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News
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
September 7, 2026
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.

 

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Прогнозирование распространения COVID-19 в ЕС с использованием рандомизированного машинного обучения динамических моделей

Информационные технологии и вычислительные системы. 2022. № 3. С. 67–78.
Popkov A., Dubnov Y. A., Popkov Y.

The work is devoted to application of the theory of Randomized Machine Learning to forecasting of the COVID-19 pandemic based on SIR epidemiological model. We propose two modelling variants, the first is based on estimation of SIR model using real case data, the second is based on the idea of modelling transmission coefficient and its prediction. Comparative study of proposed approach is based on a comparison with the standard least squares approach and is carried out on a dataset of several countries of the European Union. It is shown the performance of the proposed approach and its effectiveness and adequacy under conditions of small amount of data with a high level of uncertainty.

Research target: Mathematics Medical Technologies
Language: Russian
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Keywords: моделирование эпидемийSIRSIRrandomized machine learningSARS-CoV-2SARS-CoV-2COVID-19Entropy estimationEpidemic modellingCOVID-19рандомизированное машинное обучениеэнтропийное оценивание
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