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News
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
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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.

 

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Clique detection with a given reliability

Annals of Mathematics and Artificial Intelligence. 2024.
Semenov D., Koldanov A. P., Koldanov P., Pardalos P. M.

In this paper we propose a new notion of a clique reliability. The clique reliability is understood as the ratio of the number of statistically significant links in a clique to the number of edges of the clique. This notion relies on a recently proposed original technique for separating inferences about pairwise connections between vertices of a network into significant and admissible ones. In this paper, we propose an extension of this technique to the problem of clique detection. We propose a method of step-by-step construction of a clique with a given reliability. The results of constructing cliques with a given reliability using data on the returns of stocks included in the Dow Jones index are presented.

Research target: Mathematics Computer Science
Language: English
Full text
DOI
Keywords: correlation cliquesThreshold graphNetworkFamily-wise error ratemultiple hypotheses testing procedures Level of significance
Publication based on the results of:
Network models, optimization and computational complexity (2024)
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