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Modern problems in condensed matter sciences. Pokrovscy, . Bogdan, . Amsterdam, Oxford, New York: North Holland 1986Google Scholar. Preprint of the Institute of Physics of Metals of the Ural Science Centre of the Academy of Sciences of the USSR, 80/4, 1980 (in Russian)Google Scholar.

This book comprises the first systematic exposition of various physical aspects of the orientation of electron and nuclear spins in semiconductors by optical means. Read on the Scribd mobile app. Download the free Scribd mobile app to read anytime, anywhere.

Cite this publication. University of Zurich.

Founded in 1997, BookFinder. Coauthors & Alternates. Learn More at LibraryThing. S. E. Trullinger at LibraryThing.

Электронная книга "Solitons", . Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Solitons" для чтения в офлайн-режиме.

Solitons (Modern Problems in Condensed Matter Sciences). 0444870024 (ISBN13: 9780444870025).

Trullinger S E, Zakharov V E, Pokrovsky V L (Eds) Solitons (Modern Problems in Condensed Matter Sciences) Vol. 17 (Amsterdam: North-Holland, 1986). Kerner B S, Osipov V V Avtosolitony. Lokalizovannye Sil’no-neravnovesnye Oblasti v Odnorodnykh Dissipativnykh Sistemakh (. Nauka, 1991); Per. na angl. ya. Kerner B S, Osipov V V Autosolitons: A New Approach To Problems Of Self-Organization And Turbulence (Dordrecht: Kluwer Acad. Remoissenet M Waves Called Solitons : Concepts And Experiments (Berlin: Springer-Verlag, 1994).

Pokrovskij (ed., Solitons, Modern Problems in Condensed Matter Sciences, Vol. 17. Amsterdam et. North-Holland.

Talapov, The fractional charges in quantum hall effect and the possibility of their observation, Solid State Communs. 57 (3), 159-160 (1986). Zakharov, Shock waves propagating on solitons on the surface of a fluid (in Russian), Izvestiya vysshikh uchebnykh zavedenii. Radiofizika, 29(9), 1073–1079 (1986) . Pokrovskij (ed.

Among modern numerical techniques, neural networks, which show great power in approximating functions and extracting features of big data, are now attracting much interest. where Z h,v e−E(h,v) is the partition function. The general BM is very dicult to train, and therefore. We take the Boltzmann machine representation as a prototypical example to illustrate various aspects of the states of a neural network. some restricted architecture on the BM is introduced.