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检索条件"机构=Computational and Data-Enabled Science and Engineering Program"
199 条 记 录,以下是61-70 订阅
排序:
Analysis and computation of some tumor growth models with nutrient: From cell density models to free boundary dynamics
arXiv
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arXiv 2018年
作者: Liu, Jian-Guo Tang, Min Wang, Li Zhou, Zhennan Department of Mathematics Department of Physics Duke University School of Mathematics and Institute of Natural Sciences MOE-LSC Shanghai JiaoTong University Department of Mathematics Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Beijing International Center for Mathematical Research Peking University
In this paper, we study the tumor growth equation along with various models for the nutrient component, including the in vitro model and the in vivo model. At the cell density level, the spatial availability of the tu... 详细信息
来源: 评论
Fast sequential clustering in riemannian manifolds for dynamic and time-series-annotated multilayer networks
TechRxiv
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TechRxiv 2020年
作者: Ye, Cong Slavakis, Konstantinos Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States State University of New York at Buffalo China The Neuroscience Program UB SUNY United States The Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program UB SUNY United States The Department of Psychology Drexel University PA19104 United States The Perelman School of Medicine University of Pennsylvania PA19104 United States
This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nod... 详细信息
来源: 评论
Generative models of simultaneously heavy-tailed distributions of interevent times on nodes and edges
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Physical Review E 2020年 第5期102卷 052303-052303页
作者: Elohim Fonseca dos Reis Aming Li Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260 USA Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan
Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processe... 详细信息
来源: 评论
Network Intrusion Detection System Using Principal Component Analysis Algorithm and Decision Tree Classifier
Network Intrusion Detection System Using Principal Component...
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International Conference on computational science and computational Intelligence (CSCI)
作者: Oyeyemi Osho Sungbum Hong Tor A. Kwembe Department of Computational Data-Enabled Science and Engineering(CDS&#x0026 E) Jackson State University Jackson MS USA Department of Electrical &#x0026 Computer Engineering and Computer Science Jackson State University Jackson MS USA Department of Mathematics &#x0026 Statistical Sciences Jackson State University Jackson MS USA
Network Intrusion Detection Systems (IDS) have become expedient for network security and ensures the safety of all connected devices. Network Intrusion Detection System (IDS) alludes to observing network data informat... 详细信息
来源: 评论
End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems  32
End-to-end symmetry preserving inter-atomic potential energy...
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32nd Conference on Neural Information Processing Systems, NeurIPS 2018
作者: Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential ene... 详细信息
来源: 评论
Geospatial Intelligence for Individual Crop Detection and Anomaly Monitoring
Geospatial Intelligence for Individual Crop Detection and An...
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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)
作者: Freda Elikem Dorbu Leila Hashemi-Beni Department of Computational Data Science and Engineering North Carolina A&T State University Department of Built Environment Geomatics Program North Carolina A&T State University
Acquisition of geospatial data by UAV has been acknowledged as an effective method of attaining reliable and quick high-resolution remote sensing data for analysis and decision-making in different applications such as...
来源: 评论
Advances of machine learning in molecular modeling and simulation
arXiv
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arXiv 2019年
作者: Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical su... 详细信息
来源: 评论
Generative models of simultaneously heavy-tailed distributions of inter-event times on nodes and edges
arXiv
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arXiv 2020年
作者: dos Reis, Elohim Fonseca Li, Aming Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Zoology University of Oxford Oxford United Kingdom Department of Biochemistry University ofOxford Oxford United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States
Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processe... 详细信息
来源: 评论
Metrics for benchmarking and uncertainty quantification: Quality, applicability, and a path to best practices for machine learning in chemistry
arXiv
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arXiv 2020年
作者: Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States
This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarki... 详细信息
来源: 评论
Small inter-event times govern epidemic spreading on networks
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Physical Review Research 2020年 第2期2卷 023163-023163页
作者: Naoki Masuda Petter Holme Department of Mathematics University at Buffalo State University of New York Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New York 14260-5030 USA Tokyo Tech World Research Hub Initiative (WRHI) Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan
Many aspects of human and animal interaction, such as the frequency of contacts of an individual, the number of interaction partners, and the time between the contacts of two individuals, are characterized by heavy-ta... 详细信息
来源: 评论