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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
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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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Navigating the Blessing–Curse Paradox: An Ecological Perspective of Cultivating a Culture of Responsible Generative AI Use in Higher Education

Ch. 16. P. 247–272.
Koroleva D., Jogezai N.

The rapid growth of artificial intelligence (AI), particularly generative artificial intelligence (GAI), has positioned educational institutions and individual learners at the crossroads of opportunity and risk, highlighting both significant benefits and potential drawbacks. In this context, fostering a culture of responsible and meaningful GAI use has become essential for leveraging its advantages while mitigating associated challenges. This chapter presents the results of two phenomenological qualitative studies that explore this cultural dimension. Study one examined Russian university professors' perspectives on the articulation of individual and organizational GAI standards, while Study two investigated the views of Russian educational managers, aiming to provide a balanced assessment of guidelines at both levels. Employing semi-structured interviews and focus group discussions, the findings illuminate how individual or microsystem, and organizational, or mesosystem interactions and tensions shape the development of guidelines and practices that promote a responsible approach to GAI use in higher education. At the microsystem level, individual faculty seek awareness, autonomy, and ethical engagement with GAI, while at the mesosystem level, organizational structures, guidelines, and policies mediate these efforts, negotiating the balance between individual practices and institutional support and accountability. The results highlight that responsible GAI governance emerges not as a set of static rules but through ongoing interactions between individual agency and organizational frameworks. Future research can incorporate observational data, longitudinal designs, or cross-cultural comparisons to further validate and extend these insights.

Language: English
DOI
Keywords: higher educationgenerative AI

In book

GenAIED: Cultural and Pedagogical Catalysts in Higher Education
L.: Routledge, 2027.
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