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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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Сценарное моделирование движения беспилотных транспортных средств в искусственной дорожной сети с использованием FLAME GPU

Искусственные общества. 2021. Т. 16. № 1. С. 1–23.
Akopov A. S., Beklaryan A.

This article presents a model of the ground autonomous vehicles (AVs) motion in the Artificial Road Network (ARN) belonging to the "Manhattan Lattice" type with the implementation of the large-scale agent-based modeling framework FLAME GPU. The most important scenarios of the traffic situation development are investigated, in particular, which are associated with reducing visibility on the roads, especially in conditions of unusual behaviour of some agents of the traffic system, e.g. the unexpected appearance of obstacles such as agent-pedestrians and chaotic maneuvering of usual (i.e. manned) vehicles (MVs) having abnormal characteristics. A new approach to designing large-scale agent-based transportation simulations based on ARNs with a complex configuration and implementation using supercomputer technologies is proposed.

Research target: Computer Science Mathematics
Language: Russian
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Keywords: агентное моделированиебеспилотные транспортные средстваAutonomous vehiclesagent-based modellingFLAME GPUartificial transport systemsFLAME GPUискусственные транспортные системы
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