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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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?

Putting OAC-triclustering on MapReduce

P. 47–58.
Зудин С., Gnatyshak D. V., Ignatov D. I.
In our previous work an efficient one-pass online algorithm for triclustering of binary data (triadic formal contexts) was proposed. This algorithm is a modified version of the basic algorithm for OAC-triclustering approach; it has linear time and memory complexities. In this paper we parallelise it via map-reduce framework in order to make it suitable for big datasets. The results of computer experiments show the efficiency of the proposed algorithm; for example, it outperforms the online counterpart on Bibsonomy dataset with ≈ 800, 000 triples.  
Language: English
Full text
Text on another site
Keywords: трикластеризацияtriclusteringбольшие данныеbig dataMapReduceMapReduce
Publication based on the results of:
­­­Data mining based on lattices of closed descriptions and applied ontologies (2015)

In book

Proceedings of the Twelfth International Conference on Concept Lattices and Their Applications Clermont-Ferrand, France, October 13-16, 2015
Vol. 1466. , Clermont-Ferrand: CEUR Workshop Proceedings, 2015.
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