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June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
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On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
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Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.

 

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Estimation of the signal subspace without estimation of the inverse covariance matrix

2010. No. 2010-050.
Panov V.
Let a high-dimensional random vector $\vX$ be represented as a sum of two components - a  signal $\vS$ that belongs to some low-dimensional linear subspace $\S$,  and a noise component $\vN$.  This paper presents a new approach for estimating the subspace $\S$ based on the ideas of the Non-Gaussian Component Analysis. Our approach avoids the technical difficulties that usually appear in similar methods - it requires neither the estimation of the inverse covariance  matrix of $\vX$ nor the estimation of the covariance matrix of $\vN$.
Priority areas: mathematics
Language: English
Text on another site
Keywords: снижение размерностиdimension reductionnon-Gaussian componentssignal subspaceнегауссовские компонентыпространство сигнала
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