a computational exploration of exaggeration

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Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Shape-from-intrinsic operator

Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations. In this paper, we formulate the problem of shape-from-operator (SfO), recovering an embedding of a mesh from intrinsic differential operators defined on the mesh. Particularly interesting instances of our SfO problem include synthesis of shape analogies, shape-from-Laplacian reconstruction, and shape exaggeration. Numerically, we approach the SfO problem by splitting it into two optimization sub-problems that are applied in an alternating scheme: metric-from-operator (reconstruction of the discrete metric from the intrinsic operator) and embedding-from-metric (finding a shape embedding that would realize a given metric, a setting of the multidimensional scaling problem)

Two-Stage Eagle Strategy with Differential Evolution

Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications

Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

The integration of 3D modeling and simulation to determine the energy potential of low-temperature geothermal systems in the Pisa (Italy) sedimentary plain

Shallow, low-temperature geothermal resources can significantly reduce the environmental impact of heating and cooling. Based on a replicable standard workflow for three-dimensional (3D) geothermal modeling, an approach to the assessment of geothermal energy potential is proposed and applied to the young sedimentary basin of Pisa (north Tuscany, Italy), starting from the development of a geothermal geodatabase, with collated geological, stratigraphic, hydrogeological, geophysical and thermal data. The contents of the spatial database are integrated and processed using software for geological and geothermal modeling. The models are calibrated using borehole data. Model outputs are visualized as three-dimensional reconstructions of the subsoil units, their volumes and depths, the hydrogeological framework, and the distribution of subsoil temperatures and geothermal properties. The resulting deep knowledge of subsoil geology would facilitate the deployment of geothermal heat pump technology, site selection for well doublets (for open-loop systems), or vertical heat exchangers (for closed-loop systems). The reconstructed geological-hydrogeological models and the geothermal numerical simulations performed help to define the limits of sustainable utilization of an area's geothermal potential