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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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A Method for Audience Extending in Programmatic Advertising by Using Parsimonious Generalization of User Segments

P. 837–841.
Frolov D., Taran Z., Mirkin B.

We propose a novel method for efficient target audience augmentation in programmatic digital advertising. This method utilizes a novel ParGenFS algorithm for most adequate generalization in taxonomies which was developed by the authors in a joint work. The ParGenFS extends user segments by parsimoniously lifting them off-line as a fuzzy set over IAB content taxonomy into a higher rank ‘head subject’. This algorithm was initially intended as an intelligent information retrieval tool. Here it is applied to a very different task of targeted advertisement as an effective tool for augmenting audiences.

Language: English
DOI
Text on another site
Keywords: targeting GeneralizationProgrammatic advertisingAudience extending Taxonomy
Publication based on the results of:
A study of models of decision-making and analysis of complex structured data (2019)

In book

International Conference on Human Interaction and Emerging Technologies
Springer, 2020.
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