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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.
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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?
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Зрительная репрезентация ансамблевой статистики при различных условиях двигательной активности

Сибирский психологический журнал. 2023. № 90. С. 28–43.
Баланев Д. Ю., Koch D.

The article is devoted to the results of a study of the influence of motor activity on the noise reduction mechanism in the perception of an ensemble. The relevance of the problem of studying the influence of motor activity in the perception of an ensemble is determined by the fact that motor activity is a conscious motor activity that often contributes to perception in various tasks, and the investigation of the perception of ensembles itself is a fairly actively developing area. An ensemble is understood as a set of objects that share a common characteristic, on the basis of which a person is able to generate summary statistics of the presented objects. To test the research hypothesis that the visual representation of ensemble statistics is determined by the ability to regulate the noise reduction mechanism by specially organized motor activity, an ecologically designed experiment was conducted to study the perception of ensembles under various conditions of motor activity. The sample of the experiment consisted of 65 students and teachers of Tomsk State University, of which 50 girls (77%) and 15 boys (23%) aged 18 to 39 years, (M = 22.3; SD = 5.28), who were divided into experimental and control groups. Participants in the experiment performed tasks on the approximate number of objects in a set, as well as a task on categorical membership. The experimental group had the opportunity to regulate their motor activity while solving the presented problems of approximating quantity and categorical affiliation, while the control group had to show constant motor activity.

The results demonstrate that motor activity, as an opportunity to actively contribute to the noise reduction mechanism, does not have a significant effect, indicating the perceptual level of perception of ensembles. Insights were obtained regarding the features of conducting the experiment in an environmentally friendly version. Additionally, classical effects from the domain of ensemble perception were replicated, indicating the possibility of studying ensemble perception with the presented experimental complex.

Research target: Psychology
Language: Russian
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Keywords: ensemble representationensemble perceptionвосприятие ансамблейmotor activityдвигательная активностьensemble summary statisticsсводные статистики ансамблейрепрезентация ансамбляnoise cancellationмеханизм шумоподавления
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