Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Yahoo! Learning to Rank Challenge Overview

Abstract: Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets.

Learning a Spatially Smooth Subspace for Face Recognition

Abstract: Subspace learning based face recognition methods have attracted considerable interests in recently years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE), marginal fisher analysis (MFA) and local discriminant embedding (LDE). These methods consider an n1timesn2 image as a vector in Rn 1 timesn 2 and the pixels of each image are considered as independent. While an image represented in the plane is intrinsically a matrix. The pixels spatially close to each other may be correlated. Even though we have n1xn2 pixels per image, this spatial correlation suggests the real number of freedom is far less. In this paper, we introduce a regularized subspace learning model using a Laplacian penalty to constrain the coefficients to be spatially smooth. All these existing subspace learning algorithms can fit into this model and produce a spatially smooth subspace which is better for image representation than their original version. Recognition, clustering and retrieval can be then performed in the image subspace. Experimental results on face recognition demonstrate the effectiveness of our method.

Learning Graph Matching

Abstract: As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many formulations of this problem can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility and a quadratic term encodes edge compatibility. The main research focus in this theme is about designing efficient algorithms for approximately solving the quadratic assignment problem, since it is NP-hard. In this paper we turn our attention to a different question: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the 'labels' are matches between them. Our experimental results reveal that learning can substantially improve the performance of standard graph matching algorithms. In particular, we find that simple linear assignment with such a learning scheme outperforms Graduated Assignment with bistochastic normalisation, a state-of-the-art quadratic assignment relaxation algorithm.

Community Detection and Mining in Social Media

Abstract: This book, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website http://dmml.asu.edu/cdm/ for the latest information.

Hypersensitivity pneumonitis: insights in diagnosis and pathobiology.

Abstract: Hypersensitivity pneumonitis (HP) is a complex syndrome resulting from repeated exposure to a variety of organic particles. HP may present as acute, subacute, or chronic clinical forms but with frequent overlap of these various forms. An intriguing question is why only few of the exposed individuals develop the disease. According to a two-hit model, antigen exposure associated with genetic or environmental promoting factors provokes an immunopathological response. This response is mediated by immune complexes in the acute form and by Th1 and likely Th17 T cells in subacute/chronic cases. Pathologically, HP is characterized by a bronchiolocentric granulomatous lymphocytic alveolitis, which evolves to fibrosis in chronic advanced cases. On high-resolution computed tomography scan, ground-glass and poorly defined nodules, with patchy areas of air trapping, are seen in acute/subacute cases, whereas reticular opacities, volume loss, and traction bronchiectasis superimposed on subacute changes are observed in chronic cases. Importantly, subacute and chronic HP may mimic several interstitial lung diseases, including nonspecific interstitial pneumonia and usual interstitial pneumonia, making diagnosis extremely difficult. Thus, the diagnosis of HP requires a high index of suspicion and should be considered in any patient presenting with clinical evidence of interstitial lung disease. The definitive diagnosis requires exposure to known antigen, and the assemblage of clinical, radiologic, laboratory, and pathologic findings. Early diagnosis and avoidance of further exposure are keys in management of the disease. Corticosteroids are generally used, although their long-term efficacy has not been proved in prospective clinical trials. Lung transplantation should be recommended in cases of progressive end-stage illness.