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.

Generalized distances between rankings

Abstract: Spearman's footrule and Kendall's tau are two well established distances between rankings. They, however, fail to take into account concepts crucial to evaluating a result set in information retrieval: element relevance and positional information. That is, changing the rank of a highly-relevant document should result in a higher penalty than changing the rank of an irrelevant document; a similar logic holds for the top versus the bottom of the result ordering. In this work, we extend both of these metrics to those with position and element weights, and show that a variant of the Diaconis-Graham inequality still holds - the generalized two measures remain within a constant factor of each other for all permutations.We continue by extending the element weights into a distance metric between elements. For example, in search evaluation, swapping the order of two nearly duplicate results should result in little penalty, even if these two are highly relevant and appear at the top of the list. We extend the distance measures to this more general case and show that they remain within a constant factor of each other.We conclude by conducting simple experiments on web search data with the proposed measures. Our experiments show that the weighted generalizations are more robust and consistent with each other than their unweighted counter-parts.

Obesity and Associated Factors — Kingdom of Saudi Arabia, 2013

Abstract: Introduction Data on obesity from the Kingdom of Saudi Arabia (KSA) are nonexistent, making it impossible to determine whether the efforts of the Saudi Ministry of Health are having an effect on obesity trends. To determine obesity prevalence and associated factors in the KSA, we conducted a national survey on chronic diseases and their risk factors.

The Role of Tumor Microenvironment in Chemoresistance: To Survive, Keep Your Enemies Closer

Abstract: Chemoresistance is a leading cause of morbidity and mortality in cancer and it continues to be a challenge in cancer treatment. Chemoresistance is influenced by genetic and epigenetic alterations which affect drug uptake, metabolism and export of drugs at the cellular levels. While most research has focused on tumor cell autonomous mechanisms of chemoresistance, the tumor microenvironment has emerged as a key player in the development of chemoresistance and in malignant progression, thereby influencing the development of novel therapies in clinical oncology. It is not surprising that the study of the tumor microenvironment is now considered to be as important as the study of tumor cells. Recent advances in technological and analytical methods, especially ‘omics’ technologies, has made it possible to identify specific targets in tumor cells and within the tumor microenvironment to eradicate cancer. Tumors need constant support from previously ‘unsupportive’ microenvironments. Novel therapeutic strategies that inhibit such microenvironmental support to tumor cells would reduce chemoresistance and tumor relapse. Such strategies can target stromal cells, proteins released by stromal cells and non-cellular components such as the extracellular matrix (ECM) within the tumor microenvironment. Novel in vitro tumor biology models that recapitulate the in vivo tumor microenvironment such as multicellular tumor spheroids, biomimetic scaffolds and tumor organoids are being developed and are increasing our understanding of cancer cell-microenvironment interactions. This review offers an analysis of recent developments on the role of the tumor microenvironment in the development of chemoresistance and the strategies to overcome microenvironment-mediated chemoresistance. We propose a systematic analysis of the relationship between tumor cells and their respective tumor microenvironments and our data show that, to survive, cancer cells interact closely with tumor microenvironment components such as mesenchymal stem cells and the extracellular matrix.

Scalable inference in latent variable models

Abstract: Latent variable techniques are pivotal in tasks ranging from predicting user click patterns and targeting ads to organizing the news and managing user generated content. Latent variable techniques like topic modeling, clustering, and subspace estimation provide substantial insight into the latent structure of complex data with little or no external guidance making them ideal for reasoning about large-scale, rapidly evolving datasets. Unfortunately, due to the data dependencies and global state introduced by latent variables and the iterative nature of latent variable inference, latent-variable techniques are often prohibitively expensive to apply to large-scale, streaming datasets.In this paper we present a scalable parallel framework for efficient inference in latent variable models over streaming web-scale data. Our framework addresses three key challenges: 1) synchronizing the global state which includes global latent variables (e.g., cluster centers and dictionaries); 2) efficiently storing and retrieving the large local state which includes the data-points and their corresponding latent variables (e.g., cluster membership); and 3) sequentially incorporating streaming data (e.g., the news). We address these challenges by introducing: 1) a novel delta-based aggregation system with a bandwidth-efficient communication protocol; 2) schedule-aware out-of-core storage; and 3) approximate forward sampling to rapidly incorporate new data. We demonstrate state-of-the-art performance of our framework by easily tackling datasets two orders of magnitude larger than those addressed by the current state-of-the-art. Furthermore, we provide an optimized and easily customizable open-source implementation of the framework1.

Atrial Fibrillation After Isolated Coronary Surgery Affects Late Survival

Abstract: Background— Atrial fibrillation (AF) after coronary artery bypass graft surgery is a difficult problem and a continuing source of morbidity and mortality. However, the prognostic implications of postoperative AF are still in dispute. Our aim was to ascertain the impact of AF after coronary artery bypass graft on postoperative survival and to assess its prognostic role in cause-specific mortality. Methods and Results— We conducted a prospective observational study of 1832 patients undergoing isolated coronary artery bypass graft between January 2000 and December 2005 at 2 cardiac surgery centers in northern Italy. Patients affected by postoperative AF were identified and followed up until death or study end (April 30, 2007). A total of 570 patients (31%) developed AF after coronary surgery. Patients affected by postoperative AF experienced a longer hospital stay (7 days [25th to 75th percentile, 7 to 10 days] versus 7 days [25th to 75th percentile, 6 to 8 days]; P<0.001). Hospital mortality also was higher...