Adaptive isogeometric analysis by local h-refinement with T-splines

Isogeometric analysis based on non-uniform rational B-splines (NURBS) as basis functions preserves the exact geometry but suffers from the drawback of a rectangular grid of control points in the parameter space, which renders a purely local refinement impossible. This paper demonstrates how this difficulty can be overcome by using T-splines instead. T-splines allow the introduction of so-called T-junctions, which are related to hanging nodes in the standard FEM. Obeying a few straightforward rules, rectangular patches in the parameter space of the T-splines can be subdivided and thus a local refinement becomes feasible while still preserving the exact geometry. Furthermore, it is shown how state-of-the-art a posteriori error estimation techniques can be combined with refinement by T-splines. Numerical examples underline the potential of isogeometric analysis with T-splines and give hints for further developments.     

IRIS: illustrative rendering for integral surfaces

Integral surfaces are ideal tools to illustrate vector fields and fluid flow structures. However, these surfaces can be visually complex and exhibit difficult geometric properties, owing to strong stretching, shearing and folding of the flow from which they are derived. Many techniques for non-photorealistic rendering have been presented previously. It is, however, unclear how these techniques can be applied to integral surfaces. In this paper, we examine how transparency and texturing techniques can be used with integral surfaces to convey both shape and directional information. We present a rendering pipeline that combines these techniques aimed at faithfully and accurately representing integral surfaces while improving visualization insight. The presented pipeline is implemented directly on the GPU, providing real-time interaction for all rendering modes, and does not require expensive preprocessing of integral surfaces after computation.     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

Pattern classification with mixtures of weighted least-squares support vector machine experts

Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the kernel artifice of nonlinearly mapping input data into high-dimensional feature spaces toward the automatic construction of better discriminating linear decision boundaries. Among several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Time‐Series Plots Integrated in Parallel‐Coordinates Displays

We present a natural extension of two‐dimensional parallel‐coordinates plots for revealing relationships in time‐dependent multi‐attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel‐coordinates display. A vertical slice through one of the axes of the parallel‐coordinates display would show a time‐series plot. For a focus‐and‐context Integration of both views, we embed time‐series plots between two adjacent axes of the parallel‐coordinates plot. Both time‐series plots are drawn using a pseudo three‐dimensional perspective with a single vanishing point. An independent parallel‐coordinates panel that connects the two perspectively displayed time‐series plots can move forward and backward in time to reveal changes in the relationship between the time‐dependent attributes. The visualization of time‐series plots in the context of the parallel‐coordinates plot facilitates the exploration of time‐related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.     

Crusta: A new virtual globe for real-time visualization of sub-meter digital topography at planetary scales

Virtual globes are becoming ubiquitous in the visualization of planetary bodies and Earth specifically. While many of the current virtual globes have proven to be quite useful for remote geologic investigation, they were never designed for the purpose of serving as virtual geologic instruments. Their shortcomings have become more obvious as earth scientists struggle to visualize recently released digital elevation models of very high spatial resolution (0.5-1 m²/sample) and extent (>2000 km²). We developed Crusta as an alternative virtual globe that allows users to easily visualize their custom imagery and more importantly their custom topography. Crusta represents the globe as a 30-sided polyhedron to avoid distortion of the display, in particular the singularities at the poles characteristic of other projections. This polyhedron defines 30 “base patches,” each being a four-sided region that can be subdivided to an arbitrarily fine grid on the surface of the globe to accommodate input data of arbitrary resolution, from global (BlueMarble) to local (tripod LiDAR), all in the same visualization. We designed Crusta to be dynamic with the shading of the terrain surface computed on-the-fly when a user manipulates his point-of-view. In a similarly interactive fashion the globe's surface can be exaggerated vertically. The combination of the two effects greatly improves the perception of shape. A convenient pre-processing tool based on the GDAL library facilitates importing a number of data formats into the Crusta-specific multi-scale hierarchies that enable interactive visualization on a range of platforms from laptops to immersive geowalls and caves. The main scientific user community for Crusta is earth scientists, and their needs have been driving the development.     

Verifying Temporal Properties of Reactive Systems: A STeP Tutorial

We review a number of formal verification techniques supported by STeP, the Stanford Temporal Prover, describing how the tool can be used to verify properties of several versions of the Bakery Mutual exclusion algorithm for mutual exclusion. We verify the classic two-process algorithm and simple variants, as well as an atomic parameterized version. The methods used include deductive verification rules, verification diagrams, automatic invariant generation, and finite-state model checking and abstraction.     

Top-down proteomic analysis by MALDI-TOF profiling: Concentration-independent biomarkers

Although numerous protein biomarkers have been correlated with advanced disease states, no new clinical assays have been developed. Goals often anticipate disease-specific protein changes that exceed values among healthy individuals, a property common to acute phase reactants. This review considers somewhat different approaches. It focuses on intact protein isoform ratios that present a biomarker without change in the total concentration of the protein. These will seldom be detected by peptide level analysis or by most antibody-based assays. For example, application of an inexpensive method to large sample groups resulted in observation of several polymorphisms, including the first structural polymorphism of apolipoprotein C1. Isoform distribution of this protein was altered and was eventually linked to increased obesity. Numerous other protein isoforms included C- and N-terminal proteolysis, changes of glycoisoform ratios and certain types of sulfhydryl oxidation. While many of these gave excellent statistical correlation with advanced disease, clinical utility was not apparent. More important may be that protein isoform ratios were very stable in each individual. Diagnosis by longitudinal analysis of the same individual might increase sensitivity of protein biomarkers by 20-fold or more. Protein changes that exceed the range of values found among healthy individuals may be uncommon.