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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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Моделирование вероятности дефолта корпорации на основе прогнозной динамики показателей финансовой отчетности

Финансы и бизнес. 2020. № 3. С. 40–60.
Ichkitidze Y., Krakovich V., Romanyuk K.

The paper presents analysis of existing default probability estimation models. Their advantages and disadvantages for application to the Russian companies are considered. As a result of this analysis the new model of corporate default probability estimation is developed. The model is based on forecasting financial indicators. The model was tested on the US companies. The estimations on average have similar values as ones issued by rating agencies. The model should be in demand on the Russian market due to insufficient statistics on historical default frequency, impossibility of KMV-model application caused by low number of publicly traded companies among bond-issuers and absence of “Big-3” agencies ratings for majority of them.

Priority areas: economics
Language: Russian
Full text
DOI
Keywords: probability of defaultвероятность дефолтаcredit ratingsкредитные рейтинги
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