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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Экспериментальное исследование реконфигурируемых интеллектуальных поверхностей с обратной связью от пользовательских устройств

Информационные процессы. 2023. Т. 4. № 23. С. 600–611.
Пойда А. И., Глинский К. А., Tyarin A., Kureev A., Khorov E.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology to enhance the throughput and coverage of wireless networks by adaptively adjusting the phase and amplitude of reflected signals. The conventional approach to configuring RIS relies on transmitting channel state information from a Mobile Station (MS) to a Base Station (BS), with the BS subsequently adjusting the RIS through the BS-RIS communication channel. However, this approach necessitates integrating RIS into the operator’s infrastructure, posing challenges for deployment in existing networks. This paper explores an alternative solution where RIS is configured using a connection with the MS. This approach allows deploying RIS without the need to inform the operator or upgrade the BS. To investigate the feasibility of proposed approach, an autonomous RIS prototype is developed for configuration without BS involvement. Experimental results are conducted in an existing cellular network using the prototype demonstrate a twofold increase in throughput in both uplink and downlink channels. The study reveals that the proposed RIS configuration mechanism can be applied in contention-based access networks, such as Wi-Fi. This alternative approach offers a flexible and efficient solution for deploying RIS technology in various wireless communication scenarios.

Research target: Electronics and Electrical Engineering
Language: Russian
DOI
Text on another site
Keywords: RIS5GLTELTE5Gреконфигурируемые интеллектуальные поверхности
Publication based on the results of:
Methods for data transmission quality improvement in wireless local area networks using external reconfigurable reflectors (2023)
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