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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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Triclusters of Close Values for the Analysis of 3D Data

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894–902.
Egurnov D., Ignatov D. I.

Abstract: The paper deals with the problem of triclustering in multivalued triadic contexts in termsof one multidimensional extension of formal concept analysis; triclustering can be viewed as asearch for dense subtensors in three-dimensional tensors over the field of real numbers. Twomethods are proposed for solving this problem, namely, NOAC—a version of the OACtriclustering method for numerical data based on delta operators—and a triadic version ofthek-means method with an improved metric based on Manhattan distance andproximity predicates in each of the three dimensions. Numerical experiments are carried out bothon real and synthetic data and confirm the superiority of the NOAC method in terms of theperformance criteria for the resulting triclusters. © 2022, Pleiades Publishing, Ltd.

Research target: Computer Science Mathematics
Language: English
DOI
Keywords: Formal Concept Analysistriclustering3D tensormultivalued context
Publication based on the results of:
Development of mathematical models and methods for natural language processing, knowledge discovery in data and recommender systems (2022)
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