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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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Many-body localization and new critical phenomena in regular random graphs and constrained Erd˝os-Renyi networks

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2017.
Avetisov V. A., Nechaev S. K., Gorsky A., Valba O. V.

We consider from the localization perspective the new critical behavior discovered recently for the regular random graphs (RRG) and constrained Erd˝os-Renyi networks (CERN). The diagonal disorder for standard models, we replace by the fugacity µ of triads in the RRG and CERN. At some critical value of µ the network decays into the maximally possible number of almost full graphs, and the adjacency matrix acquires the two-zonal structure. We find that the eigenvalue statistics corresponds to delocalized states in the central zone, and to the localized states in the side one. The mobility edge lies between zones. We apply these findings to the many-body localization assuming the approximation of the hierarchical structure of the Fock space (for some interacting many-body system) by the RGG and by CERN with some vertex degree. We allow the 3-cycles in the Fock space and identify particles in the many-body system above the phase transition with clusters in the RRG. We discuss the controversial issue of the additional phase transition between ergodic and non-ergodic regions in the delocalized phase in the Fock space and find the strong ”‘memory-dependence”’ of the states in the delocalized phase.

Research target: Mathematics
Language: English
Keywords: phase transitionsnetworks
Publication based on the results of:
Quantum and wave systems of mathematical physics (2017)
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