Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes

The current generation of radio and millimeter telescopes, particularly the Atacama Large Millimeter Array (ALMA), offers enormous advances in observing capabilities. While these advances represent an unprecedented opportunity to facilitate scientific understanding, the increased complexity in the spatial and spectral structure of these ALMA data cubes lead to challenges in their interpretation. In this paper, we perform a feasibility study for applying topological data analysis and visualization techniques never before tested by the ALMA community. Through techniques based on contour trees, we seek to improve upon existing analysis and visualization workflows of ALMA data cubes, in terms of accuracy and speed in feature extraction. We review our application development process in building effective analysis and visualization capabilities for the astrophysicists. We also summarize effective design practices by identifying domain-specific needs of simplicity, integrability, and reproducibility, in order to best target and service the large astrophysics community.

Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes
P Rosen, A Seth, B Mills, A Ginsburg, J Kamenetzky, J Kern, CR Johnson, B Wang
Topological Methods in Data Analysis and Visualization (TopoInVis)

Designing Intelligent Review Forms for Peer Assessment: A Data-Driven Approach

This evidence-based practice paper employs a data-driven, explainable, and scalable approach to the development and application of an online peer review system in computer science and engineering courses. Crowd-sourced grading through peer review is an effective evaluation methodology that 1) allows the use of meaningful assignments in large or online classes (e.g. assignments other than true/false, multiple choice, or short answer), 2) fosters learning and critical thinking in a student evaluating another’s work, and 3) provides a defendable and non-biased score through the wisdom of the crowd. Although peer review is widely utilized, to the authors’ best knowledge, the form and associated grading process have never been subjected to data-driven analysis and design. We present a novel, iterative approach by first gathering the most appropriate review form questions through intelligent data mining of past student reviews. During this process, key words and ideas are gathered for positive and negative sentiment dictionaries, a flag word dictionary, and a negate word dictionary. Next, we revise our grading algorithm using simulations and perturbation to determine robustness (measured by standard deviation within a section). Using the dictionaries, we leverage sentiment gathered from review comments as a quality assurance mechanism to generate a crowd comment “grade”. This grade supplements the weighted average of other review form sections. The result of this semi-automated, innovative process is a peer assessment package (intelligently-designed review form and robust grading algorithm leveraging crowd sentiment) based on actual student work that can be used by an educator to confidently assign and grade meaningful open-ended assignments in any size class.

Designing Intelligent Review Forms for Peer Assessment: A Data-Driven Approach
Z Beasley, L Piegl, P Rosen
ASEE Annual Conference & Exposition, 2019

Mesh Learning Using Persistent Homology on the Laplacian Eigenfunctions

We use persistent homology along with the eigenfunctions of the Laplacian to study similarity amongst triangulated 2-manifolds. Our method relies on studying the lower-star filtration induced by the eigenfunctions of the Laplacian. This gives us a shape descriptor that inherits the rich information encoded in the eigenfunctions of the Laplacian. Moreover, the similarity between these descriptors can be easily computed using tools that are readily available in Topological Data Analysis. We provide experiments to illustrate the effectiveness of the proposed method.

Mesh Learning Using Persistent Homology on the Laplacian Eigenfunctions
Y Zhang, H Liu, P Rosen, M Hajij
International Geometry Summit (poster), 2019