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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Specific problems in visual cognition of dyslexic readers: Face discrimination deficits predict dyslexia over and above discrimination of scrambled faces and novel objects.

Cognition. 2018. Vol. 175. P. 157–168.
Kristjansson A., Sigurdardottir H. M., Fridriksdottir L. E., Gudjonsdottir S.

Evidence of interdependencies of face and word processing mechanisms suggest possible links between reading problems and abnormal face processing. In two experiments we assessed such high-level visual deficits in people with a history of reading problems. Experiment 1 showed that people who were worse at face matching had greater reading problems. In experiment 2, matched dyslexic and typical readers were tested, and difficulties with face matching were consistently found to predict dyslexia over and above both novel-object matching as well as matching noise patterns that shared low-level visual properties with faces. Furthermore, ADHD measures could not account for face matching problems. We speculate that reading difficulties in dyslexia are partially caused by specific deficits in high-level visual processing, in particular for visual object categories such as faces and words with which people have extensive experience.

Research target: Psychology
Language: English
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Keywords: dyslexiaReadingFace perceptionObject perceptionVisual expertise
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