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News
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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Ion-Sensitive Three-Terminal Device as a Universal Hybrid Platform With TiO2 Nanowires on the Channel

IEEE Sensors Journal. 2023. Vol. 23. No. 3. P. 1917–1924.
Dalal A., Bebarta A., Paul A. D., Barman R., Nunzi J., Alexey Tameev, Mahapatra R., Mondal A.

We developed a three-terminal ion-sensitive device (TTISD) as a proton (H+) and arsenic (As3+) sensor for the detection of glucose and As(III) dissolved in water. The TTISD was fabricated using the e-beam evaporation and glancing angle deposition (GLAD) technique. The TiO2 nanowires (NWs) were grown by GLAD on the conducting nafion channel of the TTISD to immobilize the test specimens. Arsenic (As) contamination in drinking water is a burning issue as it’s a threat to human health beyond the safe limit of As(III) in drinking water (0.005mg/dL). Glucose concentration within the limit of 0–200 mg/dL in various body fluids, such as blood, sweat, and saliva is also a necessary parameter to monitor. Early detection of high As(III) concentration in water and regular monitoring of glucose concentration in the human body are essential for human health and safety. This work presents a common nano-hybrid platform for the detection of low-concentration glucose (40–200 mg/dL) and high concentration arsenic (0.02–2 mg/dL) in drinking water. The device exhibits a rapid response time of ~1 s, enabling real-time detection of glucose and As(III).

Research target: Physics
Language: English
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Keywords: нанопроводаnanowiressensorsсенсорыheavy metalsтяжелые металлыbiosensorбиосенсорTiO2 Arsenicмышьякglancing angle deposition (GLAD)glucose sensorосаждение под скользящим угломсенсор глюкозыTiO2
Publication based on the results of:
Investigation of transport properties of heterogeneous dielectric layers of advanced photo converters aimed to improve their efficiency and lifetime in a broad temperature range  (2023)
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