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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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?

Interval Semi-supervised LDA: Classifying Needles in a Haystack

P. 265–274.
Bodrunova S., Koltsov S., Koltsova O., Nikolenko S. I., Shimorina A.

An important text mining problem is to fi nd, in a large collection of texts, documents related to speci c topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to nd the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain prede ned sets of keywords (that de ne the topics researchers are interested in) are restricted to speci c intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.

Language: English
Full text
Text on another site
Keywords: text miningtopic modelinglatent Dirichlet allocation
Publication based on the results of:
Social and Political Processes Online (2013)

In book

Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI 2013)
* I: Advances in Artificial Intelligence and Its Applications. , Berlin: Springer, 2013.
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