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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.
April 27, 2026
Fair Division: How Mathematics Helps to Divide the Indivisible
How can items be allocated among participants so that no one feels short-changed? Alexander Karpov, Assistant Professor at the Faculty of Economic Sciences, and his Singaporean colleague, Prof. Warut Suksompong, set out to find a mathematical answer to this question. In this interview, they discuss how a model of rational preferences is constructed, why one cannot rely on a simple sum of values, and where an algorithm that asks a minimal number of questions can be useful.
April 24, 2026
Electronics of the Future: Why Superconductors and Spintronics Work Together
It was once believed that superconductivity and magnetism avoided each other like the devil avoids holy water. However, modern nanostructures prove the opposite. A Russian theoretical physicist and Indian experimentalists have joined forces to create the electronics of the future—free from energy losses. Nataliya Pugach, Professor at the School of Electronic Engineering at HSE MIEM and Leading Research Fellow at the Quantum Nanoelectronics Laboratory, explains how a long-standing acquaintance in Cambridge grew into a mirror laboratory project with the Indian Institute of Technology Bombay (IIT Bombay), how superconducting spintronics works, and what surprises a researcher in India beyond the university campus.

 

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Remediation of the NMR data of natural glycans

International Journal of Biological Macromolecules. 2024. Vol. 282. Article 137042.
Kazantsev K., Toukach P.

Primary structure elucidation in glycobiology is strongly affected by published structure-reporting NMR signals, especially on the 13C nucleus. The glycan NMR simulation accuracy and machine learning outcome depend on the quality of the NMR signal assignment in glycan databases. Within our work on improving the data quality in the Carbohydrate Structure Database (CSDB), we have applied a systematic search for inconsistencies in the published NMR data. The search was based on a bulk comparison between the experimental and simulated 13C NMR chemical shifts and manual analysis of the mismatches. On the basis of this analysis, CSDB was remediated by marking and correcting the NMR errors found in 272 structure elucidation reports published over the past 40 years.

Research target: Chemistry
Language: English
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DOI
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Keywords: качество данных data qualityотнесение сигналовЯМР спектроскопия13C NMR errorsglycan databasesglycobiologyNMR assignmentCarbohydrate Structure Databaseгликобиологияуглеводные базы данных
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