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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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Развитие научной школы искусственного интеллекта в пермских университетах: история и научный приоритет (обзорная статья)

Прикладная математика и вопросы управления. 2018. № 4. С. 99–130.
Yasnitsky L.

The article is based on the annual Perm All-Russian scientific and practical conference "Artificial intelligence in solving urgent social and economic problems of the XXI century" (http://www.permai.ru/files/26.05.2018.pdf). It is also a brief overview of the results of the Perm branch of the Scientific Council of the Russian Academy of Sciences on the methodology of artificial intelli- gence, as well as several departments of the Perm state University, Perm state humanitarian pedagogi- cal University, National research University Higher school of Economics, Perm state medical University. The review covers the works that develop and apply the methods of artificial intelligence in the classical sense, i.e., those methods that simulate human intellectual activity by simulating natural mechanisms.

 

 

These are expert systems, genetic algorithms, neural networks, fuzzy mathematics. The scientific priori- ty of Perm scientists in the development of theoretical foundations and practical applications of artificial intelligence is emphasized.

Keywords: Artificial intelligence, neural network, expert system, genetic algorithm, theory, prac- tice, modeling, forecasting, optimization, recognition, data processing, knowledge extraction.

Research target: Computer Science Mathematics Mechanics and Mechanical Engineering Medical Technologies Clinical Medicine Psychology Economics and Management Education Sociology (including Demography and Anthropology Political Science, International Relations, and Public Administration Other Humanities
Priority areas: mathematics
Language: Russian
Full text
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Text on another site
Keywords: искусственный интеллектобзорartificial intelligenceискусственная нейронная сетьArtificial Neural Network (ANN)Scientific priorityнаучный приоритет
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