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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.
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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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Multiple categorization and intergroup bias: Examining the generalizability of three theories of intergroup relations

Journal of Personality and Social Psychology. 2022. Vol. 122. No. 1. P. 34–52.
Grigoryan L., Boehnke K., Cohrs J. C., van de Vijver F., Easterbrook M. J.

Research on intergroup bias usually focuses on a single dimension of social categorization. In real life, however, people are aware of others' multiple group memberships and use this information to form attitudes about them. The present research tests the predictive power of identification, perceived conflict, and perceived symbolic threat in explaining the strength of intergroup bias on various dimensions of social categorization in multiple categorization settings. We conduct a factorial survey experiment, manipulating 9 dimensions of social categorization in diverse samples from 4 countries (n = 12,810 observations, 1,281 participants representing 103 social groups). The dimensions studied are age, gender, ethnicity, religion, place of residence, education, occupation, income, and 1 country-specific dimension. This approach allows exploring the generalizability of established determinants of bias across dimensions of categorization, contexts, and target groups. Identification and symbolic threat showed good generalizability across countries and categorization dimensions, but their effects varied as a function of participant and target groups' status. Identification predicted stronger bias mainly when the participant belonged to a higher status and the target belonged to a lower status group. Symbolic threat predicted stronger bias mainly when the target was a minority group member. Conflict predicted bias only in few cases, and not only the strength but also the direction of the effects varied across countries, dimensions, and target and participant groups. These findings help to clarify the limits of generalizability of established determinants of intergroup bias and highlight the need for new explanations of social- cognitive processes among minority group members.

Research target: Psychology
Priority areas: sociology
Language: English
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Keywords: идентичностьidentityпредубежденияугрозаPrejudicemultiple categorizationмножественная категоризация threat
Publication based on the results of:
Values, intercultural and socio-economic interaction in the context of ethnic diversity and the COVID-19 pandemic (2022)
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