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News
April 30, 2026
HSE Researchers Compile Scientific Database for Studying Childrens Eating Habits
The database created at HSE University can serve as a foundation for studying children’s eating habits. This is outlined in the study ‘The Influence of Age, Gender, and Social-Role Factors on Children’s Compliance with Age-Based Nutritional Norms: An Experimental Study Using the Dish-I-Wish Web Application.’ The work has been carried out as part of the HSE Basic Research Programme and was presented at the XXVI April International Academic Conference named after Evgeny Yasin.
April 30, 2026
New Foresight Centre Study Identifies the Most Destructive Global Trends for Humankind
A team of researchers from the HSE International Research and Educational Foresight Centre has examined how global trends affect the quality of human life—from life expectancy to professional fulfilment. The findings of the study titled ‘Human Capital Transformation under the Influence of Global Trends’ were published in Foresight.
April 28, 2026
Scientists Develop Algorithm for Accurate Financial Time Series Forecasting
Researchers at the HSE Faculty of Computer Science benchmarked more than 200,000 model configurations for predicting financial asset prices and realised volatility, showing that performance can be improved by filtering out noise at specific frequencies in advance. This technique increased accuracy in 65% of cases. The authors also developed their own algorithm, which achieves accuracy comparable to that of the best models while requiring less computational power. The study has been published in Applied Soft Computing.

 

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What Machine Learning Can Do for Focusing Aerogel Detectors

Physics of Atomic Nuclei. 2023. Vol. 86. No. 5. P. 864–868.
Shipilov F., Barnyakov A., Bobrovnikov V., Kononov S., Ratnikov F.

Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). Silicon photomultipliers used for the Cherenkov light detection generate a lot of noise hits that must be mitigated to reduce both the data flow and negative effects on particle velocity resolution. In this work we present our approach to filtering signal hits, inspired by object detection techniques for computer vision. Several ML-based approaches to the FARICH reconstruction problem in different settings are also discussed.

Research target: Computer Science Physics
Language: English
Full text
DOI
Keywords: машинное обучениеnoise filteringфильтрация шумаparticle identificationидентификация частицSuper charm-tau FactoryСупер чарм-тау фабрикаMachine LearningFARICHФАРИЧ
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