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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.
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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?
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Generalized Centrality Measures for Multiplex Networks: Combining Node Attributes and Group Dynamics

Procedia Computer Science. 2025. Vol. 266. P. 818–824.
Aleskerov F. T., Semenova A.

Many complex systems are best represented as multilayer or multiplex networks, where nodes are connected through multiple types of links across different layers. This layered structure introduces additional challenges for identifying central elements, as traditional centrality measures typically assume a single-layer network. The paper introduces a new centrality framework for multiplex networks that accounts for both vertex attributes and group-mediated influence, offering a refined approach to identifying critical nodes in multilayer systems. Additionally, we introduce a block concept, combining weights, node parameters, and inter-layer group effects into a unified analytical model. The model is applied to the Florence families dataset.

Research target: Mathematics
Language: English
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DOI
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Keywords: centrality measuresмногослойные сетииндексы центральности в сетяхгрупповое влияние в сетяхgroup influence in multiplex networksmultiplex network centrality measures
Publication based on the results of:
Decision-Making in Socio-Economic, Political and Financial Spheres (2025)
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