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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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Reliability of immune receptor rearrangements as genetic markers for minimal residual disease monitoring

Bone Marrow Transplantation. 2016. Vol. 1. No. 3.
Nazarov V. I., Minervina A. A., Komkov A. Y., Pogorelyy M., Maschan M. A., Olshanskaya Y. V., Zvyagin I., Chudakov D., Lebedev Y. B., Mamedov I.

Detection of minimal residual disease (MRD) is a powerful prognostic tool in many hematological malignancies including ALL.1 Several groups of markers are widely used to monitor the concentration of a malignant clone including the detection of 'clonal B-cell (BCR) or T-cell (TCR) gene receptor rearrangements2 in ALL. Identification of a clonal rearrangement specific for the malignant clone, which usually constitutes from 20 to 90% in the initial bone marrow sample is a relatively straightforward task. Tracking this rearrangement after therapy is more tricky as the concentration of malignant cells in the sample can be very low.

Priority areas: IT and mathematics
Language: English
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Keywords: bioinformaticsбиоинформатика
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