National Research University – Higher School of Economics

About: National Research University – Higher School of Economics is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Computer science. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.

Tradition, morality and community: elaborating Orthodox identity in Putin’s Russia

Abstract: This paper draws upon a number of official, semi-official and other public texts related to the current views of the Russian Church on social and political issues. Overall, in spite of a variety of opinions and nuances, a certain mainstream becomes apparent, as expressed through this body of texts. The most discussed topics include moral values related to the human body (such as abortion, euthanasia, reproductive technologies and sexuality) and issues such as blasphemy, juvenile courts and new technologies of personal registration for Russian citizens. ‘Traditional morality’ has become the signature discourse of the Russian Orthodox Church which is attempting to construct ‘tradition’ by drawing upon a partly imagined ethos of imperial Russia and the late Soviet Union. Traditional family values are central to the church’s rhetoric. The authors of these texts see a presumed decay of traditional values as the main danger that must be opposed. They usually trace the source of this danger directly to t...

ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

Abstract: Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics in road networks. The novel aspects of our approach mainly include spatial attention, temporal attention, and spatial sentinel vectors. The spatial attention takes the graph structure information (e.g., distance between roads) and dynamically adjusts spatial correlation based on road states. The temporal attention is responsible for capturing traffic speed changes, and the sentinel vectors allow the model to retrieve new features from spatially correlated nodes or preserve existing features. The experimental results show that ST-GRAT outperforms existing models, especially in difficult conditions where traffic speeds rapidly change (e.g., rush hours). We additionally provide a qualitative study to analyze when and where ST-GRAT tended to make accurate predictions during rush-hour times.

Lecture notes on interacting quantum fields in de Sitter space

Abstract: We discuss peculiarities of quantum fields in de Sitter space on the example of the self-interacting massive real scalar, minimally coupled to the gravity background. Non-conformal quantum field theories in de Sitter space show very special infrared behavior, which is not shared by quantum fields neither in flat nor in anti-de-Sitter space: in de Sitter space loops are not suppressed in comparison with tree level contributions because there are strong infrared corrections. That is true even for massive fields. Our main concern is the interrelation between these infrared effects, the invariance of the quantum field theory under the de Sitter isometry and the (in)stability of de Sitter invariant states (and of dS space itself) under nonsymmetric perturbations.

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet.

Abstract: This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender The age is estimated as the expected value of top predictions in the neural network In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos The proposed approach is implemented in an Android mobile application It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models