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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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Model-based Monitoring and Analysis of the Network Community Dynamics in a Textured State Space

P. 44–49.
Tsukanova O. A., Vishnyakova E., Maltseva S. V.

The basic model of a network community as a dynamic system is suggested. Evolutionary platform concept and its development by the “black box” principle are described. Evolutionary progress and also different types of the network community motion order in a state space are considered. Such system categories as a “space between the texture layers” and “evolution rate” are determined. Different metrics related to the network community graph features are analyzed. Experiments are conducted on the basis of the data, imported from the Twitter Network using NodeXL software.

Language: English
Full text
Keywords: network community dynamicstextured state spaceevolutionary platformmotion orderevolution rate

In book

Proceedings of the 16th IEEE Conference on Business Informatics
Vol. 1. , Los Alamitos: IEEE Computer Society, 2014.
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