We consider the problem of feature-based face recognition in the setting where only a single example of each face is available for training. The mixture-distance technique we introduce achieves a recognition rate of 95% on a database of 685 people in which each face is represented by 30 measured distances. This is currently the best recorded recognition rate for a featurebased system applied to a database of this size. By comparison, nearest neighbor search using Euclidean distance yields 84%.In our work a novel distance function is constructed based on local second order statistics as estimated by modeling the training data as a mixture of normal densities. We report on the results from mixtures of several sizes.We demonstrate that a flat mixture of mixtures performs as well as the best model and therefore represents an effective solution to the model selection problem. A mixture perspective is dso taken for individual Gaussians to choose between first order (variance) and second order (covariance) models. Here an approximation to flat combination is proposed and seen to perform well in practice.Our results demonstrate that even in the absence of multiple training examples for each class, it is sometimes possible to infer from a statistical model of training data, a significantly improved distance function for use in pattern recognition.

Background: Eighty percent of all breast cancers and almost 90% of breast cancer deaths occur among post-menopausal women. We used a nested case control design to examine the association between nonsteroidal anti-inflammatory drug (NSAID) use and breast cancer occurrence among women over 65 years of age. The cyclooxygenase (COX)-2 enzyme is expressed more in breast cancers than in normal breast tissue. COX-2 inhibition may have a role in breast cancer prevention.

Among elderly patients receiving cardiovascular protection with ASA and pain control with anti-inflammatory drugs, celecoxib may be safer with regards to GI toxicity than NS-NSAIDs.

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