• Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns

  Williams, Katy; Bigelow, Alex; Isaacs, Kate; Univ Arizona (IEEE COMPUTER SOC, 2020-01-01)

  Common pitfalls in visualization projects include lack of data availability and the domain users' needs and focus changing too rapidly for the design process to complete. While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a "chicken and egg" problem of having the data and tools to analyze it. We found this to be the case in the domain of task parallel computing where such data and tooling is an open area of research. Despite these hurdles, we conducted a design study. Through a tightly-coupled iterative design process, we built Atria, a multi-view execution graph visualization to support performance analysis. Atria simplifies the initial representation of the execution graph by aggregating nodes as related to their line of code. We deployed Atria on multiple platforms, some requiring design alteration. We describe how we adapted the design study methodology to the "moving target" of both the data and the domain experts' concerns and how this movement kept both the visualization and programming project healthy. We reflect on our process and discuss what factors allow the project to be successful in the presence of changing data and user needs.
• Imagination, Brokers, and Boundary Objects: Interrupting the Mentor–Preservice Teacher Hierarchy When Negotiating Meanings

  Canipe, Martha M.; Gunckel, Kristin L.; Univ Arizona, Sci Edu (SAGE PUBLICATIONS INC, 2020-01)

  The mentor-preservice teacher hierarchy, that privileges mentor teacher talk and experience, often dominates mentor-preservice conversations. To realize the full potential of teacher education approaches designed to engage preservice and mentor teachers together in shared learning and teaching tasks, attention is needed to better understand the dynamics and implications of mentor-preservice teacher interactions. We analyzed how and when preservice and mentor teachers introduced ideas to group conversations and whose ideas were taken up by the group during a co-learning task. We found that mentor teachers tended to dominate group sense-making. However, preservice teacher use of imagination, the actions of teacher educators as brokers, and the use of boundary objects temporarily interrupted the dominant hierarchy. We conjecture that these moments raised preservice teacher status within the group so that mentor teachers took up preservice teachers' ideas. Implications for promoting more equitable preservice teacher participation in sense-making with mentor teachers are discussed.
• The Topology ToolKit

  Tierny, Julien; Favelier, Guillaume; Levine, Joshua A.; Gueunet, Charles; Michaux, Michael; Univ Arizona (IEEE COMPUTER SOC, 2018-01)

  This system paper presents the Topology ToolKit (TTK), a software platform designed for the topological analysis of scalar data in scientific visualization. While topological data analysis has gained in popularity over the last two decades, it has not yet been widely adopted as a standard data analysis tool for end users or developers. TTK aims at addressing this problem by providing a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependency-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website [108].
• The Effect of Data Transformations on Scalar Field Topological Analysis of High-Order FEM Solutions

  Jallepalli, Ashok; Levine, Joshua A; Kirby, Robert M; Univ Arizona, Dept Comp Sci (IEEE COMPUTER SOC, 2020-01-01)

  High-order finite element methods (HO-FEM) are gaining popularity in the simulation community due to their success in solving complex flow dynamics. There is an increasing need to analyze the data produced as output by these simulations. Simultaneously, topological analysis tools are emerging as powerful methods for investigating simulation data. However, most of the current approaches to topological analysis have had limited application to HO-FEM simulation data for two reasons. First, the current topological tools are designed for linear data (polynomial degree one), but the polynomial degree of the data output by these simulations is typically higher (routinely up to polynomial degree six). Second, the simulation data and derived quantities of the simulation data have discontinuities at element boundaries, and these discontinuities do not match the input requirements for the topological tools. One solution to both issues is to transform the high-order data to achieve low-order, continuous inputs for topological analysis. Nevertheless, there has been little work evaluating the possible transformation choices and their downstream effect on the topological analysis. We perform an empirical study to evaluate two commonly used data transformation methodologies along with the recently introduced L-SIAC filter for processing high-order simulation data. Our results show diverse behaviors are possible. We offer some guidance about how best to consider a pipeline of topological analysis of HO-FEM simulations with the currently available implementations of topological analysis.
• Modelling kindness

  Dufwenberg, Martin; Kirchsteiger, Georg; Univ Arizona (ELSEVIER, 2019-11)

  Kindness is an important concept in reciprocity theory, and may matter also for other forms of motivation. We critically compare definitions proposed by Rabin (1993) and by Dufwenberg and Kirchsteiger (2004). Several reasons to prefer the latter definition are highlighted, but also a flaw (discovered by Isoni and Sugden 2018) which we show can be eliminated using a slightly revised definition. (C) 2018 Elsevier B.V. All rights reserved.

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