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

Inferring Quality in Point Cloud-based 3D Printed Objects using Topological Data Analysis

Assessing the quality of 3D printed models before they are printed remains a challenging problem, particularly when considering point cloud-based models. This paper introduces an approach to quality assessment, which uses techniques from the field of Topological Data Analysis (TDA) to compute a topological abstraction of the eventual printed model. Two main tools of TDA, Mapper and persistent homology, are used to analyze both the printed space and empty space created by the model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identifies potential anomalies that may appear in the final product.

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Handling Anomalies in Object Slicing for 3-D Printing

This paper introduces and extension to our previous papers to handle anomalies in the point based object slicing method. The anomalies handled are point, line and plane touch cases as well as overlaps. These anomalies can cause major problems in any intersection procedure, yet, they are seldom discussed, let alone handled. It turns out that the point based approach is capable of handling these special cases with minor extensions.

Handling Anomalies in Object Slicing for 3-D Printing
W Oropallo, L Piegl, P Rosen, K Rajab
Computer-Aided Design and Applications, 2019

Computational Geometry: Young Researchers Forum (at SoCG)

One of theSoCG satellite event will be the ”Computational Geometry: Young Researchers Forum” (CG:YRF), which is aimed at current and recent students. The active involvement by students and recent graduates in research, discussions, and social events has been a longstanding tradition in the CG community. Participation in a top-level event such as SoCG can be educating, motivating, and useful for networking, both with other students and with more senior scientists.

The YRF presents young researchers (*defined as not having received a formal doctorate before 2017*) an opportunity to present their work (in progress as well as finished results) to the CG community in a friendly, open setting. Just like in the main event, presentations will be given in the form of talks. A pre-screening (but no formal review process) will ensure appropriate quality control.