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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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Математическая модель оценки рисков негосударственного пенсионного фонда

Цифровая экономика. 2022. № 4(20). С. 42–52.
Яркова О. Н., Чудинова О. С., Ramenskaya A.

The work is devoted to the development of mathematical models and a tool for the analysis of actuarial and financial risks of non-state pension Funds for non-state pension provision, taking into account the sto-chastic variability in the number and gender and age structure of participants. The author's mathematical model is proposed for assessing the dynamics of financial reserves of non-state pension Funds for non-state pension provision, which allows taking into account the stochastic nature of the inflow and outflow of funds, fluctuations in the number and gender and age structure of participants in the pension program. The model was tested using the developed decision support tool. The proposed toolkit makes it possible to evaluate the indicators of financial reliability of non-state pension Funds in the medium and long term.

Research target: Mathematics Economics and Management Computer Science
Language: Russian
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Keywords: математическое моделированиевероятность неразорения риск - менеджментпакетные финансовые решения
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