In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dynamic information about the current state of a large scientific workflow, which could be generalized to other types of applications. The specific task to be modeled is the time needed for transferring a file from an experimental facility to a data center. The key idea of our approach is to find recent past data transfer events that match the current event in some ways. Tests showed that we could identify recent events matching some recorded properties and reduce the prediction error by about 12% compared to the similar models with only static features. We additionally explored an application specific technique to extract information about the data production process, and was able to reduce the average prediction error by 44%.
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
Southern Methodist University（南卫理公会大学）；Marshall Space Flight Center, NASA（美国国家航空航天局马歇尔太空飞行中心）；Science Mission Directorate, NASA Headquarters（美国国家航空航天局总部科学任务局）；Jet Propulsion Laboratory, NASA（美国国家航空航天局喷气推进实验室）
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Department of Neuroscience, Baylor College of Medicine（神经科学系，贝勒医学院）；Department of Applied and Computational Mathematics and Statistics, University of Notre Dame（应用与计算数学和统计学系，圣母大学）；Department of Statistics, Columbia University（统计学系，哥伦比亚大学）；Zuckerman Institute & Irving Institute, Columbia University（Zuckerman研究所与Irving研究所，哥伦比亚大学）
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Fermilab（费米实验室）；Northwestern University（西北大学）；Columbia University（哥伦比亚大学）；MIT（麻省理工学院）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织（CERN））
Workflow Provenance in the Lifecycle of Scientific Machine Learning
Inria, Univ. Montpellier, CNRS & LIRMM, France（法国国家信息与自动化研究所，蒙彼利埃大学，法国国家科学研究中心与LIRMM）；Federal University of Rio de Janeiro, Brazil（里约热内卢联邦大学）；IBM Research（IBM研究院）
Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation
University of Tennessee（田纳西大学）；Oxford Instruments Asylum Research（牛津仪器庇护研究公司）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；National Cheng Kung University（国立成功大学）；the Pennsylvania State University（宾夕法尼亚州立大学）
AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；NSF National Optical-Infrared Astronomy Research Laboratory（国家光学红外天文研究实验室）；University of Virginia（弗吉尼亚大学）；SLAC National Accelerator Laboratory（斯坦福直线加速器实验室）；Texas Advanced Computing Center（德克萨斯高级计算中心）
Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research
Université Côte d’Azur（蔚蓝海岸大学）；CNRS（法国国家科学研究中心）；ICN（化学研究所）；Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d’Azur（蔚蓝海岸跨学科人工智能研究所）；Inria（法国国家信息与自动化研究所）；I3S（信息系统与软件工程实验室）
