Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Silver, T."'
Autor:
Wang, Xiao-Lin, Dou, S. X., Hossain, M. S. A., Cheng, Z. X., Liao, X. Z., Ghorbani, S. R., Yao, Q. W., Kim, J. H., Silver, T.
Publikováno v:
Physical Review B 81, 224514 (?2010?)
In this work, we present the following important results: 1) We introduce a new Si source, liquid SiCl4, which is free of C, to significantly enhance the irreversibility field (Hirr), the upper critical field (Hc2), and the critical current density (
Externí odkaz:
http://arxiv.org/abs/0903.3858
The U/n method is a well-established means of improving flux pinning and critical current performance in cuprate superconductors. The method involves the doping of the superconductor with 235U followed by irradiation with thermal neutrons to promote
Externí odkaz:
http://arxiv.org/abs/cond-mat/0304070
Autor:
Li, A. H., Wang, X. L., Ionescu, M., Soltonian, S., Horvat, J., Silver, T., Liu, H. K., Dou, S. X.
The fabrication, characterisation, and superconductivity of MgB2 thick films grown on stainless steel substrate were studied. XRD, SEM, and magnetic measurements were carried out. It was found that the MgB2 thick films can be fast formed by heating s
Externí odkaz:
http://arxiv.org/abs/cond-mat/0104501
Publikováno v:
In Physics Procedia 2015 78:247-254
Autor:
Silver, T. Michael1 michael.silver@ualberta.ca
Publikováno v:
Information Technology & Libraries. Mar2010, Vol. 29 Issue 1, p8-22. 15p. 6 Black and White Photographs, 6 Diagrams, 2 Charts.
Autor:
Levinson, Andrew, Silver, T.
Publikováno v:
BMJ: British Medical Journal, 2009 Jan 01. 338(7685), 52-52.
Externí odkaz:
https://www.jstor.org/stable/20511698
Publikováno v:
Scopus-Elsevier
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a sim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8a4e46c993f9d17a8b7380dd1401911
http://arxiv.org/abs/2001.08299
http://arxiv.org/abs/2001.08299
Publikováno v:
Scopus-Elsevier
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65593e99b93f17dec40bb48119ac44b6
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