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.

Respondent-driven sampling as Markov chain Monte Carlo

Abstract: Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and the recruitment procedure on the variance of RDS estimates. Past work has assumed that the variance of RDS estimates is primarily affected by segregation between healthy and infected individuals. We examine an illustrative model to show that this is not necessarily the case, and that bottlenecks anywhere in the networks can substantially affect estimates. We also show that variance is inflated by a common design feature in which the sample members are encouraged to recruit multiple future sample members. The paper concludes with suggestions for implementing and evaluating RDS studies.

Search engine with augmented relevance ranking by community participation

Abstract: Embodiments of the present invention provide systems and methods for ranking a result set. The method according to one embodiment comprises selecting an item from the result set, selecting a user profile from one or more user profiles and selecting one or more items of personalized information from the selected user profile. A rank is calculated for the item on the basis of the selected one or more items of personalized information and the selected item in the result set is ranked in accordance with the calculated rank.

Total Hip Arthroplasty for Congenital Hip Disease

Abstract: Background: It is generally agreed that the clinical and radiographic results of total hip replacement performed for degenerative arthritis secondary to congenital hip disease vary depending on the severity of the anatomical abnormality. In this study, we report the mid-term and long-term clinical and radiographic results of total hip arthroplasty performed for each of the three different types of congenital hip disease. Methods: Between 1976 and 1994, the senior author performed 229 consecutive primary total hip arthroplasties in 168 patients with osteoarthritis secondary to congenital hip disease. Seventy-six hips were dysplastic, sixty-nine had a low dislocation, and eighty-four had a high dislocation. The Charnley low-friction technique was performed in 178 hips, and the so-called hybrid technique was performed in forty-six hips. Cementless arthroplasty was used in only five hips. Results: After a minimum of seven years of follow-up, the rates of revision of the acetabular components were 15% in the dysplastic hips, 21% in the hips with a low dislocation, and 14% in those with a high dislocation. The rates of revision of the femoral components were 14%, 14%, and 16%, respectively. Survivorship analysis predicted an overall rate of prosthetic survival at fifteen years of 88.8% ± 4.8% in the dysplastic hips, 73.9% ± 7.2% in the hips with a low dislocation, and 76.4% ± 8.1% in those with a high dislocation. Conclusions: An understanding of the anatomical abnormalities and the use of appropriate techniques and implants make total hip arthroplasty feasible for treatment of the three types of congenital hip disease. In patients with a low dislocation, the major technical problem is reconstruction of the natural acetabulum. In those with a high dislocation, the challenge is to place the acetabular component inside the reconstructed true acetabulum and to use an appropriate femoral implant in the hypoplastic narrow femoral diaphysis. Level of Evidence: Prognostic study, Level II-1 (retrospective study). See Instructions to Authors for a complete description of levels of evidence.

Managing mangroves in Bangladesh: A strategy analysis

Abstract: Bangladesh, favoured by a tropical climate, houses the world's largest stretch of mangroves forests (Sundarbans Reserved Forest) and plantations. Around half of the forests of the country occur in the coastal zone. People extract various goods and services from the mangroves. Nevertheless the mangrove forests are depleting. Although the extent of the Sundarbans forest has not changed much, its decline is of a qualitative nature. Mangrove plantations are increasing in area but they are losing growing stock. To arrest this, Bangladesh has adopted several strategies.The ‘Sustainable Ecosystem Management’ strategy has now been adopted instead of the ‘Sustained Yield Principle’. Biodiversity conservation and enhancement has been taken as a key management goal. A zoning system is being developed for both production and protection purposes. The government facilitates alternative income for the local people by generating activities for the communities which are dependent on the forest. Different non-governmental organizations collaborate with the government in reducing the local people's dependence on the forest. Coastal plantations are erected to protect people from cyclones and to make the land more suitable for habitation. Through this greening of the coastal belt tree plantation is encouraged in coastal villages. Coastal embankments are being planted and leased to poor settlers in exchange for routine maintenance of the embankments. Plantations on newly accreted mud flats help in stabilizing the land, which can later on be settled by victims of erosion elsewhere. These adopted management measures do not only contribute to forestry resource management but also to the social, environmental and economic wellbeing of the coastal communities. These efforts are at present being integrated into an Integrated Coastal Zone Management (ICZM) project.

Online aggregation for large MapReduce jobs

Abstract: In online aggregation, a database system processes a user's aggregation query in an online fashion. At all times during processing, the system gives the user an estimate of the final query result, with the confidence bounds that become tighter over time. In this paper, we consider how online aggregation can be built into a MapReduce system for large-scale data processing. Given the MapReduce paradigm's close relationship with cloud computing (in that one might expect a large fraction of MapReduce jobs to be run in the cloud), online aggregation is a very attractive technology. Since large-scale cloud computations are typically pay-as-you-go, a user can monitor the accuracy obtained in an online fashion, and then save money by killing the computation early once sufficient accuracy has been obtained.