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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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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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Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks. In this type of approximation, a norm is weighted by a matrix of input activations, significantly improving metrics over the unweighted case. Nevertheless, existing methods for neural networks suffer from numerical instabilities due to their ...
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An extended method for calculating heat transfer coefficients using programs for automated analysis of the thermal regime of TRIANA (ASONIKA-T) radio-electronic equipment for various designs is proposed. Based on the analysis of the obtained heat transfer coefficients, it is shown how to set realistic values for the convective heat transfer coefficient in SOLIDWORKS Simulation and other CAE thermal analysis systems. ...
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IEEE, 2025.
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Decision-Making in Computational Intelligence-Based Systems: New Approaches, Methods, and Applications
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Advancements in robotics have expanded a use of unmanned aerial vehicle (UAV) swarms in critical tasks such as disaster response, including search and rescue operations during floods, hurricanes, landsliding, and earthquakes. Swarm formation control stands as a critical challenge in UAV swarm control. In this article, a simple and resource-efficient method for addressing collisions within swarm formations during outdoor ...
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