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June 19, 2026
HSE Researchers Determine Which Internet Users Are More Likely to Fact-Check
Researchers at HSE University examined the strategies employed by Russian internet users to verify unreliable information and the factors that motivate them to do so. The study found that more than half of users who encounter potentially false information online attempt to verify it by locating the original source. The likelihood of fact-checking is influenced by several factors, including age, place of residence, social status, information literacy skills, and the use of AI. The findings have been published in Monitoring of Public Opinion: Economic and Social Changes.
June 5, 2026
'Im Used to Producing Distilled Knowledge'
Ivan Rubachev works in a HSE University laboratory established jointly with Yandex Research, where he focuses on machine learning with tabular data. In this interview with the HSE Young Scientists project, he discusses why following a vibe can be better than goal-setting, explains the concept of the Neural Turing Machine, and argues why withholding scientific knowledge is counterproductive.
June 17, 2026
Population Lifespan Is Governed by Mathematical Laws
Researchers at HSE University and MSU have established a universal law governing the time to extinction of a population in a random environment. Their analysis of the evolution of branching processes—complex probabilistic systems—shows that, regardless of the initial population size, extinction follows strict mathematical laws. The results have been published in the Journal of Applied Probability.

 

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Variational Autoencoders for Precoding Matrices with High Spectral Efficiency

Ch. 22. P. 315–326.
Bobrov E., Markov A., Panchenko S., Vetrov D.

Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE). We propose a computationally efficient algorithm for sampling precoding matrices with minimal loss of quality compared to the optimal precoding. In addition to VAE, we use the conditional variational autoencoder (CVAE) to build a unified generative model. Both of these methods are able to reconstruct the distribution of precoding matrices of high SE by sampling latent variables. This distribution obtained using VAE and CVAE methods is described in the literature for the first time.

Language: English
DOI
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Keywords: optimizationMIMOprecodingVAEDLSINR

In book

Mathematical Optimization Theory and Operations Research: Recent Trends. 21st International Conference, MOTOR 2022, Petrozavodsk, Russia, July 2–6, 2022, Revised Selected Papers
Springer, 2022.
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