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Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection.(More)
Recently much attention has been devoted to model selection through regularization methods in regression and classification where features are selected by use of a penalty function (e.g. Lasso in Tibshirani, 1996). While the resulting sparsity leads to more interpretable models, one may want to further incorporate natural groupings or hierarchical(More)
Many patients with traumatic spinal cord injury (SCI) report pain that persists indefinitely and is resistant to available therapeutic approaches. We recently showed that microglia become activated after experimental SCI and dynamically maintain hyperresponsiveness of spinal cord nociceptive neurons and pain-related behaviors. Mechanisms of signaling(More)
We propose an automatic approach to generate street-side 3D photo-realistic models from images captured along the streets at ground level. We first develop a multi-view semantic segmentation method that recognizes and segments each image at pixel level into semantically meaningful areas, each labeled with a specific object class, such as building, sky,(More)
BACKGROUND Methylenetetrahydrofolate reductase (MTHFR) is a critical enzyme in folate metabolism and is involved in DNA methylation, DNA synthesis, and DNA repair. In addition, it is a possible risk factor in neural tube defects (NTDs). The association of the C677T polymorphism in the MTHFR gene and NTD susceptibility has been widely demonstrated, but the(More)
Peripheral nerve injury is known to up-regulate the expression of rapidly-repriming Nav1.3 sodium channel within first-order dorsal root ganglion neurons and second-order dorsal horn nociceptive neurons, but it is not known if pain-processing neurons higher along the neuraxis also undergo changes in sodium channel expression. In this study, we hypothesized(More)
In this paper, we propose the Boosted Lasso (BLasso) algorithm that is able to produce an approximation to the complete regularization path for general Lasso problems. BLasso is derived as a coordinate descent method with a fixed small step size applied to the general Lasso loss function (L1 penalized convex loss). It consists of both a forward step and a(More)