University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Gene. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.

Abstract: When the number of projections does not satisfy the Shannon/Nyquist sampling requirement, streaking artifacts are inevitable in x-ray computed tomography (CT) images reconstructed using filtered backprojection algorithms. In this letter, the spatial-temporal correlations in dynamic CT imaging have been exploited to sparsify dynamic CT image sequences and the newly proposed compressed sensing (CS) reconstruction method is applied to reconstruct the target image sequences. A prior image reconstructed from the union of interleaved dynamical data sets is utilized to constrain the CS image reconstruction for the individual time frames. This method is referred to as prior image constrained compressed sensing (PICCS). In vivo experimental animal studies were conducted to validate the PICCS algorithm, and the results indicate that PICCS enables accurate reconstruction of dynamic CT images using about 20 view angles, which corresponds to an undersampling factor of 32. This undersampling factor implies a potential radiation dose reduction by a factor of 32 in myocardial CT perfusion imaging.

Precise interprocedural dataflow analysis via graph reachability

Abstract: The paper shows how a large class of interprocedural dataflow-analysis problems can be solved precisely in polynomial time by transforming them into a special kind of graph-reachability problem. The only restrictions are that the set of dataflow facts must be a finite set, and that the dataflow functions must distribute over the confluence operator (either union or intersection). This class of probable problems includes—but is not limited to—the classical separable problems (also known as “gen/kill” or “bit-vector” problems)—e.g., reaching definitions, available expressions, and live variables. In addition, the class of problems that our techniques handle includes many non-separable problems, including truly-live variables, copy constant propagation, and possibly-uninitialized variables.Results are reported from a preliminary experimental study of C programs (for the problem of finding possibly-uninitialized variables).

NBO 6.0: natural bond orbital analysis program.

Abstract: We describe principal features of the newly released version, NBO 6.0, of the natural bond orbital analysis program, that provides novel “link-free” interactivity with host electronic structure systems, improved search algorithms and labeling conventions for a broader range of chemical species, and new analysis options that significantly extend the range of chemical applications. We sketch the motivation and implementation of program changes and describe newer analysis options with illustrative applications. © 2013 Wiley Periodicals, Inc.

Sparse reconstruction by separable approximation

Abstract: Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to minimize an objective function that includes a quadratic (pound 2) error term added to a sparsity-inducing (usually pound 1) regularizer. We present an algorithmic framework for the more general problem of minimizing the sum of a smooth convex function and a nonsmooth, possibly nonconvex, sparsity-inducing function. We propose iterative methods in which each step is an optimization subproblem involving a separable quadratic term (diagonal Hessian) plus the original sparsity-inducing term. Our approach is suitable for cases in which this subproblem can be solved much more rapidly than the original problem. In addition to solving the standard pound 2 - pound 1 case, our approach handles other problems, e.g., pound p regularizers with p ne 1, or group-separable (GS) regularizers. Experiments with CS problems show that our approach provides state-of-the-art speed for the standard pound 2 - pound 1 problem, and is also efficient on problems with GS regularizers.

The Medicago genome provides insight into the evolution of rhizobial symbioses

Abstract: Legumes (Fabaceae or Leguminosae) are unique among cultivated plants for their ability to carry out endosymbiotic nitrogen fixation with rhizobial bacteria, a process that takes place in a specialized structure known as the nodule. Legumes belong to one of the two main groups of eurosids, the Fabidae, which includes most species capable of endosymbiotic nitrogen fixation. Legumes comprise several evolutionary lineages derived from a common ancestor 60 million years ago (Myr ago). Papilionoids are the largest clade, dating nearly to the origin of legumes and containing most cultivated species. Medicago truncatula is a long-established model for the study of legume biology. Here we describe the draft sequence of the M. truncatula euchromatin based on a recently completed BAC assembly supplemented with Illumina shotgun sequence, together capturing ∼94% of all M. truncatula genes. A whole-genome duplication (WGD) approximately 58 Myr ago had a major role in shaping the M. truncatula genome and thereby contributed to the evolution of endosymbiotic nitrogen fixation. Subsequent to the WGD, the M. truncatula genome experienced higher levels of rearrangement than two other sequenced legumes, Glycine max and Lotus japonicus. M. truncatula is a close relative of alfalfa (Medicago sativa), a widely cultivated crop with limited genomics tools and complex autotetraploid genetics. As such, the M. truncatula genome sequence provides significant opportunities to expand alfalfa's genomic toolbox.