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News
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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Versions of least-squares k-means algorithm for interval data

2024.
Mirkin B., Parinov A., Halynchyk M., Carvalho F.
Recently, k-means clustering has been extended to the so-called interval data. In contrast to conventional data case, the interval data feature values are intervals rather than single reals. This paper further explores the least-squares criterion for k-means clustering to tackle the issue of initialization, that is, finding a proper set of initial cluster centers at interval data clustering. Specifically, we extend, for the interval data, a Pythagorean decomposition of the data scatter in the sum of two items, one being a genuine k-means least-squares criterion, the other, a complementary criterion, requiring the clusters to be numerous and anomalous. Therefore we propose a method for one-byone obtaining anomalous clusters. After a run of the method, we start k-means iterations from the centers of the most numerous of the found anomalous clusters. We test and validate our proposed BIKM algorithm at versions of two newly introduced interval datasets.
Priority areas: IT and mathematics business informatics mathematics
Language: English
Full text
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Keywords: k-meansfeature weights least-squares problem Interval data Anomalous cluster
Publication based on the results of:
Building knowledge systems and data analysis based on textual information (2024)
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