Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Aristana Scourtas"'
Autor:
Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian Foster
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-9 (2022)
Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovatio
Externí odkaz:
https://doaj.org/article/d4f06a97624a4ca6b9b33142632fd44c
Autor:
Ryan Jacobs, Lane E Schultz, Aristana Scourtas, KJ Schmidt, Owen Price-Skelly, Will Engler, Ian Foster, Ben Blaiszik, Paul M Voyles, Dane Morgan
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045051 (2024)
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision
Externí odkaz:
https://doaj.org/article/8dd31ac1a0fe4b92972448920f032228
Autor:
Jingrui Wei, Carter Francis, Dane Morgan, KJ Schmidt, Aristana Scourtas, Ian Foster, Ben Blaiszik, Paul M Voyles
Publikováno v:
Microscopy and Microanalysis. 28:3094-3096
The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are hi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c05fbaebf57d69df44879a80ad19e7e8
http://arxiv.org/abs/2207.10173
http://arxiv.org/abs/2207.10173
Autor:
Aristana Scourtas, K J Schmidt, Paul M. Voyles, Marcus Schwarting, Xiang-Guo Li, Ryan Jacobs, Dane Morgan, Ben Blaiszik
Publikováno v:
The Journal of chemical physics. 155(15)
Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning m
Autor:
Aristana, Scourtas, Jonathan C, Dudley, William R, Brugge, Abdurrahman, Kadayifci, Mari, Mino-Kenudson, Martha B, Pitman
Publikováno v:
Cancer cytopathology. 125(3)
Mucinous cystic neoplasms (MCNs) of the pancreas present a management conundrum. The majority are benign but all are resected due to their malignant potential. Recent studies have recommended nonsurgical management. In the current study, the authors
Publikováno v:
Diagnostic cytopathology. 43(6)