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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.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

 

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Функциональная связанность в височных областях головного мозга и конверсия в болезнь Альцгеймера при мягком когнитивном снижении амнестического типа

Психиатрия. 2025. Т. 23. № 1. С. 6–17.
Паникратова Я. Р., Комарова А. Ю., Абдуллина Е. Г., Божко О. В., Cherkasov N., Гаврилова С. И., Колыхалов И. В., Лебедева И. С.

Background: structural and neurochemical abnormalities in temporal lobe and temporoparietal junction (T-TPJ) are seen not only in Alzheimer’s disease (AD) but also in amnestic mild cognitive impairment (aMCI). At the same time, studies of conversion to dementia focused on functional characteristics of these regions are lacking. 

The aim was to search for patterns of functional connectivity (FC) within T-TPJ that differentiate patients with aMCI with future conversion to AD from stable aMCI patients and healthy controls. 

Patients, Comparison Group and Methods: patients with aMCI who further converted to dementia due to AD (converters, n = 15), patients with stable aMCI (n = 12), and healthy individuals without cognitive deficits (n = 29) underwent resting-state functional magnetic resonance imaging. FC between cortical T-TPJ structures was compared between groups separately for each hemisphere (one-way ANCOVA and post hoc between-group comparisons). 

Results: an increased FC between posterior parts of left middle and inferior temporal gyri was observed in converters compared to other groups. There was an inverse correlation between this FC and delayed recall of words (MoCA scale) in the entire sample, however, this correlation did not reach the level of statistical significance (p = 0.055). 

Conclusions: the posterior parts of left middle and inferior temporal gyri are involved in auditory verbal memory and storing of visual images associated with a word, respectively. Therefore, а pattern of increased FC observed in future converters to AD may be a consequence of pathological processes that have already started and/or compensatory mechanisms.

Research target: Medical and Health Sciences Clinical Medicine Basic Medicine
Language: Russian
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Keywords: functional connectivitydementiaдеменцияresting-state fMRIфМРТ покояAlzheimer’s diseaseболезнь Альцгеймераamnestic mild cognitive impairmentмягкое когнитивное снижение амнестического типафункциональная связанность
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