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The lasso penalizes a least squares regression by the sum of the absolute values (L 1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0). We propose the 'fused lasso', a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso(More)
We perform an algebraic analysis of a generalization of the augmented Lagrangian method for solution of saddle point linear systems. It is shown that in cases where the (1,1) block is singular, specifically semidefinite, a low-rank perturbation that minimizes the condition number of the perturbed matrix while maintaining sparsity is an effective approach.(More)
Numerical tests are used to validate a practical estimate for the optimal backward errors of linear least squares problems. This solves a thirty-year-old problem suggested by Stewart and Wilkinson. " A great deal of thought, both by myself and by J. H. Wilkinson, has not solved this problem, and I therefore pass it on to you: find easily computable(More)
For efficient prevention of viral infections and cross protection, simultaneous targeting of multiple viral epitopes is a powerful strategy. Llama heavy chain antibody fragments (VHH) against the trimeric envelope proteins of Respiratory Syncytial Virus (Fusion protein), Rabies virus (Glycoprotein) and H5N1 Influenza (Hemagglutinin 5) were selected from(More)
—The ability of the aquatic plant Eurasian Watermilfoil (Myriophyllum spicatum) to transform 2,4,6-trinitrotoluene (TNT) was investigated in a series of batch assays. The TNT was added to plant cultures in single and multiple consecutive additions, at various initial concentrations, to determine its transformation kinetics, identify products formed,(More)