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News
September 4, 2026
‘Hedgehog Versus ‘Relatives: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
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.’

 

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GraphTyper: Вывод типов из графовой репрезентации кода посредством нейронных сетей

Труды Института системного программирования РАН. 2024. Т. 36. № 4. С. 69–80.
Арутюнов Г. А., Avdoshin S. M.

Although software development is mostly a creative process, there are many scrutiny tasks. As in other industries, there is a trend for automation of routine work. In many cases, machine learning and neural networks have become a useful assistant in that matter. Programming is not an exception: GitHub has stated that Copilot is already used to write up to 30% of code in the company. Copilot is based on Codex, a Transformer model trained on code as a sequence. However, a sequence is not a perfect representation for programming languages. In this work, we claim and demonstrate that by combining the advantages of Transformers and graph representations of code, it is possible to achieve excellent results even with comparably small models.

Research target: Computer Science
Language: Russian
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Keywords: neural networksgraphstransformersabstract syntax tree
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