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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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BTI 2016 | Russia Country Report

P. 1–33.
Petrov N.

The international financial and economic crisis, which hit Russia in the fall of 2008, marked the end of a long economic boom. Since then, economic growth has been sluggish and the public funds saved during the previous period of favorable economic growth have been spent on easing the economic and social consequences of the crisis. In 2014, without having completely recovered from the previous crisis, Russia was hit by further economic difficulties. These difficulties were caused by structural deficiencies within the economy, economic sanctions imposed by the West and, most importantly, a drop in the world oil price. Despite these difficulties, the Kremlin has chosen to prioritize security interests over the requirements of Russia’s economic development – for which it is bound to ultimately pay a high price. In an effort to consolidate power and to increase control over Russia’s elites, Putin – now looking more like a tsar than a president – has effectively facilitated the creation of an authoritarian-bureaucratic nomenklatura system. Russia is characterized by: • the dependence of private individuals on their official position and support of their direct superior; • short-term, hierarchical decision-making processes; • dominance of vertical over horizontal networks, which is ensured by, among other things, frequent bureaucratic rotations at the regional level; • temporary rather than permanent property ownership patterns, which supports a system of patronage; • moral and legal norms for those in power are much broader than for ordinary citizens. Most notably, the role of the Siloviki (i.e., politicians from the security and military services) has become much more pronounced, while the role of the judiciary has decreased.

Language: English
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Keywords: statenesspolitical participationthe rule of lawsocioeconomic developmentproperty rights protectionparty systemRussia's transformationStability of Democratic InstitutionsMarket-based competition

In book

Transformation Index BTI 2016. Political Management in International Comparison
Berlin: Verlag Bertelsmann Stieftung, 2016.
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