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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
'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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Genetic heritage of the Balto-Slavic speaking populations: A synthesis of autosomal, mitochondrial and Y-chromosomal data

Plos One. 2015. Vol. 10. No. 9.
Dybo A., Kushniarevich A., Utevska O., Chuhryaeva M., Owings A. C., Schurr T. G., Agdzhoyan A., Dibirova K., Uktveryte I., Möls M., Mulahasanovic L., Pshenichnov A., Frolova S., Shanko A., Metspalu E., Reidla M., Tambets K., Tamm E., Koshel S., Zaporozhchenko V., Atramentova L., Kučinskas V., Davydenko O., Goncharova O., Evseeva I., Churnosov M., Pocheshchova E., Yunusbayev B., Khusnutdinova E., Marjanović D., Rudan P., Yankovsky N., Endicott P., Kassian A., Tyler-Smith C., Balanovska E., Metspalu M., Kivisild T., Villems R., Balanovsky O.

The Slavic branch of the Balto-Slavic sub-family of Indo-European languages underwent rapid divergence as a result of the spatial expansion of its speakers from Central-East Europe, in early medieval times. This expansion-mainly to East Europe and the northern Balkans-resulted in the incorporation of genetic components from numerous autochthonous populations into the Slavic gene pools. Here, we characterize genetic variation in all extant ethnic groups speaking Balto-Slavic languages by analyzing mitochondrial DNA (n = 6,876), Y-chromosomes (n = 6,079) and genome-wide SNP profiles (n = 296), within the context of other European populations. We also reassess the phylogeny of Slavic languages within the Balto-Slavic branch of Indo-European. We find that genetic distances among Balto-Slavic populations, based on autosomal and Y-chromosomal loci, show a high correlation (0.9) both with each other and with geography, but a slightly lower correlation (0.7) with mitochondrial DNA and linguistic affiliation. The data suggest that genetic diversity of the present-day Slavs was predominantly shaped in situ, and we detect two different substrata: 'central-east European' for West and East Slavs, and 'south-east European' for South Slavs. A pattern of distribution of segments identical by descent between groups of East-West and South Slavs suggests shared ancestry or a modest gene flow between those two groups, which might derive from the historic spread of Slavic people.

Research target: Philology and Linguistics Biology
Priority areas: humanitarian
Language: English
DOI
Keywords: Balto-Slavic languages and peoples
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