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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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Pushing the Energy-Sensitivity Balance with High-Performance Bifuroxans

ACS Applied Energy Materials. 2020. Vol. 3. No. 8. P. 7764–7771.
Larin A., Shaferov A., Epishina M., Melnikov I., Muravyev N., Ananyev I., Fershtat L., Makhova N.

Several energy-rich bifuroxans incorporating nitro and azido functionalities have been synthesized and thoroughly characterized by IR and multinuclear NMR spectroscopy, elemental analyses, single-crystal X-ray diffraction, and differential scanning calorimetry. N-oxide regiochemistry was employed to design the tunable azido(nitro)bifuroxans with different physicochemical and energetic properties. All synthesized compounds have high enthalpies of formation (449-777 kJ mol(-1)) and attractive performances, as evidenced by the high detonation velocities (8.95-9.75 km s(-1)) and Champan-Jouguet pressures (35-45 GPa). The most powerful energetic material in this series is 4,4'-dinitro-3,3'-bifuroxan. This hydrogen-free molecule (C4N6O8) exhibits an outstanding heat of explosion value of 15.3 kJ cm(-1), far exceeding the top energetic material hexanitrohexaazaisowurtzitane CL-20. At the same time, the impact and friction sensitivities of 4,4'-dinitro-3,3'-bifuroxan were deemed acceptable for practical use. Overall, 4,4'-dinitro-3,3'-bifuroxan breaks a general trend called the "energy-sensitivity rule", which describes a linear increase of the mechanical sensitivity with a growth of the energetic content of the molecule, and, thus, offers great promise for future applications.

Research target: Chemistry
Language: English
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Keywords: nitrogen heterocyclesазотные гетероциклы1,2,5-оксадиазолenergetic matrialsdetonation performanceэнергетические материалыфуроксан1,2,5-oxadiazoles
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