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.

Core decomposition of uncertain graphs

Abstract: Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of existence. The fact that core decomposition can be computed efficiently in deterministic graphs does not guarantee efficiency in uncertain graphs, where even the simplest graph operations may become computationally intensive. Here we show that core decomposition of uncertain graphs can be carried out efficiently as well.We extensively evaluate our definitions and methods on a number of real-world datasets and applications, such as influence maximization and task-driven team formation.

Honey and Microbial Infections: A Review Supporting the Use of Honey for Microbial Control

Abstract: Honey has been used as a medicine throughout the ages and has recently been reintroduced to modern medical practice. Much of the research to date has addressed honey's antibacterial properties and its effects on wound healing. Laboratory studies and clinical trials have shown that honey is an effective broad-spectrum antibacterial agent. Honey antimicrobial action explains the external and internal uses of honey. Honey has been used to treat adult and neonatal postoperative infection, burns, necrotizing fasciitis, infected and nonhealing wounds and ulcers, boils, pilonidal sinus, venous ulcers, and diabetic foot ulcers. These effects are ascribed to honey's antibacterial action, which is due to acidity, hydrogen peroxide content, osmotic effect, nutritional and antioxidants content, stimulation of immunity, and to unidentified compounds. When ingested, honey also promotes healing and shows antibacterial action by decreasing prostaglandin levels, elevating nitric oxide levels, and exerting prebiotic effects. These factors play a major role in controlling inflammation and promoting microbial control and healing processes. This article reviews data supporting the effectiveness of natural honey in eradicating human pathogens and discusses the mechanism of actions.

Antinociceptive and Anti-Inflammatory Activities of the Ethanol Extract of Annona muricata L. Leaves in Animal Models

Abstract: Antinociceptive and anti-inflammatory activities of the ethanol extract from Annona muricata L. leaves were investigated in animal models. The extract delivered per oral route (p.o.) reduced the number of abdominal contortions by 14.42% (at a dose of 200 mg/kg) and 41.41% (400 mg/kg). Doses of 200 and 400 mg/kg (p.o) inhibited both phases of the time paw licking: first phase (23.67% and 45.02%) and the second phase (30.09% and 50.02%), respectively. The extract (p.o.) increased the reaction time on a hot plate at doses of 200 (30.77% and 37.04%) and 400 mg/kg (82.61% and 96.30%) after 60 and 90 minutes of treatment, respectively. The paw edema was reduced by the ethanol extract (p.o.) at doses of 200 (23.16% and 29.33%) and 400 mg/kg (29.50% and 37.33%) after 3 to 4 h of application of carrageenan, respectively. Doses of 200 and 400 mg/kg (p.o.), administered 4 h before the carrageenan injection, reduced the exudate volume (29.25 and 45.74%) and leukocyte migration (18.19 and 27.95%) significantly. These results suggest that A. muricata can be an active source of substances with antinociceptive and anti-inflammatory activities.

Learning to re-rank: query-dependent image re-ranking using click data

Abstract: Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features. Image search engines therefore primarily rely on static and textual features for ranking. Visual features are mainly used for secondary tasks such as finding similar images. Second, image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts. Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker. Finally, a static ranker is typically built to handle disparate user queries. The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results. We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data.We hypothesize that images clicked in response to a query are mostly relevant to the query. We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list. Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image, and combines it with the original ranking score. Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries.