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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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Navigating the Future: AI, Global Supply Chains, and ESG for Sustainable Development

Sustainable Development. 2026. Vol. 34. No. S2. P. 403–421.
Tiwari S., Tzeremes P., Kazemzadeh E., Soltani H., Doğan B.

This study examines the impact of global supply chain pressures (GSCP), artificial intelligence (AI), economic complexity (EC), and sustainability uncertainty (SUI) on the productive capacities index (PCI) in the United States from 2018 to 2024. Employing advanced econometric techniques—including quantile-on-quantile regression (QQR), cross-quantilogram (CQ), and wavelet-quantile correlation (WQC)—the analysis assesses variable effects, directional predictability, causal relationships, and multiscale correlations. The findings indicate that AI significantly enhances productive capacity through technological innovation, automation, and efficiency gains. In contrast, GSCP and ESG-related uncertainties (captured by EC and SUI) hinder productive capacities by exacerbating operational disruptions and investment risks across US industries. These results underscore the necessity for strategic policy interventions that leverage AI's potential to mitigate supply chain vulnerabilities and sustainability challenges. To bolster productive capacities, we recommend that US policymakers prioritize fostering a dynamic entrepreneurial ecosystem, promoting technological innovation, strengthening infrastructure resilience, and investing in human capital development. This study provides critical insights for policymakers, industry leaders, and researchers navigating the interplay of technological advancement, global supply chain volatility, and sustainability in shaping economic productivity.

Research target: Economics and Management
Language: English
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DOI
Text on another site
Keywords: global supply chainssustainable developmentAIESG
Publication based on the results of:
Trends and factors of sustainable science and technology development (2025)
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