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October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
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HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.

 

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Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation

European Physical Journal: Special Topics. 2026. P. 1–16.
Vlasenko D., Saranskaia I., Zakharov D.

Hypergraphs provide a natural framework for representing neurophysiological interactions distributed across sets of sensors. A key methodological question is how hyperedges should be defined from frequency-resolved electroencephalography/magnetoencephalography (EEG/MEG) data. We demonstrate a construction strategy in which hyperedges are obtained from canonical coherence (caCOH), an extension of coherence that estimates coupling between multidimensional signal spaces. To our knowledge, this is the first work to construct hypergraphs directly from a multivariate connectivity measure specifically designed for frequency-resolved neurophysiological analysis. We propose two caCOH-based representations: a one-to-space hypergraph, where each external signal defines a hyperedge over the EEG/MEG sensor space, and a space-to-space hypergraph, where two multidimensional signal spaces are represented by a single hyperedge. We evaluate the approach in controlled simulations with known coupling frequencies and varying signal-to-noise ratio (SNR). Compared with graphs based on magnitude-squared coherence (MSC), caCOH-based hypergraphs showed statistically higher target–baseline contrasts at almost all SNR levels, indicating stronger recovery of coupling frequencies. They also recovered sensor-level spatial patterns associated with the simulated sources. In addition, one-to-space and space-to-space representations reduced 610 MSC edges per frequency to ten hyperedges and one hyperedge, respectively. These results establish multivariate spectral connectivity as a natural methodological basis for EEG/MEG hypergraphs.

Research target: Mathematics Computer Science Psychology
Language: English
DOI
Text on another site
Keywords: EEGЭЭГMEGМЭГhypergraphsгиперграфыcaCOHfrequency-resolved connectivitymultivariate connectivityКаноническая когерентность caCOHЧастотно-разрешающая связностьМногомерная связность
Publication based on the results of:
Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)
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