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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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Роль внимания в преодолении ограничений пространственного статистического научения

Экспериментальная психология. 2025. Т. 18. № 1. С. 54–69.
Деева Т. М., Kozlov D.

Context and relevance.
Statistical learning is ability to extract and learn regularities from the sensory environment. If these regularities concern with the spatial arrangement of objects in relation to each other we say about visual spatial statistical learning. When we have some hierarchical structure as a pattern, information about the whole set or about some of its subsets can be learned. For example, only pairwise connections between elements can be learned. In a row of experiments on statistical learning it has been demonstrated that when global information about a set of elements is learned, information about subsets is blocked, and vice versa — learning of embedded structure is accompanied by blocking of information about the whole set.

Objective.
We were interested in the situation where attention is directed to some elements of a complex stimuli more often than to others. The experiment examined whether, in this case, global information about the set of elements would be retained, or only about the subset to which attention was more frequently directed.

Methods and materials.
Subjects (N=104) performed a search task for the target element of complex stimuli. All the stimuli were composed by the same rule. The frequency of directing attention to one or another element of the stimuli was varied by special instructions. Just after the learning phase subjects completed a series of two-alternative forced choice tests with new correct and incorrect complete and incomplete stimuli.

Results.
We found that correct complete stimuli and correct subsets of more attended elements were appeared more familiar than stimuli with disrupted patterns.

Conclusions.
We demonstrate in our experiment the possibility of learning spatial information about both the global pattern and its substructure. The results are discussed in terms of two types of statistical learning, attention-dependent (explicit) and attention-independent (implicit).

Research target: Psychology
Language: Russian
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Keywords: зрительное вниманиеимплицитное научениестатистическое научение
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