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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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Исследовательский проект как инструмент обучения методам анализа текста: предсказание класса поста в социальной сети

Компьютерные инструменты в образовании. 2018. № 3. С. 49–64.
Suvorova A., Смирнова К. Р., Будин Е. А., Тулупьева Т. В., Тулупьев А. Л., Абрамов М. В.

The article describes a student research project on predicting the class of a post on a social network based on its textual content. The features of the project are discussed as an integral part of the trajectory of teaching data analysis methods, including text analysis methods and tools that are often not included in machine learning courses. The formulation of the problem, the stages of its solution, the sequence of considering new methods as a way for solving students’ problems, as well as the used tool of the R environment are described. The possibilities of expanding the task and its modi1cations depending on the level of training of students are given.

Priority areas: IT and mathematics
Language: Russian
Full text
DOI
Keywords: социальные сетимашинное обучениеsocial networksклассификацияанализ текстаtext analysismachine learningclassificationProblem-based learningR languageязык Rпроблемно-ориентированное обучениеresearch automationавтоматизация исследований
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