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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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Understanding emotions through biological motion in autism spectrum disorder: A systematic review

Research in Autism. 2026. Vol. 132. Article 202833.
Gavrilova E., Novoselova K., Myachykov A., Shtyrov Y.

Background

Body movements convey crucial insights into emotions. Although autistic individuals may process these cues differently, the specific factors influencing emotion-from-motion perception in autism remain poorly understood. This systematic review synthesizes current research and highlights key findings in this area of study.

Objectives

This systematic review aimed to assess autistic individuals’ ability to recognize emotions through biological motion (operationalized via point-light displays or avatars) and to detect task- and stimulus-related factors that may affect emotion recognition quality.

Design

Relevant studies were selected from PubMed and ScienceDirect databases. Sixteen publications were eligible for the final review following strict selection criteria. The results were assessed specifically with respect to the experimental paradigms, stimulus characteristics, and control tasks (a range of non-emotion-from-motion tasks included in the reviewed studies).

Results

Methodological synthesis reveals that empirical heterogeneity is systematically linked to experimental design. Paradigm choice critically shapes outcomes, with consistent group differences in verbal naming tasks but not in perceptual rating or matching tasks. Over a third of studies omitted specific emotion analysis, and stimulus parameter investigations were scarce and inconsistent. Control task data vary widely across studies. Although some research suggests emotion-specific deficits, the inconsistent findings and absence of standardized, well-matched control paradigms make it impossible to draw firm conclusions.

Conclusion

While difficulties in emotion-from-motion perception in autism are widely reported, they represent a profile highly sensitive to methodological context, not a uniform deficit. The field is hampered by methodological fragmentation and insufficient replication. Future research must prioritize cross-paradigm comparisons, open science, and systematic stimulus exploration to clarify the specific conditions underlying perceptual differences.

Research target: Psychology
Language: English
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Keywords: social cognitionresearch methodologyemotion recognitionbiological motionmotion processing
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