280,627 research outputs found

    On the Optimization of Visualizations of Complex Phenomena

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    The problem of perceptually optimizing complex visualizations is a difficult one, involving perceptual as well as aesthetic issues. In our experience, controlled experiments are quite limited in their ability to uncover interrelationships among visualization parameters, and thus may not be the most useful way to develop rules-of-thumb or theory to guide the production of high-quality visualizations. In this paper, we propose a new experimental approach to optimizing visualization quality that integrates some of the strong points of controlled experiments with methods more suited to investigating complex highly-coupled phenomena. We use human-in-the-loop experiments to search through visualization parameter space, generating large databases of rated visualization solutions. This is followed by data mining to extract results such as exemplar visualizations, guidelines for producing visualizations, and hypotheses about strategies leading to strong visualizations. The approach can easily address both perceptual and aesthetic concerns, and can handle complex parameter interactions. We suggest a genetic algorithm as a valuable way of guiding the human-in-the-loop search through visualization parameter space. We describe our methods for using clustering, histogramming, principal component analysis, and neural networks for data mining. The experimental approach is illustrated with a study of the problem of optimal texturing for viewing layered surfaces so that both surfaces are maximally observable

    Boundary Condition Adjustment Methods And Systems

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    Methods and systems for reactor lattice depletion are disclosed. One exemplary method, among others, comprises the steps of defining a reactor eigenvalue, the reactor eigenvalue being a specified ratio of actual neutron production to loss in the reactor; producing a lattice eigenvalue, the lattice eigenvalue being an estimated ratio of neutron production to loss in the lattice; and adjusting a boundary condition of the lattice to cause convergence of the lattice eigenvalue and the reactor eigenvalue in order to produce at least one physics parameter.Georgia Tech Research Corporatio

    Search-based amorphous slicing

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    Amorphous slicing is an automated source code extraction technique with applications in many areas of software engineering, including comprehension, reuse, testing and reverse engineering. Algorithms for syntax-preserving slicing are well established, but amorphous slicing is harder because it requires arbitrary transformation; finding good general purpose amorphous slicing algorithms therefore remains as hard as general program transformation. In this paper we show how amorphous slices can be computed using search techniques. The paper presents results from a set of experiments designed to explore the application of genetic algorithms, hill climbing, random search and systematic search to a set of six subject programs. As a benchmark, the results are compared to those from an existing analytical algorithm for amorphous slicing, which was written specifically to perform well with the sorts of program under consideration. The results, while tentative at this stage, do give grounds for optimism. The search techniques proved able to reduce the size of the programs under consideration in all cases, sometimes equaling the performance of the specifically-tailored analytic algorithm. In one case, the search techniques performed better, highlighting a fault in the existing algorith

    Deformable Shape Completion with Graph Convolutional Autoencoders

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    The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learning-based method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality.Comment: CVPR 201
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