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June 17, 2026
Population Lifespan Is Governed by Mathematical Laws
Researchers at HSE University and MSU have established a universal law governing the time to extinction of a population in a random environment. Their analysis of the evolution of branching processes—complex probabilistic systems—shows that, regardless of the initial population size, extinction follows strict mathematical laws. The results have been published in the Journal of Applied Probability.
June 16, 2026
Taking Stock Without Euphemisms: Experts Propose Solutions for Russias Foreign and Defence Policy
The recent 34th Assembly of the Council on Foreign and Defence Policy (SVOP) presented analytical approaches to emerging global challenges and developed practical recommendations in the context of a transforming world order. Experts from HSE University took an active part in the sessions and closed briefings.
June 15, 2026
Sociologists: Conservative Consumers Dominate Russian Middle Class
The Russian middle class cannot be regarded as a homogeneous and uniformly stable social group. Similar income levels often mask significant differences in financial strategies, lifestyles, and levels of economic security. This is the conclusion reached by sociologists at HSE University. The study has been published in Voprosy Ekonomiki.

 

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Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

Expert Systems: The Journal of Knowledge Engineering. 2013. Vol. 30. No. 4. P. 294–305.
Mirkin B., Nascimento S., Felizardo R.

This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state-of-the-art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo-inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

Research target: Computer Science
Priority areas: IT and mathematics
Language: English
Full text
Text on another site
Keywords: spectral fuzzy clusteringnumber of clustersспектральный кластерLaplacian normalizationчисло кластеровнормализация Лапласа
Publication based on the results of:
An investigation of new methods of mathematical modelling and mechanism design in the social, economic and political sciences (2013)
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