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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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Социальные медиа: о чем и кому пишут их пользователи? Некоторые подходы к анализу данных

Прикладная эконометрика. 2017. № 3. С. 74–99.
Kotyrlo E.

Study of users and their segmentation, based on users’ preferred topics of discussion and their networking, is the unique opportunity offered by social networks. Variety of approaches to social media analysis based on social network analysis and text mining is summarized in the paper. It is extended by concentration index application and visualizing of the results of social network analysis.

The study of a model set exhibits that: 1) users can be successfully segmented on the base of their most mentioned topics, which is useful for a product placement and other commercial purposes; 2) distribution of number of posts by authors is highly uneven regardless to the topic of discussion; 3) users connected on-line typically live in the same geographical area; 4) users’ number of posts and centrality indices are correlated.

Research target: Sociology (including Demography and Anthropology Mathematics Philology and Linguistics
Priority areas: sociology IT and mathematics
Language: Russian
Full text
DOI
Text on another site
Keywords: анализ текстовсоциальные медиарегрессионный анализregression analysistext miningиндекс Джиниsocial network analysissocial network sitesGini coefficientсоциально-сетевой анализ
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