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October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.
October 5, 2026
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Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
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In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.

 

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Talking about the [same] revolution? A comparative analysis of main datasets of revolutionary events

Defence and peace economics. 2026. P. 1–31.
Guseinov R., Ustyuzhanin V., Korotayev A.

We conduct a comparative analysis of the main datasets on revolutionary events, including The Nonviolent and Violent Campaigns and Outcomes (NAVCO, versions 1.3 and 2.1), Cross-National Time-Series (CNTS), Revolutionary Episodes Dataset by Mark Beissinger, Revolutionary Database by HSE University (CSRA), and Historical Regime Data (HRD). To compare these sources systematically, we develop several complementary empirical instruments designed to identify and interpret differences across datasets. We show that these differences usually concern event boundaries and the inclusion of borderline cases rather than the substantive results of cross-national quantitative analyses, even though the datasets are not fully equivalent and rely on somewhat different operationalizations, definitions, and coding procedures. Across the major sources, we recover similar temporal and spatial distributions of events, as well as broadly similar structural predictors. We therefore argue that these datasets are empirically highly convergent for many comparative purposes, while not being conceptually identical.

Research target: Political Science, International Relations, and Public Administration
Language: English
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Keywords: revolutionsреволюции
Publication based on the results of:
State Resilience and National Security of Russia in the Context of the World Order Transformation (2025)
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