RankMerging: A supervised learning-to-rank framework to predict links in large social network

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.Comment: 43 pages, published in Machine Learning Journa

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Multi-level DEA Approach in Research Evaluation

It is well known that the discrimination power of DEA models will be diminishing if too many inputs or outputs are used. It is a dilemma if the decision makers want to select comprehensive indicators to present a relatively holistic evaluation using DEA. In this work we show that by utilizing hierarchical structures of input-output data DEA can handle quite large numbers of inputs and outputs. We present two approaches in a pilot evaluation of 15 institutes for basic research in Chinese Academy of Sciences using DEA models

Efficiency frontier on Japanese banking system

Since the emergence of the efficiency frontier techniques, a series of comparisons between the methods that led to the resultant efficiency has been presented. In this paper, data from 99 Japanese banks are used in order to prove the applicability of efficiency frontier analysis on the East-Asian financial system and to reveal the differences between inter and intra-regional banks, showing the effect of the present financial crisis on the efficiency of the studied banks. DEA and FDH are used to determine the technical and scale efficiency of the analyzed banks and also it compares fully efficient banks by ranking them through the super-efficiency notion.This work was co-financed from the European Social Fund through the Sectoral Operational Programme Human Resources Development 2007–2013, project number POSDRU 159/1.5/S/134197 “Performance and excellence in doctoral and postdoctoral research in Romanian economics science domain”