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News
May 18, 2026
The 'Second Shift' Is Not Why Women Avoid News
Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.
May 15, 2026
Preserving Rationality in a Period of Turbulence
The HSE International Laboratory for Logic, Linguistics and Formal Philosophy studies logic and rationality in a transformed world characterised by a diversity of logical systems and rational agents. The laboratory supports and develops academic ties with Russian and international partners. The HSE News Service spoke with the head of the laboratory, Prof. Elena Dragalina-Chernaya, about its work.
May 15, 2026
‘All My Time Is Devoted to My Dissertation
Ilya Venediktov graduated from the Master’s programme at the HSE Tikhonov Moscow Institute of Electronics and Mathematics through the combined Master’s–PhD track and is currently studying at the HSE Doctoral School of Engineering Sciences. At present, he is undertaking a long-term research internship at the University of Science and Technology of China in Hefei, where he is preparing his dissertation. In this interview, he explains how an internship differs from an academic mobility programme, discusses his research topic, and describes the daily life of a Russian doctoral student in China.

 

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Non-Gaussian Component Analysis: New Ideas, New Proofs, New Applications

2010. No. 2010-026.
Panov V.
In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector $\vX$ can be represented as a sum of two components - a low-dimensional signal $\vS$ and a noise component $\vN$. We show that this assumption enables us for a special representation for the density function of $\vX$. Similar facts are proven in original papers about NGCA (\cite{Blan06}, \cite{Died07},  \cite{Kaw07}), but our representation differs from the previous versions. The new form helps us to provide a strong theoretical support for the algorithm; moreover, it gives some ideas about new approaches in multidimensional statistical analysis. In this paper, we establish important results for the NGCA procedure using the new representation, and show benefits of our method.
Priority areas: mathematics
Language: English
Text on another site
Keywords: классификацияclassificationснижение размерностиValue at Riskdimension reductionnon-Gaussian componentsValue at RiskEDR subspaceнегауссовские компоненты
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