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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Review of Practices of Collecting and Annotating Texts in the Learner Corpus REALEC

P. 77–88.
Vinogradova O. I., Lyashevskaya O.

REALEC, learner corpus released in the open access, had received 6,054 essays written in English by HSE undergraduate students in their English university-level examination by the year 2020. This paper reports on the data collection and manual annotation approaches for the texts of 2014–2019 and discusses the computer tools available for working with the corpus. This provides the basis for the ongoing development of automated annotation for the new portions of learner texts in the corpus. The observations in the first part were made on the reliability of the total of 134,608 error tags manually annotated across the texts in the corpus. Some examples are given in the paper to emphasize the role of the interference with learners’ L1 (Russian), one more direction of the future corpus research. A number of studies carried out by the research team working on the basis of the REALEC data are listed as examples of the research potential that the corpus has been providing

Language: English
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Keywords: разметка корпусаcorpus annotationучебный корпусlearner corpusосвоение первого и второго языка (L1 и L2)learner academic writing in EnglishL1 Russianerror taxonomyучебное академическое письмо на английском языкетаксономия ошибок
Publication based on the results of:
Automated Detection of Writing Inaccuracies for Students of English in Russia (2021)

In book

Text, Speech, and Dialogue. 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings Lecture Notes in Computer Science (LNAI), vol. 13502
Vol. 13502. , Cham: Springer Publishing Company, 2022.
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