National Institute of Standards and Technologyにおける超伝導研究及び生活
By 尚弘 島影. 2001
Subfields: AI for Physical Sciences
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In AI for Physical Sciences
- Lacey Best-Rowden
- Xinyun Chen
- Dawn Song
- Sudipta Sengupta
- Michael Steiner
- Hu Han
- Ramkant Nevatia
- Thomas Schneider
Citations in the corpus
- AI and the Everything in the Whole Wide World Benchmark (2021, methodology): “The method of disaggregated analysis has since been adopted by a myriad of auditing and evaluation works [e.g. Raji and Buolamwini, 2019] and integrated into frameworks of standardized model reporting [Mitchell et al., 2019].”
- Research challenges to ultra‐efficient inorganic solid‐state lighting (2007, background): “The white-light spreadsheet-based simulator [69, 70] is quite general.”
- Spiritual leadership as a model for performance excellence: a study of Baldrige award recipients (2017, background): “In Building on Baldrige: American Quality for the twenty-first Century, the private Council on Competitiveness said, “More than any other program, the Baldrige Award is responsible for making quality a national priority and disseminating best practices across the United States” (NIST 2014).”
- Ascon v1.2: Lightweight Authenticated Encryption and Hashing (2021, unknown): “:3) functions used in SHA-2 [71,72].”
- Smart Grid — The New and Improved Power Grid: A Survey (2012, unknown): “Within the framework of SG, many management goals, which are difficult and possibly infeasible to realize in conventional power grids, become possible and easy.”