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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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Обзор экспериментальных задач на объектное зрительное воображение и их применимости в психофизиологических исследованиях

Вопросы психологии. 2024. Т. 70. № 6. С. 105–115.
Pechenkova E., Rachinskaya M., Vasilenko V., Иванушко В. П., Мершина Е. А.

Modern psychological accounts of mental imagery distinguish two aspects of visual mental representation: object (representation of pictorial features, such as shape, color, and texture) and spatial (representation of spatial relations and transformations). The hypothetical neural substrates of object and spatial imagery are the ventral and dorsal visual pathways in the brain. To study spatial imagination, many experimental tasks have been developed with the possibility of objective verification of their correctness, and many of them are well adapted for conducting psychophysiological research. In contrast, for the study of object visual imagination, mainly methods of subjective assessment of the brightness of images are used, and only a few tests have been created that allow checking the correctness of such techniques. In this regard, the present review analyzes the experimental tasks on object imagination described in the literature and evaluates their applicability for psychophysiological research. The analysis was conducted on the basis of the following parameters: the nature of the task requirements and the imaginative processes involved in task accomplishment (generation, examination, transformation of mental images); the possibility of objective verification of the representation; the validity of the methods; the reproductive (reliance on past experience) or productive (creative) nature of imagination; reliance on special knowledge and experience, including professional and culturally specific; working memory load; reliance on verbal coding; control for interference from current visual stimulation in the process of imagination; the potential for creating sets of homogeneous tasks, as well as parameterization of tasks and control of task difficulty levels. The review revealed that, at present, no task meets the criteria of an optimal test methodology for studying the brain correlates of object imagery in psychophysiological research and none can claim the status of a methodological gold standard, analogous to mental rotation tasks for spatial imagery.

Research target: Psychology
Language: Russian
Keywords: taskзадачарепрезентацияrepresentationmental imageryneuroimagingнейровизуализациязрительное воображениеobject imageryобъектное воображениеvisual imageryмысленные образы
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