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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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Developing and Using Indicators of Emerging and Enabling Technologies

Ch. 15. P. 349–380.
Gokhberg L., Fursov K., Miles I. D., Perani G.

The chapter focuses on the measurement of emerging technologies that have the potential to transform wide swathes of social and economic activity. The relevance of the topic is explained by the fact that the existing mechanisms of statistical accounting for a number of reasons fail to measure technologies that are at an early stage of development and still do not have specific applications. This results in a reduced ability to manage technology progress. Analyzing the lessons learned from statistical work around ICT and biotechnology, the authors propose a "three-dimensional" approach to the classification of technologies that covers all stages of the R&D cycle — from an idea to market launch of an end-user product or service. The basic components in measuring this area are the scientific base or origin of a given technology, its applications and the socio-economic impact. The authors demonstrate the scope for using technology Foresight to orient statistical work, and suggest that a combination of improved statistical monitoring of ongoing developments, and Foresight analysis of anticipated technology and technology applications, represents a powerful approach to achieving early footholds by way of indicators of emerging technologies in concrete circumstances. The chapter outlines the main requirements for such an approach to be effectively implemented and its results utilized; it concludes with proposals for future methodological and conceptual development.

 

 

 
Language: English
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Text on another site
Keywords: Indicators of Emerging and Enabling Technologiestechnology statisticsinnovation indicatorsS&T indicators
Publication based on the results of:
Development of novel approaches to the analysis and modelling behavior of innovation actors (2013)

In book

Handbook Of Innovation Indicators And Measurement
Handbook Of Innovation Indicators And Measurement
Northampton, Cheltenham: Edward Elgar Publishing, 2013.
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