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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)
Compelling evidence indicates that the CRISPR-Cas system protects prokaryotes from viruses and other potential genome invaders. This adaptive prokaryotic immune system arises from the clustered regularly interspaced short palindromic repeats (CRISPRs) found in prokaryotic genomes, which harbor short invader-derived sequences, and the CRISPR-associated (Cas)(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)
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)
In this paper, we present two new algorithms for cell image segmentation. First, we demonstrate that pixel classification-based color image segmentation in color space is equivalent to performing segmentation on grayscale image through thresholding. Based on this result, we develop a supervised learning-based two-step procedure for color cell image(More)
Automatic simdization for multimedia extensions faces several new challenges that are not present in traditional vectorization. Some of the new issues are due to the more restrictive SIMD architectures designed for multimedia extensions. Among them are alignment constraints, lack of memory gather and scatter support, and the short and fixed-length nature of(More)