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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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Коварные слова и где они обитают

С. 1011–1024.
Iomdin B., Морозов Д. А.

Educational texts for children have two distinctly differing purposes: their readers must understand them and at the same time learn new words from them. It seems important and useful to be able to automatically detect words that may be unfamiliar to children of different ages. A challenging task is to identify words that readers perceive as familiar and understandable, but in fact understand them incorrectly. We propose a metric, called word deceptiveness, which is based on surveying and calculated as the product of the number of those respondents who mark the word as familiar by the number of those who correctly determine its meaning. We conducted a series of experiments and discovered several deceptive words in Russian. Several hypothetical mechanisms for the emergence of such words have been identified. In general, these are closeness to other, more familiar linguistic units: words, morphemes and word formation models. Future work will include an endeavor to learn to identify deceptive words on the basis of various linguistic factors.

Language: Russian
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Keywords: semanticsсемантикакорпусная лингвистикаcorpus linguisticscomplexityсложностьвнутренняя форма словапаронимыsemantic complexityсемантическая сложностьword awarenessparonymsinner formword segmentabilityизвестность словчленимость слов

In book

Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17–20 июня 2020 г.)
Т. 2: дополнительный том. Вып. 19. , Издательский центр «Российский государственный гуманитарный университет», 2020.
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