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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.
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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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?

Methods for Reducing the Amount of Data Transmitted and Stored in IoT Systems

P. 21–31.
Anufrienko A.

In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can use cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach focuses on sensor processing. Correlation and autocorrelation methods for event detection depending on waveform are described in detail and implementation of endpoint architecture is proposed. Novelty and another feature of this approach is the use of not the full waveform, but their components and processing on the device. This significantly reduces the number of operations and complexity of implementation. Other methods focus on the cloud computing paradigm. The results of the simulation show that at the data transfer rate from the sensor ~10 MSample/s, the proposed method allows you to transmit and store 280 bytes in 70 min instead of 157 GB using the bypass method. Reducing data transfer and storage requirements will simplify and reduce the cost of IoT systems, improve performance, and apply additional precision sensors to provide more accurate data.

The solutions are focused on low-power and FPGA/ASIC implementations.

Language: English
DOI
Keywords: IoT

In book

Internet of Things, Smart Spaces, and Next Generation Networks and Systems. 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I
Issue 12525. , Springer, 2020.
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