Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

A quantum chemical study of the mechanism of action of Vitamin K epoxide reductase (VKOR) II. Transition states

A reaction path including transition states is generated for the Silverman mechanism [R.B. Silverman, Chemical model studies for the mechanism of Vitamin K epoxide reductase, J. Am. Chem. Soc. 103 (1981) 5939-5941] of action for Vitamin K epoxide reductase (VKOR) using quantum mechanical methods (B3LYP/6-311G**). VKOR, an essential enzyme in mammalian systems, acts to convert Vitamin K epoxide, formed by Vitamin K carboxylase, to its (initial) quinone form for cellular reuse. This study elaborates on a prior work that focused on the thermodynamics of VKOR [D.W. Deerfield II, C.H. Davis, T. Wymore, D.W. Stafford, L.G. Pedersen, Int. J. Quant. Chem. 106 (2006) 2944-2952]. The geometries of proposed model intermediates and transition states in the mechanism are energy optimized. We find that once a key disulfide bond is broken, the reaction proceeds largely downhill. An important step in the conversion of the epoxide back to the quinone form involves initial protonation of the epoxide oxygen. We find that the source of this proton is likely a free mercapto group rather than a water molecule. The results are consistent with the current view that the widely used drug Warfarin likely acts by blocking binding of Vitamin K at the VKOR active site and thereby effectively blocking the initiating step. These results will be useful for designing more complete QM/MM studies of the enzymatic pathway once three-dimensional structural data is determined and available for VKOR.     

Towards understanding the mechanism of action of the multidrug resistance-linked half-ABC transporter ABCG2: a molecular modeling study

The ATP-binding cassette protein ABCG2 is a member of a broad family of ABC transporters with potential clinical importance as a mediator of multidrug resistance. We carried out a homology and knowledge-based, and mutationally improved molecular modeling study to establish a much needed structural framework for the protein, which could serve as guidance for further genetic, biochemical, and structural analyses. Based on homology with known structures of both full-length and nucleotide-binding domains (NBD) of ABC transporters and structural knowledge of integral membrane proteins, an initial model of ABCG2 was established. Subsequent refinement to conform to the lipophilic index distributions in the transmembrane domain (TMD) and to the results of site-directed mutagenesis experiments led to an improved model. The complete ABCG2 model consists of two identical subunits facing each other in a closed conformation. The dimeric interface in the nucleotide-binding domain (NBD) involves a characteristic nucleotide sandwich and the interface in the TMD consists of the TM helices 1–3 of one subunit and the helices 5 and 6 of the other. The interface between the NBD and the TMD is bridged by the conserved structural motif between TM2 and TM3, the intracellular domain 1 (ICD1), and the terminal β-strand (S6) of the central β-sheet in the NBD. The apparent flexibility of the ICD1 may play a role in transmitting conformational changes from the NBD to the TMD or from the TMD to the NBD.     

CST: constructive solid trimming for rendering BReps and CSG

Abstract-To eliminate the need to evaluate the intersection curves in explicit representations of surface cutouts or of trimmed faces in BReps of CSG solids, we advocate using Constructive Solid Trimming (CST). A CST face is the intersection of a surface with a Blist representation of a trimming CSG volume. We propose a new GPU-based CSG rendering algorithm that trims the boundary of each primitive using a Blist of its active zone. This approach is faster than the previously reported Blister approach, eliminates occasional speckles of wrongly colored pixels, and provides additional capabilities: painting on surfaces, rendering semitransparent CSG models, and highlighting selected features in the BReps of CSG models.     

Curve-skeleton properties, applications, and algorithms

Curve-skeletons are thinned 1D representations of 3D objects useful for many visualization tasks including virtual navigation, reduced-model formulation, visualization improvement, animation, etc. There are many algorithms in the literature describing extraction methodologies for different applications; however, it is unclear how general and robust they are. In this paper, we provide an overview of many curve-skeleton applications and compile a set of desired properties of such representations. We also give a taxonomy of methods and analyze the advantages and drawbacks of each class of algorithms     

Time dependent processing in a parallel pipeline architecture

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.     

Conjoint analysis to measure the perceived quality in volume rendering

Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.     

A flexible multi-volume shader framework for arbitrarily intersecting multi-resolution datasets

We present a powerful framework for 3D-texture-based rendering of multiple arbitrarily intersecting volumetric datasets. Each volume is represented by a multi-resolution octree-based structure and we use out-of-core techniques to support extremely large volumes. Users define a set of convex polyhedral volume lenses, which may be associated with one or more volumetric datasets. The volumes or the lenses can be interactively moved around while the region inside each lens is rendered using interactively defined multi-volume shaders. Our rendering pipeline splits each lens into multiple convex regions such that each region is homogenous and contains a fixed number of volumes. Each such region is further split by the brick boundaries of the associated octree representations. The resulting puzzle of lens fragments is sorted in front-to-back or back-to-front order using a combination of a view-dependent octree traversal and a GPU-based depth peeling technique. Our current implementation uses slice-based volume rendering and allows interactive roaming through multiple intersecting multi-gigabyte volumes.     

Querying and creating visualizations by analogy

While there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system.     

Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.