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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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?

Влияние тональности писем CEO на финансовые показатели компании

Российский журнал менеджмента. 2017. Т. 15. № 4. С. 441–462.
Fedorova E., Осетров Р. А., Демин И. С., Федоров Ф. Ю., Khrustova L.

The paper is devoted to the analysis of CEO letters as an instrument for influencing the expectations of shareholders and potential investors. The aim of the research is to analyze empirically the influence of semantic characteristics of CEO letters on financial indicators of the company. The authors suggested that CEO letter’s tonality, its length and readability have a great impact on the company’s financial indicators, their prediction and mid-year stock value. To check the hypotheses stated, a sample group of 102 Russian companies was analyzed with the use of “bag of words” method (specialized dictionaries were applied). For this purpose, neural network models were also developed. The results obtained confirmed the influence of CEO letter’s semantic characteristics on the stock value of company.

Priority areas: economics
Language: Russian
DOI
Text on another site
Keywords: нейронные сетиtext analysisneural networksтекстовый анализCEO Letterbag of wordsCEO Letterмешок слов
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