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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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Статистический анализ системы высшего образования в Центральном федеральном округе.

Вопросы статистики. 2015. № 10. С. 39–47.
Melikyan A.

The article presents the results of the study, which aims to evaluate the condition of higher education system in the Central Federal District (CFD) on the basis of statistical data from the project - Monitoring the efficiency of higher educational institutions in 2014. The higher educational institutions have been arranged into groups according to various criteria: status, organizational and legal form, specialization, number of students and departmental affiliation. The article analyzes the location of higher educational institutions within the district and the distribution of the reported number of students at institutions of various types and groups of specialties (majors).
According to the results of the research the majority of the state higher educational institutions in the CFD are located in major cities. Private universities and branches are mainly located in small and medium-sized cities. Approximately one third of higher educational institutions is affiliated with the Ministry of Education and Science of the Russian Federation and is attended by 59,1% of the students in the district. The proportion of non-state higher educational institutions is also considerable (43,2%); however they are attended only by 8,4% of students. Higher educational institutions affiliated with the sectoral ministries, generally offer specialties corresponding to the profile of the ministry. The most popular fields of study in the CFD are: «Economics and Management» and «Humanities»; almost half of the total number of reported students is enrolled in them.

Research target: Education Mathematics
Priority areas: management
Language: Russian
Full text
Keywords: образовательная политикаeducational statisticsрегиональные системы высшего образованиямониторинг эффективности вузовregional systems of higher educationобразовательная статистикаmonitoring of the effectiveness of higher educational institutionseducational policy
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