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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.
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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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Elastic single-walled carbon nanotubes pixel matrix electrodes for flexible optoelectronics

Applied Physics Letters. 2022. No. 121. Article 243504.
Mukhangali S., Neplokh V., Kochetkov F., Vorobyev A., Mitin D., Mukhin M., Krasnikov D. K., Tian J., Islamova R., Nasibulin A. G., Mukhin I.

This paper demonstrates an elastic (i.e., both flexible and stretchable) pixel matrix based on the patterned films of single-walled carbon nanotubes, which were obtained using optical lithography and plasma etching. The proposed elastic electrodes maintain their initial resistivity at 10% stretching. We also demonstrate an application of the elastic pixel matrix to an array of III–V semiconductor nanowires encapsulated into the elastic polymer matrix, which paves the way for highly deformable inorganic light-emitting or photodetector devices.

This work was funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program “Priority 2030” (Agreement No. 075-15-2021-1333). The SWCNT synthesis was supported by the Russian Science Foundation (Grant No. 22-13-00436). Membrane fabrication was funded by St. Petersburg State University (Project No. 94385659). V.N. thanks the Russian Foundation for Basic Research (RFBR Project No. 19-32-60040) for PDMS/MW membrane fabrication.

The authors thank Dr. Vladimir Fedorov for n-GaP NWs sample synthesis.

Research target: Physics
Language: English
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Keywords: polymerssemiconductor nanostructuresoptoelectronicsNanotubeselectrodesPhotolithographyInorganic compounds
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