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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Факторы и индикаторы теневой экономики России: эмпирический анализ региональных данных

Вопросы статистики. 2022. Т. 29. № 5. С. 17–34.
Арженовский С. В., Orlova Y., Semerikova E. V., Sidorova E.

The purpose of the research is to assess factors influencing the rescaling of the shadow economy in the Russian Federation (on the example of certain regions). The analysis was carried out using mathematical and statistical methods according to regional statistics for 2013–2019. In particular, a model of multiple indicators and factors (MIMIC) was built. It adapted to the panel data structure. Variables characterizing the tax burden, government regulation and the state of the labor market were used as factors. As indicators of the shadow economy – monetary variables and characteristics of the formal economy.

It is shown that in the conditions of economic difficulties there was a «compression» of the scale of the shadow economy in certain regions. According to the authors, a significant role in reducing the shadow component in the regional economy over the period under review was played by the improvement of the tax administration mechanism in the process of forming regional and local budgets (personal income tax, property taxes) as well as coordination of interdepartmental interaction in order to reliably determine the size of the taxable base. The positive role of preferential taxation regimes, which can be established by regional authorities, was proved. A significant impact on the scale of shadow activity of the structure of the regional economy and conditions in the labor market was discovered: a high share of the extractive industry and an increase in wages in the region compared to the average Russian level create incentives for participation in the formal economy and reducing the scale of the shadow economy.

The modeling results confirmed the relevance of shadow economy indicators: the larger size of the regional shadow economy entails less participation in the labor force and an increase in cash turnover.

The conclusions obtained from a new angle focus on the task of improving the investment climate, reducing pressure on entrepreneurship, and supporting small and medium-sized businesses.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: теневая экономикаMultiple Group MIMIC Modelроссийские регионыinvestment risksинвестиционные рискиshadow economytax burdenналоговая нагрузка Russian regionsMIMIC
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