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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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A large-scale study of academic specialization and sex effects on Corsi block performance

Frontiers in Education. 2025. Vol. 10. Article 1639086.
Malykh S., Anna Pavlova, Malykh A., Adamovich T., Tikhomirova T.

This study aimed to investigate the relationship between sex, educational specialization (science, technology, engineering, and mathematics (STEM) vs. non-STEM), and performance on the Corsi block-tapping task (CBT). Behavioral outcomes included the total number of correct responses, mean reaction time (RT), and mean RT for correct responses. The sample comprised 5,455 first-year Russian university students (mean age = 18.5; 62% female). Data were analyzed using rank-based analysis of variance (ANOVA) and hierarchical linear regression models. On average, male students outperformed female students in terms of both accuracy and response speed, while STEM students achieved higher scores than non-STEM students but did not differ in RT. Notably, sex differences in the number of correct responses disappeared within educational groups, whereas sex differences in RT persisted among educational groups, regardless of educational specialization. Regression analysis revealed that mean RT (correct responses), age, sex, and educational specialization collectively explained approximately 20% of the variance in performance accuracy. The predictive contribution of educational specialization exceeded that of sex, while the interaction between these variables was not significant. These findings suggest that, while both educational specialization and sex contribute to individual differences in visuospatial working memory performance, educational specialization emerges as a slightly more influential predictor of accuracy than sex, particularly in the number of correct responses. In contrast, reaction time appears to be more consistently associated with sex-related differences, irrespective of educational specialization. The results highlight the importance of considering both cognitive and contextual factors in the assessment of spatial working memory.

Research target: Psychology Education
Language: English
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Keywords: reaction timesex differencesSTEM EDUCATIONCorsi block-tapping taskvisuospatial working memoryeducational specialization
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