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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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Разложение Лапласа для статистики Бартлетта–Нанда–Пиллая и оценка ошибки приближения”

Теория вероятностей и ее применения. 2023. Т. 68. № 4. С. 705–718.
Вакаки Х., В. В. Ульянов

Asymptotic expansions for the distribution function of Bartlett–Nand–Pillai statistics are constructed, provided that the null linear hypothesis is valid in a multidimensional linear model.
At the same time, computable estimates of the accuracy of the approximation are obtained
using the Laplace approximation method, which is generalized
to integrals of matrix-valued functions.

Research target: Mathematics Computer Science
Language: Russian
DOI
Keywords: математическая статистика
Publication based on the results of:
Structural learning and its applications (2023)
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