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arXiv 2020年

作者： Al Daas, Hussam Ballard, Grey Benner, Peter Department of Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Computer Science Department Wake Forest University Winston SalemNC United States

We present efficient and scalable parallel algorithms for performing mathematical operations for low-rank tensors represented in the tensor train (TT) format. We consider algorithms for addition, elementwise multiplication, computing norms and inner products, orthonormalization, and rounding (rank truncation). These are the kernel operations for applications such as iterative Krylov solvers that exploit the TT structure. The parallel algorithms are designed for distributed- memory computation, and we propose a data distribution and strategy that parallelizes computations for individual cores within the TT format. We analyze the computation and communication costs of the proposed algorithms to show their scalability, and we present numerical experiments that demonstrate their efficiency on both shared-memory and distributed-memory parallel systems. For example, we observe better single-core performance than the existing MATLAB TT-Toolbox in rounding a 2GB TT tensor, and our implementation achieves a 34× speedup using all 40 cores of a single node. We also show nearly linear parallel scaling on larger TT tensors up to over 10,000 cores for all mathematical operations. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Guidance of development,validation,and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

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Military Medical Research 2025年第5期 788-798页

作者： Wen Wang Ying-Hui Jin Mei Liu Qiao He Jia-Yue Xu Ming-Qi Wang Guo-Wei Li Bo Fu Si-Yu Yan Kang Zou Xin Sun Institute of Integrated Traditional Chinese and Western Medicine Chinese Evidence-Based Medicine and Cochrane China CenterWest China HospitalSichuan University NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan Sichuan Center of Technology Innovation for Real World Data Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan University Department of Health Research Methods Evidence and ImpactMcMaster University Center for Clinical Epidemiology and Methodology Guangdong Second Provincial General Hospital Biostatistics Unit Research Institute at St.Joseph's Healthcare Hamilton School of Computer Science and Engineering University of Electronic Science and Technology of China West China School of Public Health and West China Fourth Hospital Sichuan University

Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD).These studies rely on algorithms to identify specific health conditions (e.g.,diabetes or sepsis) for statistical ***,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study ***,these issues are often ***:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status (DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and ***,we conducted an empirical study on an algorithm for identifying *** on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the ***,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize ***:A standardized workflow for algorithm development,validation,and evaluation was *** by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study ***,13 good practice recommendations were formulated with detailed ***,a practical study on sepsis identification was included to demonstrate the application of this ***:The establishment of guidance is intended to aid researchers and clinician

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Correction to: A two-stage Bayesian semiparametricmodel for novelty detection with robust prior information

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Statistics and Computing 2022年第1期32卷 1-1页

作者： Denti, Francesco Cappozzo, Andrea Greselin, Francesca Department of Statistics and Computer Science University of California Irvine CA USA MOX - Laboratory for Modeling and Scientific Computing Department of Mathematics Politecnico di Milano Italy Department of Statistics and Quantitative Methods University of Milano-Bicocca Milan Italy

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MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

arXiv

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arXiv 2022年

作者： Wang, Xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist China Shenzhen International Graduate School China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT China Tsinghua University Beijing China Xi’an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Application of artificial intelligence and digital technologies in the organization of the educational process of specialists in the field of physics, engineering and metrology 2

Application of artificial intelligence and digital technolog...

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2nd International Scientific Conference on Metrological Support of Innovative Technologies, ICMSIT II-2021

作者： Shamina, S.V. Munister, V.D. Zolkin, A.L. Verbitskiy, R.A. Dragulenko, V.V. Department Natural Science Disciplines South Ural State Agrarian University Gagarin street 13 Troitsk Chelyabinsk region457100 Russia Department of Mathematical Methods and Computer-Aided Design Private Educational Institution Donetsk Academy of Transport Dzerzhinsky Street 7 Donetsk83086 Ukraine Department of Enterprise Economics State Educational Institution of Higher Professional Education Donetsk National Technical University Shibankova Street 2 Pokrovsk Donetsk region85300 Ukraine Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L.Tolstogo Street 23 Samara443010 Russia Natural Sciences Department Private Institution of Higher Education Medical University Reaviz Chapaevskaya Street 227 Samara443001 Russia Engineering Drawing Department Mirea - Russian Technological University Vernadsky Avenue 178 Moscow119454 Russia Department of Tractors Automobiles and Technical Mechanics Federal State Budgetary Educational Institution of Higher Education Kuban State Agrarian University Named after I.T. Trubilin Kalinina Street 13 Krasnodar350044 Russia

The article deals with the problems and the importance of modern advanced learning. A model of teaching for students mastering programs of secondary and higher vocational education, based on the terminal-matrix approach, which includes the integration of several related disciplines, is proposed. The importance of artificial intelligence methods in engineering, physical, engineering education is substantiated by the example of checking of research results. © Published under licence by IOP Publishing Ltd.

关键词： Engineering education

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Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use

arXiv

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arXiv 2024年

作者： Eacersall, Douglas Pretorius, Lynette Smirnov, Ivan Spray, Erika Illingworth, Sam Chugh, Ritesh Strydom, Sonja Stratton-Maher, Dianne Simmons, Jonathan Jennings, Isaac Roux, Rian Kamrowski, Ruth Downie, Abigail Thong, Chee Ling Howell, Katharine A. Library Services University of Southern Queensland Australia University of the Philippines Open University Philippines School of Curriculum Teaching and Inclusive Education Faculty of Education Monash University Melbourne Australia Graduate Research School University of Technology Sydney Australia School of Education University of Newcastle Australia Department for Learning and Teaching Enhancement Edinburgh Napier University Edinburgh United Kingdom School of Engineering & Technology CML-NET Research Centre Central Queensland University Australia Centre for Learning Technologies Stellenbosch University South Africa School of Nursing and Midwifery University of Southern Queensland Australia Writing Services University of Alberta Canada eResearch and Specialised Platforms Griffith University Australia UniSQ College University of Southern Queensland Australia Griffith Graduate Research School Griffith University Australia Institute of Computer Science and Digital Innovation UCSI University Malaysia Graduate Research Edith Cowan University Perth Australia

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: ‘Examine policies and guidelines’, ‘Think about social impacts’, ‘Harness understanding of the technology’, ‘Indicate use’, ‘Critically engage with outputs’, ‘Access secure versions’, and ‘Look at user agreements’. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI’s benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies. © 2024, CC BY.

关键词： Generative adversarial networks

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STEM course on circuit design accomplished to enhance scientific and technological capabilities of students

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Journal of Physics: Conference Series 2022年第1期2193卷

作者： V Vexler M Aibashev A Saipbekova I Fominykh R Bazhenov Department of Information Systems and Technologies in Education Saratov State University 83 Astrakhanskaya Saratov 410012 Russia Department of Accounting and Economic Analysis Osh State University 331 Lenina Osh 723500 Kyrgyz Republic Department of Applied Mathematics Computer Science Osh State University 331 Lenina Osh 723500 Kyrgyz Republic Department of Methods of Teaching Mathematics Computer Science and Natural Science Disciplines Mari State University 6 Eshkinina Yoshkar-Ola 424033 Russia Department of Information Systems and Mathematics Sholom-Aleichem Priamursky State University 70A Shirokaya Birobidzhan 679015 Russia

The aim of the research is to develop an interdisciplinary course of extracurricular activities integrating knowledge in science, technology, engineering and mathematics for students to develop their scientific and technological skills. 52 students from secondary schools of Engels, Yoshkar-Ola (Russia), Sholom-Aleichem Priamursky State University (Birobidzhan, Russia), Osh State University College (Osh, Kyrgyz Republic) took part in the educational experiment. The theme-based course Basic Concepts of Arduino Systems Engineering is developed based on the proprietary technology of simple projects of practical relevance. The projects render the general concept from knowledge to competence. The course consists of four modules: Start, Big Workshop/Hands-on Training, Go Ahead/Step forward, Group Design. Arduino applied in design modeling allows students to create electrical devices that would be productive in one or another area of life at a low cost. The course program is aimed at engaging students in innovative design technologies, computer programming and using robotic devices. The findings of the study were observed as part of an interview with students, educational achievement, subsequent STEM literacy test activities and the performance of game summarizing projects. The recommended method has shown the efficiency in mastering the required core set of skills of circuit design and computer programming.

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Computing pathways: A quantitative inquiry into the dynamic pathways of students in computing with gender comparisons

Computing pathways: A quantitative inquiry into the dynamic ...

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2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Kargarmoakhar, Maral Ross, Monique S. Hazari, Zahra Weiss, Mark A. Georgiopoulos, Michael Christensen, Ken Solis, Tiana Florida International University United States Department of Teaching and Learning STEM Transformation Institute Florida International University United States College of Engineering and Computing Florida International University MiamiFL United States OrlandoFL United States Department of Computer Science and Engineering University of South Florida United States School of Computing and Information Sciences Florida International University United States

The number of female students in computing fields remains low despite the millions of dollars spent on research for attracting more female students. In order to entice more female students to these male dominated fields, we first have to understand their pathways to CS and educational years we are losing female students. For the purposes of this study, we utilized the data from the Florida IT Paths (FLIT-PATH) project, an NSF funded study. Participants included approximately, 1650 students from three large public universities. The survey contained 39 questions on identity, field of study, and occupational interest during middle school, high school, and college. The responses gathered through the Qualtrics survey system and were analyzed in R by the research team. The research questions that guided this study were: 1- To what extent are female students interested in computing related fields at middle school, the beginning of high school, and the beginning of college? 2- How have these occupational pursuits changed over time? Do they differ for gender? The results of the study indicated a majority of female students that were attracted to computing fields during middle school remained in those fields during high school and college years. However, there was no significant flow from other majors to computing fields observed during the different educational years. © American Society for Engineering Education 2020.

关键词： Students

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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