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News
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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Proceedings of the 28th Conference on Computational Natural Language Learning

Association for Computational Linguistics, 2024.
Compiler: L. Barak, M. Alikhani

CoNLL is a conference organized yearly by SIGNLL (ACL’s Special Interest Group on Natural Language Learning), focusing on theoretically, cognitively and scientifically motivated approaches to computational linguistics. This year, CoNLL was held alongside EMNLP 2024.

Chapters
Of Models and Men: Probing Neural Networks for Agreement Attraction with Psycholinguistic Data
Bazhukov M., Voloshina E., Sergey Pletnev et al., , in: Proceedings of the 28th Conference on Computational Natural Language Learning.: Association for Computational Linguistics, 2024. P. 280–290.
Added: March 11, 2025
Research target: Engineering and Technology
Language: English
DOI
Text on another site
Keywords: cognitive sciencenatural language learningLarge Language ModelsNatural Language Processing
Proceedings of the 28th Conference on Computational Natural Language Learning
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