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.
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.’
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.
Olga Blinova, Tarasov N., Frontiers in Artificial Intelligence 2022 Vol. 5 Article 1008530
This article proposes a hybrid model for the estimation of the complexity of legal documents in Russian. The model consists of two main modules: linguistic feature extractor and a transformer-based neural encoder. The set of linguistic metrics includes both non-specific metrics traditionally used to predict complexity, as well as style-specific metrics developed in order to ...
Blinova O. V., Мир русского слова 2022 № 2 С. 4–13
The paper describes the metrics-based model for assessing complexity of Russian legal texts. The architecture of the model implies the use of 130 metrics divided into following categories: “basic metrics”, “readability formulas”, “words of different part-of-speech classes”, “n-grams of part-of-speech tags”, “frequency of lemmas”, “word-building patterns”, “grammes”, “lexical and semantic features, multi-word expressions”, “syntactic features”, ...