Zobrazeno 1 - 10
of 11 870
pro vyhledávání: '"A. A. Nikulin"'
Publikováno v:
Вестник анестезиологии и реаниматологии, Vol 20, Iss 6, Pp 58-66 (2023)
The objective was to identify prognostic criteria for unfavorable outcome in pregnant women with severe and extremely severe forms of COVID-19 and to build a model for predicting clinical outcome.Materials and methods. The cohort single-center retros
Externí odkaz:
https://doaj.org/article/8a0a8f17d23042519d0b0f045a8cf390
Autor:
V. F. Bezhenar, I. A. Dobrovolskaya, I. M. Nesterov, A. V. Schegolev, A. N. Kucheryavenko, S. G. Meshchaninova, V. S. Pakin, A. A. Nikulin
Publikováno v:
Акушерство, гинекология и репродукция, Vol 17, Iss 1, Pp 75-91 (2023)
Aim: to asses an opportunity for predicting an unfavorable perinatal and maternal pregnancy outcome in severe novel coronavirus infection (NCI) COVID-19. Materials and Methods. A retrospective comparative study of the course and outcomes of pregnanci
Externí odkaz:
https://doaj.org/article/cf2157c039dc4b72b1ae977c8891ab1a
Autor:
Fradkin, I. M., Nikulin, A. V., Solodovchenko, N. S., Filonov, D. S., Baranov, D. G., Rybin, M. V., Samusev, K. B., Limonov, M. F., Dyakov, S. A., Gippius, N. A.
Dense lattices of photonic crystals can serve as artificial materials, with light propagation in these structures described by effective material parameters that surpass the capabilities of natural materials. In this study, we introduce a metamateria
Externí odkaz:
http://arxiv.org/abs/2412.04530
Autor:
Semkin, Valentin, Shabanov, Aleksandr, Kapralov, Kirill, Kashchenko, Mikhail, Sobolev, Alexander, Mazurenko, Ilya, Myltsev, Vladislav, Nikulin, Egor, Chernov, Alexander, Kameneva, Ekaterina, Bocharov, Alexey, Svintsov, Dmitry
Two-dimensional materials offering ultrafast photoresponse suffer from low intrinsic absorbance, especially in the mid-infrared wavelength range. Challenges in 2d material doping further complicate the creation of light-sensitive $p-n$ junctions. Her
Externí odkaz:
http://arxiv.org/abs/2411.06480
In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (
Externí odkaz:
http://arxiv.org/abs/2411.01958
Publikováno v:
Российский технологический журнал, Vol 9, Iss 2, Pp 22-34 (2021)
This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of m
Externí odkaz:
https://doaj.org/article/7d01dabb89c74c469010ab512fd0f9b8
Autor:
Citrin, Jonathan, Goodfellow, Ian, Raju, Akhil, Chen, Jeremy, Degrave, Jonas, Donner, Craig, Felici, Federico, Hamel, Philippe, Huber, Andrea, Nikulin, Dmitry, Pfau, David, Tracey, Brendan, Riedmiller, Martin, Kohli, Pushmeet
We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat transport, electron heat transport, particle transport, and current d
Externí odkaz:
http://arxiv.org/abs/2406.06718
Autor:
Beckmann, Arian, Stephani, Tilman, Klotzsche, Felix, Chen, Yonghao, Hofmann, Simon M., Villringer, Arno, Gaebler, Michael, Nikulin, Vadim, Bosse, Sebastian, Eisert, Peter, Hilsmann, Anna
Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured fro
Externí odkaz:
http://arxiv.org/abs/2405.08527
Autor:
Huang, Shengyi, Gallouédec, Quentin, Felten, Florian, Raffin, Antonin, Dossa, Rousslan Fernand Julien, Zhao, Yanxiao, Sullivan, Ryan, Makoviychuk, Viktor, Makoviichuk, Denys, Danesh, Mohamad H., Roumégous, Cyril, Weng, Jiayi, Chen, Chufan, Rahman, Md Masudur, Araújo, João G. M., Quan, Guorui, Tan, Daniel, Klein, Timo, Charakorn, Rujikorn, Towers, Mark, Berthelot, Yann, Mehta, Kinal, Chakraborty, Dipam, KG, Arjun, Charraut, Valentin, Ye, Chang, Liu, Zichen, Alegre, Lucas N., Nikulin, Alexander, Hu, Xiao, Liu, Tianlin, Choi, Jongwook, Yi, Brent
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to repro
Externí odkaz:
http://arxiv.org/abs/2402.03046
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined
Externí odkaz:
http://arxiv.org/abs/2312.13327