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June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Разработка методов автоматизации высокоуровневого моделирования сетей на кристалле

Труды Института системного программирования РАН. 2023. Т. 35. № 5. С. 67–80.
Amerikanov A., Таржанов Т. В., Romanova I., Romanov A.

The paper analyzes the existing methods to optimize the time costs and increase the accuracy of calculations in the high-level simulation of networks-on-chip. The description of parameters and characteristics of networks-on-chip calculated by different models is given, and their influence on the speed of high-level simulation is analyzed. Adaptation of existing methods of modeling optimization for implementation in the system of automation of networks-on-chip design is carried out.

Research target: Computer Science
Language: Russian
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Keywords: автоматизация проектированияCADСАПРdesign automationсеть на кристаллеhigh-level modelingвысокоуровневое моделированиеnetworks-on-chip
Publication based on the results of:
Automation of networks-on-chip modeling (2023)
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