Duality between Multidimensional Convolutional Codes and Systems

Multidimensional convolutional codes generalize (one dimensional) convolutional codes and they correspond under a natural duality to multidimensional systems widely studied in the systems literature.Comment: 16 pages LaTe

Outsourcing Evaluation Approach For An Information Systems Project

This paper presents a detailed methodology for computing the technical and managerial scores of a Worth Index used in the evaluation of proposals during an Information Systems outsourcing project

Different modes of hypertrophy in skeletal muscle fibers

Skeletal muscles display a remarkable diversity in their arrangement of fibers into fascicles and in their patterns of innervation, depending on functional requirements and species differences. Most human muscle fascicles, despite their great length, consist of fibers that extend continuously from one tendon to the other with a single nerve endplate band. Other mammalian muscles have multiple endplate bands and fibers that do not insert into both tendons but terminate intrafascicularly. We investigated whether these alternate structural features may dictate different modes of cell hypertrophy in two mouse gracilis muscles, in response to expression of a muscle-specific insulin-like growth factor (IGF)-1 transgene (mIGF-1) or to chronic exercise. Both hypertrophic stimuli independently activated GATA-2 expression and increased muscle cross-sectional area in both muscle types, with additive effects in exercising myosin light chain/mIGF transgenic mice, but without increasing fiber number. In singly innervated gracilis posterior muscle, hypertrophy was characterized by a greater average diameter of individual fibers, and centralized nuclei. In contrast, hypertrophic gracilis anterior muscle, which is multiply innervated, contained longer muscle fibers, with no increase in average diameter, or in centralized nuclei. Different modes of muscle hypertrophy in domestic and laboratory animals have important implications for building appropriate models of human neuromuscular disease

Variational Level-Set Detection of Local Isosurfaces from Unstructured Point-based Volume Data

A standard approach for visualizing scalar volume data is the extraction of isosurfaces. The most efficient methods for surface extraction operate on regular grids. When data is given on unstructured point-based samples, regularization can be applied but may introduce interpolation errors. We propose a method for smooth isosurface visualization that operates directly on unstructured point-based volume data avoiding any resampling. We derive a variational formulation for smooth local isosurface extraction using an implicit surface representation in form of a level-set approach, deploying Moving Least Squares (MLS) approximation, and operating on a kd-tree. The locality of our approach has two aspects: first, our algorithm extracts only those components of the isosurface, which intersect a subdomain of interest; second, the action of the main term in the governing equation is concentrated near the current isosurface position. Both aspects reduce the computation times per level-set iteration. As for most level-set methods a reinitialization procedure is needed, but we also consider a modified algorithm where this step is eliminated. The final isosurface is extracted in form of a point cloud representation. We present a novel point completion scheme that allows us to handle highly adaptive point sample distributions. Subsequently, splat-based or mere (shaded) point rendering is applied. We apply our method to several synthetic and real-world data sets to demonstrate its validity and efficiency