2 resultados para on-disk data layout

em DRUM (Digital Repository at the University of Maryland)


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Observational studies demonstrate strong associations between deficient serum vitamin D (25(OH)D) levels and cardiovascular disease. To further examine the association between vitamin D and hypertension (HTN), data from the 2003-2006 National Health and Nutrition Examination Survey were analyzed to assess whether the association between vitamin D and HTN varies by sufficiency of key co-nutrients necessary for metabolic vitamin D reactions to occur. Logistic regression results demonstrate independent effect modification by calcium, magnesium, and vitamin A on the association between vitamin D and HTN. Among non-pregnant adults with adequate renal function, those with low levels of calcium, magnesium, and vitamin D levels had 1.75 times the odds of HTN compared to those with sufficient vitamin D levels (p = <0.0001). Additionally, participants with low levels of calcium, magnesium, vitamin A, and vitamin D had 5.43 times the odds of HTN compared to those with vitamin D sufficiency (p = 0.0103).

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Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.