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.

Review: Formal concept analysis in knowledge processing: A survey on applications

Abstract: This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.

Monopolistic Competition: Beyond the Constant Elasticity of Substitution

Abstract: We propose a model of monopolistic competition with additive preferences and variable marginal costs. Using the concept of “relative love for variety,” we provide a full characterization of the free-entry equilibrium.When the relative love for variety increases with individual consumption, the market generates pro-competitive effects. When it decreases, the market mimics anti-competitive behavior. The constant elasticity of substitution is the only case in which all competitive effects are washed out. We also show that our results hold true when the economy involves several sectors, firms are heterogeneous, and preferences are given by the quadratic utility and the translog.

Spatially Adaptive Computation Time for Residual Networks

Abstract: This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.