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.

Effects of daily consumption of honey solution on hematological indices and blood levels of minerals and enzymes in normal individuals.

Abstract: Seven men and three women (mean age, 31.2 years; range, 20-45 years) received a strictly controlled regular diet during a 2-week control period, followed by the regular diet supplemented with daily consumption of 1.2 g/kg body weight honey dissolved in 250 ml of water during a 2-week test period. At the end of each period, overnight fasting blood samples were withdrawn for assays of blood glucose, blood minerals, vitamin C, beta-carotene, uric acid, glutathione reductase, immunoglobulin E, hemoglobin, blood indices and cells, serum ferritin, serum iron, and iron-binding capacity. Results showed that honey increased antioxidant agents. It increased blood vitamin C concentration by 47%, beta-carotene by 3%, uric acid by 12%, and glutathione reductase by 7%. Honey increased serum iron by 20% and decreased plasma ferritin by 11%. It increased the percentage of monocytes by 50%, and increased lymphocyte and eosinophil percentages slightly. Honey reduced serum immunoglobulin E by 34% and increased serum copper by 33%. It decreased aspartate transaminase by 22% and alanine transaminase by 18%. Honey markedly reduced lactic acid dehydrogenase by 41%, decreased creatinine kinase by 33%, and reduced fasting blood sugar by 5%. It caused slight elevations in blood zinc and magnesium, hemoglobin, and packed cell volume. It may be concluded that honey increased antioxidant agents, serum iron and blood indices, and trace elements and decreased immunoglobulin E, liver and muscle enzymes, and fasting blood sugar in healthy subjects.

Privacy-Preserving Data Publishing

Abstract: Privacy is an important issue when one wants to make use of data that involves individuals' sensitive information. Research on protecting the privacy of individuals and the confidentiality of data has received contributions from many fields, including computer science, statistics, economics, and social science. In this paper, we survey research work in privacy-preserving data publishing. This is an area that attempts to answer the problem of how an organization, such as a hospital, government agency, or insurance company, can release data to the public without violating the confidentiality of personal information. We focus on privacy criteria that provide formal safety guarantees, present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. Many challenges still remain. This survey provides a summary of the current state-of-the-art, based on which we expect to see advances in years to come.

A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

Abstract: We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to construct a hypersphere that contains most of the normal examples, such that the volume of this sphere is as small as possible, while at the same time the margin between the surface of this sphere and the outlier training data is as large as possible. This can result in a closed and tight boundary around the normal data. To build such a sphere, we only need to solve a convex optimization problem that can be efficiently solved with the existing software packages for training nu-support vector machines. Experimental results are provided to validate the effectiveness of the proposed algorithm.

Dengue fever: diagnosis and treatment.

Abstract: Dengue fever is a common tropical infection. This acute febrile illness can be a deadly infection in cases of severe manifestation, causing dengue hemorrhagic shock. In this brief article, I will summarize and discuss the diagnosis and treatment of this disease. For diagnosis of dengue, most tropical doctors make use of presumptive diagnosis; however, the definite diagnosis should be based on immunodiagnosis or viral study. Focusing on treatment, symptomatic and supportive treatment is the main therapeutic approach. The role of antiviral drugs in the treatment of dengue fever has been limited, but is currently widely studied.

Large-Scale Unusual Time Series Detection

Abstract: It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on server performance are collected every hour for each of thousands of servers. We wish to identify servers that are behaving unusually. We compute a vector of features on each time series, measuring characteristics of the series. The features may include lag correlation, strength of seasonality, spectral entropy, etc. Then we use a principal component decomposition on the features, and use various bivariate outlier detection methods applied to the first two principal components. This enables the most unusual series, based on their feature vectors, to be identified. The bivariate outlier detection methods used are based on highest density regions and α-hulls.