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.

The use of technology platforms as a policy tool to address research challenges and technology transfer

Abstract: The paper analyses technology platforms (TPs) that are seen as a valuable policy instrument to assist a multi-stakeholder formulation and implementation of long-term research and development (R&D) programs in specific technology areas. TP are predominantly initiated by policy-makers to support a wide range of priority technologies through direct funding and indirect support measures, information and technology transfer at economy or industry level. The authors propose a theoretical approach to TPs as a science, technology and innovation policy concept. A taxonomy of TPs is offered that may be useful for policy-makers in designing the R&D support measures through assessing a platform’s risk level. The paper clarifies the position of TPs in the science, technology and innovation policy mix. Through a case-study of Russia’s newly established Technology Platforms, designed after the European Technology Platforms, the authors demonstrate the policy adoption and policy learning approach to application of this tool.

A Finite Analog of the AGT Relation I: Finite W -Algebras and Quasimaps’ Spaces

Abstract: Recently Alday, Gaiotto and Tachikawa [2] proposed a conjecture relating 4-dimensional super-symmetric gauge theory for a gauge group G with certain 2-dimensional conformal field theory. This conjecture implies the existence of certain structures on the (equivariant) intersection cohomology of the Uhlenbeck partial compactification of the moduli space of framed G-bundles on \({\mathbb{P}^2}\) . More precisely, it predicts the existence of an action of the corresponding W-algebra on the above cohomology, satisfying certain properties.

Survey on graph embeddings and their applications to machine learning problems on graphs

Abstract: Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

Metrics for the elements of intellectual capital in an economy driven by knowledge

Abstract: Purpose – The purpose of this paper is to explore the plausibility of six elements of IC and justify the measurement ability of a set of indicators based on publicly available data for each of the proposed element in order to provide tools to managers for their decision-making process in knowledge management (KM). Design/methodology/approach – Core company's intangibles are combined into six intellectual capital (IC) elements that appear after the division of each of the traditional components (human, structural and relational capital (RC)). The human capital includes management and human resources capabilities (HRC). Structural capital is divided into innovation and internal process capabilities (IPC). RC contains networking capabilities and customer loyalty. In drawing on the relevant literature each element is described through a set of indicators collected from publicly available data. The validity of proposed IC model is justified through structural equation modeling. Each element is tested on a samp...

A Well-Organized Play: Symbolic Politics and the Effect of the Pro-Putin Rallies

Abstract: Pro-Putin rallies before the 2012 presidential elections became campaign venues in which the Kremlin used political symbols—woven into a narrative of nationalism and tradition—to define and activate core voters across the Russian Federation.