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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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Composite Cathodes Based on Lithium-Iron Phosphate and N-Doped Carbon Materials

Batteries. 2022. Vol. 8. No. 12. Article 256.
Irina Stenina, Safikanov D., Minakova Polina, Novikova Svetlana A., Tatiana Kulova, Yaroslavtsev A. B.

The effect of different nitrogen-doped carbon additives (carbon coating from polyaniline,
N-doped carbon nanotubes, and N-doped carbon nanoparticles) on electrochemical performance
of nanocomposites based on the olivine-type LiFePO4 was investigated. Prepared materials were
characterized by XRD, SEM, TGA-MS, CHNS-analysis, IR-, Raman, and impedance spectroscopies.
Polyaniline deposition on the LiFePO4 precursor with following annealing lead to the formation of
a LiFePO4/C nanocomposite with a carbon coating doped with nitrogen. Due to nitrogen atoms
presence in carbon coating, the LiFePO4/N-doped carbon nanocomposites showed enhanced conductivity
and C-rate capability. The discharge capacities of the synthesized materials in LIBs were
close to the theoretical value at 0.1 C and retained high values with increasing current density. At
high C-rates, the best results were obtained for a more dispersed LiFePO4/C composite with carbon
coating prepared from polyaniline previously in situ deposited on LiFePO4 precursor particles. Its
discharge capacity reached 96, 84, 73, and 47 mAh g􀀀1 at 5, 10, 20, and 60 C-rates, respectively.

Research target: Chemistry Nanotechnologies
Language: English
Full text
DOI
Text on another site
Keywords: cathodeCNTSpolyanilineLiFePO4Carbon coatingNitrogen-doped carbon nanomaterialslithium ion battery
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