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News
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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Развитие методики ранней диагностики финансовых кризисов с использованием системы сигнальных экономических индикаторов

Финансовая аналитика: проблемы и решения. 2020. Т. 13. № 4. С. 362–382.
Яшина Н. И., Kashina O., Петров С. С., Прончатова-Рубцова Н. Н.

Subject. This article discusses new possibilities for diagnosing the state of the country's financial and economic system and monitoring the true signs of crisis phenomena. Objectives. The article aims to develop effective methods of diagnosing and predicting financial crises in Russia using a special system of economic indicators and digital analytical tools. Methods. For the study, we used the signaling approach, non-parametric and mathematical statistics, and econometric modeling. Results. The article proposes a developed methodology for diagnosing crisis events, and tested on the basis of a sample of official data of the Federal State Statistics Service, the Ministry of Finance of the Russian Federation, the Central Bank of the Russian Federation, and the exchange information of the world financial markets for 1998-2019. Conclusions. The study confirmed the prospects for using a signaling approach in combination with the binary choice model and econometric modeling methods to predict the onset of crisis events in the Russian economy. The proposed methodology for diagnosing financial market crises contributes to the development of scientific approaches to the analysis and forecasting of the price dynamics of financial markets and periods of their increased volatility. Its practical implementation provides information on emerging trends in financial and economic instability and assesses the likelihood of a financial crisis.

Research target: Economics and Management
Language: Russian
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Keywords: эконометрическое моделированиефинансовый рынокfinancial marketфинансовый кризисfinancial crisisэкономическая нестабильностьEconomic instabilityeconometric modellingsignal indicatorsсигнальный подход
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