Zobrazeno 1 - 10
of 15 956
pro vyhledávání: '"Sokolsky"'
Autor:
Marcinkevičius, Andrius
Publikováno v:
Literatūra / Literature. 64(2):8-27
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1105219
Autor:
Jockel, Joseph1 (AUTHOR), Sokolsky, Joel2 (AUTHOR)
Publikováno v:
American Review of Canadian Studies. Jun2022, Vol. 52 Issue 2, p193-203. 11p.
Publikováno v:
Кардиоваскулярная терапия и профилактика, Vol 21, Iss 4S (2022)
The article is dedicated to the 215th anniversary of the birth of Grigory Ivanovich Sokolsky, a well-known Russian clinician and scientist who made a huge contribution to the development of clinical diagnostics of diseases, phthisiology, internal dis
Externí odkaz:
https://doaj.org/article/de4562e4316842bbac70865f372226c4
Autor:
Андрюс Марцинкявичюс
Publikováno v:
Literatūra (Vilnius), Vol 64, Iss 2 (2022)
The article explores biography and various aspects of public activity of the Russian refugee from Lithuania, lawyer, professor of Russian language and literature at the University of South Florida Anatole Sokolsky (1993–2006). Memoirs and articles
Externí odkaz:
https://doaj.org/article/c7f415b7dea64e6e8f82343fe2403f83
Akademický článek
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Akademický článek
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Publikováno v:
Научно-практическая ревматология, Vol 56, Iss 1, Pp 127-130 (2018)
The paper is devoted to the anniversary – the 210th anniversary of the birth of the outstanding scientist and clinician Grigory Ivanovich Sokolsky. G.I. Sokolsky made a huge contribution to the development of rheumatology, internal medicine, pathof
Externí odkaz:
https://doaj.org/article/1c41452428e14636923a65cbbdf9973b
Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but current methods lack interpretability. We leverage common properties of existing methods and recent advances in generative models to introduce counterfactu
Externí odkaz:
http://arxiv.org/abs/2410.24178
Autor:
Park, Jean, Pugh, Sydney, Sridhar, Kaustubh, Liu, Mengyu, Yarna, Navish, Kaur, Ramneet, Dutta, Souradeep, Bernardis, Elena, Sokolsky, Oleg, Lee, Insup
Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine since high quality labeled data is often scarce. Data programming has
Externí odkaz:
http://arxiv.org/abs/2407.07982
Autor:
Telescope Array Collaboration, Abbasi, R. U., Abe, Y., Abu-Zayyad, T., Allen, M., Arai, Y., Arimura, R., Barcikowski, E., Belz, J. W., Bergman, D. R., Blake, S. A., Buckland, I., Cheon, B. G., Chikawa, M., Fujii, T., Fujisue, K., Fujita, K., Fujiwara, R., Fukushima, M., Furlich, G., Globus, N., Gonzalez, R., Hanlon, W., Hayashida, N., He, H., Hibi, R., Hibino, K., Higuchi, R., Honda, K., Ikeda, D., Inoue, N., Ishii, T., Ito, H., Ivanov, D., Iwasaki, A., Jeong, H. M., Jeong, S., Jui, C. C. H., Kadota, K., Kakimoto, F., Kalashev, O., Kasahara, K., Kasami, S., Kawakami, S., Kawata, K., Kharuk, I., Kido, E., Kim, H. B., Kim, J. H., Kim, S. W., Kimura, Y., Komae, I., Kuzmin, V., Kuznetsov, M., Kwon, Y. J., Lee, K. H., Lubsandorzhiev, B., Lundquist, J. P., Matsumiya, H., Matsuyama, T., Matthews, J. N., Mayta, R., Mizuno, K., Murakami, M., Myers, I., Nagataki, S., Nakai, K., Nakamura, T., Nishio, E., Nonaka, T., Oda, H., Ogio, S., Onishi, M., Ohoka, H., Okazaki, N., Oku, Y., Okuda, T., Omura, Y., Ono, M., Oshima, A., Oshima, H., Ozawa, S., Park, I. H., Park, K. Y., Potts, M., Pshirkov, M. S., Remington, J., Rodriguez, D. C., Rott, C., Rubtsov, G. I., Ryu, D., Sagawa, H., Saito, R., Sakaki, N., Sako, T., Sakurai, N., Sato, D., Sato, K., Sato, S., Sekino, K., Shah, P. D., Shibata, N., Shibata, T., Shikita, J., Shimodaira, H., Shin, B. K., Shin, H. S., Shinto, D., Smith, J. D., Sokolsky, P., Stokes, B. T., Stroman, T. A., Takagi, Y., Takahashi, K., Takamura, M., Takeda, M., Takeishi, R., Taketa, A., Takita, M., Tameda, Y., Tanaka, K., Tanaka, M., Tanoue, Y., Thomas, S. B., Thomson, G. B., Tinyakov, P., Tkachev, I., Tokuno, H., Tomida, T., Troitsky, S., Tsuda, R., Tsunesada, Y., Udo, S., Urban, F., Warren, D., Wong, T., Yamazaki, K., Yashiro, K., Yoshida, F., Zhezher, Y., Zundel, Z.
We report an estimation of the injected mass composition of ultra-high energy cosmic rays (UHECRs) at energies higher than 10 EeV. The composition is inferred from an energy-dependent sky distribution of UHECR events observed by the Telescope Array s
Externí odkaz:
http://arxiv.org/abs/2406.19287