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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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The Open System for Storing and Processing of a Dataset of Combinational Circuits

Proceedings of the Institute for System Programming of the RAS. 2023. Vol. 35. No. 5. P. 81–90.
D.A. Miachin, V.P. Pugach, S.S. Avdeiuk, V.V. Zunin, A.Y. Romanov

This paper presents an open-source software for generation, storage, and analysis of combinational circuits. The previously created methods for generating combinational circuits have been optimized, and a dataset has been formed. The generation of combinational circuits is carried out on various devices. The application implements the possibility to combine the generated datasets into a single storage (Synology Drive), as well as analyze the fault tolerance of combinational circuits using various methods for their evaluation. New possible methods for assessing combinational circuits’ reliability using machine learning are proposed.

Research target: Engineering and Technology Electronics and Electrical Engineering
Language: English
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Keywords: машинное обучениеreliabilityотказоустойчивостьcombinational circuitsкомбинационные схемыmachine learningDatasetclient-server applicationдатасетклиент-серверное приложение
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