Zobrazeno 1 - 10
of 3 459
pro vyhledávání: '"P. A. Petrenko"'
Autor:
Fomin, A. A., Kozlov, G. G., Petrenko, M. V., Petrov, M. Yu., Smirnov, D. S., Zapasskii, V. S.
For an isotropic medium, a magnetic field applied in the Voigt geometry affects the optical properties in the second order only, so its effect is much weaker than in the Faraday geometry. In this work, we show that, under resonant excitation well bey
Externí odkaz:
http://arxiv.org/abs/2409.05411
Autor:
Hier, Daniel B., Obafemi-Ajayi, Tayo, Olbricht, Gayla R., Burns, Devin M., Petrenko, Sasha, Wunsch II, Donald C.
Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatt
Externí odkaz:
http://arxiv.org/abs/2403.20246
Autor:
Obikhod, Tetiana, Petrenko, Ievgenii
The cosmological observations of gravitational lenses, cosmic microwave background, rotation speed of stars in galaxies confirm the existence of about 27% dark matter in the Universe. The nature of these particles is unknown, however, there are theor
Externí odkaz:
http://arxiv.org/abs/2401.02528
Autor:
Ohlendorf, Rahel, Spachmann, Sven, Fischer, Lukas, Carstens, Frederik Leon, Brunt, Daniel, Balakrishnan, Geetha, Petrenko, Oleg A., Klingeler, Rüdiger
We report high-resolution dilatometry studies on single crystals of the Shastry-Sutherland-lattice magnet NdB$_4$ supported by specific heat and magnetometry data. Our dilatometric studies evidence pronounced anomalies at the phase boundaries which i
Externí odkaz:
http://arxiv.org/abs/2312.00715
The monograph discusses certain aspects of modern real-world problems facing humanity, which are much more challenging than scientific ones. Modern science is unable to solve them in a fundamental way. Vernadsky's noosphere thesis, in fact, appeals t
Externí odkaz:
http://arxiv.org/abs/2311.04910
Autor:
Islam, M., d'Ambrumenil, N., Khalyavin, D. D., Manuel, P., Orlandi, F., Ollivier, J., Hatnean, M. Ciomaga, Balakrishnan, G., Petrenko, O. A.
We investigate the magnetic properties of the monoclinic D-type $\rm{Er_2Si_2O_7}$ with a distorted honeycomb lattice using powder and single crystal neutron scattering techniques, as well as single crystal magnetisation measurements. The powder neut
Externí odkaz:
http://arxiv.org/abs/2310.09268
Autor:
Sergey Bespalyy, Gulmira Alnazarova, Vincenzo Nunzio Scalcione, Pavel Vitliemov, Aleksandr Sichinava, Alexandr Petrenko, Andrey Kaptsov
Publikováno v:
Discover Sustainability, Vol 5, Iss 1, Pp 1-18 (2024)
Abstract Education for sustainable development in universities is shaping the agenda in this area, demanding continuous improvement in quality. There is a trend towards integrating sustainability issues and the implementation of sustainable developme
Externí odkaz:
https://doaj.org/article/e9e821bf851c47dd80013757a6cb847c
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Iss 3, Pp 1247-1256 (2024)
This paper presents the assessment of the formation of economic integration in the EAEU as the basis for improving the management of knowledge and information systems in the digital economy. The goal of the paper was to find the specifics of the infl
Externí odkaz:
https://doaj.org/article/0efd9f91f23d4c8899b90254748a761b
Autor:
Huang, Zhehui, Batra, Sumeet, Chen, Tao, Krupani, Rahul, Kumar, Tushar, Molchanov, Artem, Petrenko, Aleksei, Preiss, James A., Yang, Zhaojing, Sukhatme, Gaurav S.
Reinforcement learning (RL) has shown promise in creating robust policies for robotics tasks. However, contemporary RL algorithms are data-hungry, often requiring billions of environment transitions to train successful policies. This necessitates the
Externí odkaz:
http://arxiv.org/abs/2306.09537
Autor:
Batra, Sumeet, Tjanaka, Bryon, Fontaine, Matthew C., Petrenko, Aleksei, Nikolaidis, Stefanos, Sukhatme, Gaurav
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement Learning (QD-RL) is an emerging research area that blends the best asp
Externí odkaz:
http://arxiv.org/abs/2305.13795