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Subject
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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Диагностика тяжести симптомов депрессии при помощи объяснимого искусственного интеллекта

Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика). 2023. Т. 514. № 2. С. 242–249.
Shalileh S., Koptseva A., Shishkovskaya T., Khudyakova M., Dragoy O.

This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group having the most acceptable results. Regarding our second objective, we determined the importance of the features of our best model using the SHapley Additive exPlanations approach: our findings showed that the fourth Mel-frequency cepstral coefficients, harmonic difference, and shimmer are the most important features.

Research target: Medical Technologies Health Studies Psychology
Language: Russian
DOI
Text on another site
Keywords: explainable artificial intelligenceобъяснимый искусственный интеллект artificial intelligencedepression recognition acoustic features regressionраспознавание депрессииакустические признаки регрессия
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