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arXiv 2024年

作者： Zhou, Zhihan Wu, Weimin Ho, Harrison Wang, Jiayi Shi, Lizhen Davuluri, Ramana V. Wang, Zhong Liu, Han Department of Computer Science Northwestern University United States Department of Statistics and Data Science Northwestern University United States School of Natural Sciences University of California at Merced United States Department of Biomedical Informatics Stony Brook University United States Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory United States Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory United States

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 23 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. Model, codes, and data is publicly available at https://***/MAGICS-LAB/DNABERT_S. © 2024, CC BY.

关键词： Genome

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Lab Color Model and Contrast Modification as a Tool for Improving the Quality of Medical Images

Lab Color Model and Contrast Modification as a Tool for Impr...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality Visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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Equitable Research PRAXIS: A Framework for Health informatics Methods

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Yearbook of Medical informatics 2022年第1期31卷 307-316页

作者： Veinot, Tiffany C. Clarke, Phillipa J. Romero, Daniel M. Buis, Lorraine R. Dillahunt, Tawanna R. Vydiswaran, V.G. Vinod Beals, Ashley Brown, Lindsay Richards, Olivia Williamson, Alicia Antonio, Marcy G. School of Information University of Michigan Ann Arbor MI United States Department of Health Behavior and Health Education School of Public Health University of Michigan Ann Arbor MI United States Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI United States Institute for Social Research University of Michigan Ann Arbor MI United States Division of Computer Science and Engineering College of Engineering and Computer Science University of Michigan Ann Arbor MI United States Center for the Study of Complex Systems College of Literature Science and the Arts University of Michigan Ann Arbor MI United States Department of Family Medicine University of Michigan Medical School Ann Arbor MI United States Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States

Objectives: There is growing attention to health equity in health informatics research. However, the literature lacks a comprehensive framework outlining critical considerations for health informatics research with marginalized groups. Methods: Literature review and experiences from nine equity-focused health informatics conducted in the United States and Canada. Studies focus on disparities related to age, disability or chronic illness, gender/sex, place of residence (rural/urban), race/ethnicity, sexual orientation, and socioeconomic status. Results: We found four key equity-related methodological considerations. To assist informaticists in addressing equity, we contribute a novel framework to synthesize these four considerations: PRAXIS (Participation and Representation, Appropriate methods and interventions, conteXtualization and structural competence, Investigation of Systematic differences). Participation and representation refers to the necessity for meaningful participation of marginalized groups in research, to elevate the voices of marginalized people, and to represent marginalized people as they are comfortable (e.g., asset-based versus deficit-based). Appropriate methods and interventions mean targeting methods, instruments, and interventions to reach and engage marginalized people. Contextualization and structural competence mean avoiding individualization of systematic disparities and targeting social conditions that (re-)produce inequities. Investigation of systematic differences highlights that experiences of people marginalized according to specific traits differ from those not so marginalized, and thus encourages studying the specificity of these differences and investigating and preventing intervention-generated inequality. We outline guidance for operationalizing these considerations at four research stages. Conclusions: This framework can assist informaticists in systematically addressing these considerations in their research in four research s

关键词： conceptual frameworks health disparities Health equity marginalized populations research methods

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Predicting Interaction Shape of Soft Continuum Robots using Deep Visual Models

Predicting Interaction Shape of Soft Continuum Robots using ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yunqi Huang Abdulaziz Y. Alkayas Jialei Shi Federico Renda Helge Wurdemann Thomas George Thuruthel Department of Computer Science University College London London UK Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi UAE Department of Mechanical Engineering University College London London UK Department of Mechanical Engineering Mechatronics in Medicine The Hamlyn Centre for Robotic Surgery Imperial College London UK Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Soft continuum robots, characterized by their inherent compliance and dexterity, are increasingly pivotal in applications requiring delicate interactions with the environment such as the medical field. Despite their advantages, challenges persist in accurately modeling and controlling their shape during interactions with surrounding objects. This is because of the difficulty in modeling the large degrees of freedom in soft-bodied objects that become more active during interactions. In this study, we present a deep visual model to predict the interaction shapes of a soft continuum robot in contact with surrounding objects. By formulating this task as a forward-statics problem, the model uses the initial state images containing the object configuration and future actuation values to predict interactive state images of the robot under this actuation condition. We developed and tested the model in both simulated and physical environments, explored the model’s predictive capabilities using monocular and binocular views, and tested the model’s generalization ability on different datasets. Our results show that deep learning methods are a promising tool for solving the complex problem of predicting the shape of a soft continuum robot interacting with the environment, requiring no prior knowledge about the system dynamics and explicit mapping of the environment. This study paves the way for future explorations in robot-environment interaction modeling and the development of more adaptable interaction shape control strategies.

关键词： Adaptation models Visualization Accuracy Uncertainty Shape Robot kinematics Predictive models Data collection Data models Continuum robots

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Application of artificial intelligence in reverse logistics: A bibliometric and network analysis

Supply Chain Analytics

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Supply Chain Analytics 2024年 7卷

作者： Bhowmik, Oyshik Chowdhury, Sudipta Ashik, Jahid Hasan Mahmud, GM Iqbal Khan, Md Muzahid Hossain, Niamat Ullah Ibne Department of Industrial & Production Engineering National Institute of Textile Engineering and Research (NITER) Department of Industrial Engineering and Management Khulna University of Engineering & Technology Department of Systems and Industrial Engineering University of Arizona Engineering Management Department College of Engineering and Computer Science Arkansas State University

Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further. © 2024 The Authors

关键词： Artificial Intelligence Bibliometric analysis Network analysis Reverse logistics Systematic literature review

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Experience in Teaching and Engaging computer science and computer Information systems Students in Active Learning

Experience in Teaching and Engaging Computer Science and Com...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Weizheng Gao Olumide Malomo Somasheker Akkaladevi Ephrem Eyob Adeyemi A. Adekoya Shanzhen Gao Julian D. Allagan Jianning Su Department of Mathematics and Computer Science College of Natural Sciences and Mathematics West Virginia State University Institute WV USA Department of Computer Information Systems Reginald F Lewis College of Business Virginia State University Petersburg VA USA Department of Mathematics Computer Science and Engineering Technology Elizabeth City State University Elizabeth City NC USA Department of Mathematics and Sciences Midway University Midway KY

Active learning refers to a broad range of teaching and pedagogical strategies that engage students as “partners-in-progress” participants in their learning during class time with their instructors. We discuss some real-life experiences related to students' engagement in an early introductory course to computer science. In this paper, commonly known best teaching practices such as Active Learning, Problem-Based Learning, and Integrated Learning are implemented and proven to be effective given students' survey responses.

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network

arXiv

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arXiv 2024年

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States New York University Graduate School of Art & Science New York United States University of Southern California Viterbi School of Engineering Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States Carnegie Mellon University School of Computer Science Pittsburgh United States University of Houston Cullen College Of Engineering Houston United States

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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Video-based Person Re-identification Based on Distributed Cloud Computing

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 110-120页

作者： Chengyan Zhong Xiaoyu Jiang Guanqiu Qi College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Intelligence Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222USA

Person re-identification has been a hot research issues in the field of computer *** recent years,with the maturity of the theory,a large number of excellent methods have been ***,large-scale data sets and huge networks make training a time-consuming *** the same time,the parameters and their values generated during the training process also take up a lot of computer ***,we apply distributed cloud computing method to perform person re-identification *** distributed data storage method,pedestrian data sets and parameters are stored in cloud *** speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution ***,we assign different layers of the re-identification network to different nodes to complete the training in the way of model *** comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

关键词： person re-identification distributed cloud computing data redundancy mechanism

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Deep Learning of First-Order Nonlinear Hyperbolic Conservation Law Solvers

SSRN

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SSRN 2023年

作者： Morand, Victor Müller, Nils Weightman, Ryan Keimer, Alexander Piccoli, Benedetto Bayen, Alexandre M. Ecole Polytechnique Palaiseau France Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States Max Planck Institute for Software Systems Saarland Informatics Campus Germany Max Planck Institute for Intelligent Systems Germany Department of Mathematics Rutgers University NJ United States Department of Mathematics FAU Erlangen-Nürnberg Germany

In this contribution, we study the numerical approximation of scalar conservation laws by computational optimization of the numerical flux function in a first-order finite volume method. The cell-averaging inherent to finite volume schemes presents a challenge in the design of such numerical flux functions toward achieving high solution accuracy. Dense neural networks, as well as (piecewise) polynomials, serve as function classes for the numerical flux. Using a parametrization of such a function class, we optimize the numerical flux with respect to its solution accuracy on a set of Riemann problems for which we have closed-form solutions. By design, our resulting first-order finite volume schemes are *** show empirically that the proposed schemes can achieve higher accuracy than the Godunov and EnquistOsher method on a wide range of *** proposed method can be applied to the inverse problem, i.e., the data-driven synthesis of macroscopic physical laws for real-world flux phenomena. © 2023, The Authors. All rights reserved.

关键词： Finite volume method

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