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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.
May 25, 2026
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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.
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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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Electronic phase separation: recent progress in the old problem

Physics Reports. 2021. Vol. 916. No. 1. P. 1–105.
Kagan M.Yu., Kugel K.I., Rakhmanov A. L.

We consider the nanoscale electronic phase separation in a wide class of different materials,  mostly in strongly correlated electron systems. The phase separation turns out to be quite ubiquitous manifesting itself in different situations, where the itineracy of charge carriers competes with their tendency toward localization. The latter is often related to some specific type of magnetic ordering, e.g. antiferromagnetic in manganites and low-spin states in cobaltites. The interplay between the localization induced lowering of potential energy and metallicity (which provides the gain in the kinetic energy) favors an inhomogeneous ground state such as nanoscale ferromagnetic
droplets in an antiferromagnetic insulating background. The present review article deals with the advances in the subject of electronic phase separation and formation of different types of nanoscale ferromagnetic (FM) metallic droplets (FM polarons or ferrons) in antiferromagnetically ordered (AFM), charge-ordered (CO), or orbitally-ordered (OO) insulating matrices, as well as the colossal magnetoresistance (CMR) effect and tunneling electron transport in the nonmetallic phase-separated state of complex magnetic oxides. It also touches upon the compounds with spin-state transitions, inhomogeneous phase separated state in strongly correlated multiband systems, and electron polaron effect.  A special, attention is paid to the systems with the imperfect Fermi surface nesting such
as chromium alloys, iron-based pnictides, and AA stacked graphene bilayers. 

Research target: Nanotechnologies Mathematics Physics
Priority areas: engineering science
Language: English
Full text
DOI
Keywords: strongly correlated electron systemsГрафен и новые двумерные материалыimperfect nestingнеидеальный нестингelectronic phase separationэлектронное фазовое расслоениесильно коррелированные электронные системыmagnetic polaronsorbital orderinggraphene-based materialsмагнитные поляроныорбитальное упорядочение
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