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.

Venous Thromboembolism in the Hematologic Malignancies

Abstract: Patients with hematologic malignancies are at high risk of thrombotic or hemorrhagic complications. The incidence of these events is greatly variable and is influenced by many factors, including the type of disease, the type of chemotherapy, and the use of a central venous device. As in solid tumors, a number of clinical risk factors have been identified and contribute to the increasing thrombotic rate in hematologic malignancies. Biologic properties of the tumor cells can influence the hypercoagulable state of patients with these malignancies by several mechanisms. Of interest, oncogenes responsible for neoplastic transformation in leukemia also may be involved in clotting activation. Epidemiologic data allow an estimate of the incidence of venous thromboembolism (VTE) in acute leukemia, lymphomas, and multiple myeloma (MM). In this review, we focus on the epidemiology, pathogenesis, and VTE management in these three hematologic malignancies. No recommendation for routine thromboprophylaxis in these conditions, with the exception of MM, is available. Large, prospective, randomized clinical trials are needed to establish the best practice for thromboprophylaxis and treatment of VTE in these types of cancers.

Adaptive bootstrapping of recommender systems using decision trees

Abstract: Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.

Online domain adaptation of a pre-trained cascade of classifiers

Abstract: Many classifiers are trained with massive training sets only to be applied at test time on data from a different distribution. How can we rapidly and simply adapt a classifier to a new test distribution, even when we do not have access to the original training data? We present an on-line approach for rapidly adapting a “black box” classifier to a new test data set without retraining the classifier or examining the original optimization criterion. Assuming the original classifier outputs a continuous number for which a threshold gives the class, we reclassify points near the original boundary using a Gaussian process regression scheme. We show how this general procedure can be used in the context of a classifier cascade, demonstrating performance that far exceeds state-of-the-art results in face detection on a standard data set. We also draw connections to work in semi-supervised learning, domain adaptation, and information regularization.

Real and Perceived Attitude Agreement in Social Networks

Abstract: It is often asserted that friends and acquaintances have more similar beliefs and attitudes than do strangers; yet empirical studies disagree over exactly how much diversity of opinion exists within local social networks and, relatedly, how much awareness individuals have of their neighbors' views. This article reports results from a network survey, conducted on the Facebook social networking platform, in which participants were asked about their own political attitudes, as well as their beliefs about their friends' attitudes. Although considerable attitude similarity exists among friends, the results show that friends disagree more than they think they do. In particular, friends are typically unaware of their disagreements, even when they say they discuss the topic, suggesting that discussion is not the primary means by which friends infer each other's views on particular issues. Rather, it appears that respondents infer opinions in part by relying on stereotypes of their friends and in part by projecting their own views. The resulting gap between real and perceived agreement may have implications for the dynamics of political polarization and theories of social influence in general.

Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index

Abstract: Quantitative assessments of the degree to which the modeled behavior of a system matches with the observations provide an evaluation of the model’s predictive abilities. In this context, the Nash–Sutcliffe efficiency index is widely used in water resources sector to assess the performance of a hydrologic model. Through a series of results, this technical note demonstrates that this index alone is not adequate in describing the performance of a model. It is shown that relatively poor models can give a high value of the index and vice-versa. Thus, it is advisable to employ the other statistical measures before arriving at a definite conclusion about the performance of a hydrologic model.