingredient of galaxies and their evolution, consisting of multiphase, turbulent dust and gas. Some of the star-forming regions in our Galaxy originate from cloud-cloud and wind-cloud collisions, which generate shock waves that change the physical and chemical properties of the gas. We utilise our own python-based shock-

Sanjit Bhowmick, Deepak Kumar Dalai

An additive code is an $\mathbb{F}_q$-linear subspace of $\mathbb{F}_{q^m}^n$ over $\mathbb{F}_{q^m}$, which is not a linear subspace over $\mathbb{F}_{q^m}$. Linear complementary pairs (LCP) of codes have important roles in cryptography, such as increasing the speed and capacity of digital communication and strengthening security by improving the encryption

Michael D. Perlman

A {\it pure significance test} (PST) tests a simple null hypothesis $H_f:Y\sim f$ {\it without specifying an alternative hypothesis} by rejecting $H_f$ for {\it small} values of $f(Y)$. When the sample space supports a proper uniform pmf $f_\mathrm{unif}$, the PST can be viewed as a classical likelihood ratio test for testing $H_f$ against this uniform alter

Marcus Khuri, Hari Kunduri

We prove the spacetime Penrose inequality for asymptotically flat $2(n+1)$-dimensional initial data sets for the Einstein equations, which are invariant under a cohomogeneity one action of $\mathrm{SU}(n+1)$. Analogous results are obtained for asymptotically hyperbolic initial data that arise as spatial hypersurfaces in asymptotically Anti de-Sitter spacetim

Zekai Li, Yanxia Qin, Qian Liu, Min-Yen Kan

We propose Iterative Facuality Refining on Informative Scientific Question-Answering (ISQA) feedback\footnote{Code is available at \url{https://github.com/lizekai-richard/isqa}}, a method following human learning theories that employs model-generated feedback consisting of both positive and negative information. Through iterative refining of summaries, it pr

Kiminad A. Mamo, Ismail Zahed

We introduce a string-based parametrization for nucleon quark and gluon generalized parton distributions (GPDs) valid at all skewness values. The conformal moments of the GPDs are expressed as sums of the spin-j nucleon A-form factor, and the skewness-dependent spin-j nucleon D-form factor. This representation, which fulfills the polynomiality condition (due

Xiaoli Tang, Han Yu, Xiaoxiao Li, Sarit Kraus

Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders (i.e., data consumers, data owners, and the auc

Pedro Gabriel Fernández Dalgo, Oscar Jarrín

In this research, the Cauchy problem of the 3D viscous Boussinesq system is studied considering an initial temperature with negative Sobolev regularity. Precisely, we construct local in time mild solutions to this system where the temperature term belongs to Sobolev spaces of negative order. Our main contribution is to show how the coupled structure of the B

Pragya Sharma, Tolga Atalay, Hans-Andrew Gibbs, Dragoslav Stojadinovic

5G mobile networks leverage Network Function Virtualization (NFV) to offer services in the form of network slices. Each network slice is a logically isolated fragment constructed by service chaining a set of Virtual Network Functions (VNFs). The Network Repository Function (NRF) acts as a central OpenAuthorization (OAuth) 2.0 server to secure inter-VNF commu

Solomon Ojo, Juwon Onasanya, Morad Benamara, Bothina Hamad

Monolayers of ReS2 were grown by a chemical vapor deposition technique on SiO2/Si substrates and investigated at room temperature by using micro-Raman, micro-photoluminescence (PL) and absorbance spectroscopies. The Raman scattering spectrum exhibits several phonon modes that were confirmed by the computation analysis based on the density functional theory.

Seamus Somerstep, Yuekai Sun, Ya'acov Ritov

Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we compare employers that anticipate the strategic response of a labor force with employers that do not. We show through a combination of theory and experiment that

Yuyan Shi, Jialu Ma, Jin Yang, Shasha Wang

Medical image segmentation plays an important role in many image-guided clinical approaches. However, existing segmentation algorithms mostly rely on the availability of fully annotated images with pixel-wise annotations for training, which can be both labor-intensive and expertise-demanding, especially in the medical imaging domain where only experts can pr

Thomas Trask

The study in interaction patterns between students in on-campus and MOOC-style online courses has been broadly studied for the last 11 years. Yet there remains a gap in the literature comparing the habits of students completing the same course offered in both on-campus and MOOC-style online formats. This study will look at browser-based usage patterns for st

Feibo Jiang, Li Dong, Siwei Tu, Yubo Peng

Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges related to security and privacy. Federated Learning (FL), with its decentralized architecture, offers enhanced data privacy protection. Nevertheless, when integrated with LLMs, FL sti

Hengyu Mu, Jian Guo, Chong Han, Lijuan Sun

With the increasing emphasis on user privacy protection, biometric recognition based on federated learning have become the latest research hotspot. However, traditional federated learning methods cannot be directly applied to finger vein recognition, due to heterogeneity of data and open-set verification. Therefore, only a few application cases have been pro