NSF III-2216227: November 1, 2022 to October 31, 2025

PI and Point of Contact

Alon Friedman
Associate Professor
University of South Florida
https://alonfriedman.net/
alonfriedman AT usf DOT edu

Co-PIs

Paul Rosen
Associate Professor
University of Utah
http://www.cspaul.com
prosen AT sci DOT utah DOT edu

Kevin Hawley
Master Instructor
University of South Florida
http://masscom.usf.edu/faculty/khawley/
kevinhawley AT usf DOT edu

Overview

This project aims to serve the national interest by increasing the quality of peer reviews given by students. Peer reviews, in which students have the opportunity to analyze and evaluate projects made by their classroom peers, are a widely acknowledged pedagogical method for engaging students and have become a standard practice in undergraduate education. Peer review is most often used in classes with large number of students to provide timely feedback on student assignments. However, peer review has benefits far beyond scalability. Peer review gathers diverse feedback, raises students’ comfort level with having their work evaluated in a professional setting, and most importantly, the action of giving a peer review is often more valuable than receiving a peer review. The software platforms in current use that support peer review have seen limited innovation in recent decades, and potential improvements to enhance student outcomes have not been thoroughly evaluated. This project plans to develop and study an innovative peer review system that uses behavioral nudges, a method of subtlety reinforcing positive habits, to improve the evaluation skills of students and the quality of the feedback they provide in peer reviews.

This project proposes to 1) create a software system supporting peer review that includes behavioral nudges for guiding the peer review process to hone students’ evaluation and critical analysis skills and 2) study how the use of behavioral nudges improves student engagement. The approach will be evaluated using four different visualization courses that serve approximately 800 students per year. Two groups of students will be studied. The control group will utilize peer review with additional feedback modalities but no nudges, while the test group will use peer review with the same feedback modalities but also include nudges. Common statistical tests such as, ANOVA, t-tests, and correlations, will be used to validate the results. The resulting system could provide a significant and measurable improvement in outcomes in courses that utilize peer review across different STEM disciplines. The resulting technologies will be disseminated as open source to enable widespread adoption. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

Manuscripts

Theses

Presentations

Software

Personnel

Former Personnel

Collaborators

Acknowledgments

This material is based upon work supported or partially supported by the National Science Foundation under Grant No. 2216227, project titled “Using Behavioral Nudges in Peer Review to Improve Critical Analysis in STEM Courses”. 

Any opinions, findings, and conclusions or recommendations expressed in this project are those of author(s) and do not necessarily reflect the views of the National Science Foundation. 

NSF III-2316496: February 10, 2023 to May 31, 2026
NSF III-1845204: September 1, 2019 to February 9, 2023
NSF III-2027787: REU Supplement
NSF III-2129857: REU Supplement

PI and Point of Contact

Paul Rosen
Associate Professor
Kahlert School of Computing
Scientific Computing and Imaging Institute
University of Utah
http://www.cspaul.com
paul DOT rosen AT utah DOT edu

Overview

In science, ensembles are used to model uncertainties that occur in data from a variety of sources, including errors in measurements, inaccuracies in modeling, and a lack of adequate sampling. Understanding these errors is critical to improving human understanding of phenomena in many areas of science, from urban planning to astrophysics to medicine to weather forecasting, etc. This project investigates new Topological Data Analysis and visualization methods to analyze uncertain data. This will enable scientists to better understand phenomena within their domain by developing new insights and making discoveries more quickly. The techniques will be tested in collaboration with a biomedical engineering research team helping to develop new life-saving treatments for heart attacks and a research team helping to develop technologies that support a safe, clean, and reliable national energy grid. Furthermore, this project will study and advocate for integrating better teaching methodologies, such as peer review, into computer science curricula. The results will be integrated into visualization and computational geometry courses through course materials, such as design mini-challenges, and shared with the educational community through outreach activities, such as pedagogy-themed panels and workshops.

To accomplish the goals of the project, the tools of Topological Data Analysis provide a strong theoretical basis for robustly extracting features from ensembles and designing visualizations for performing important uncertainty analysis tasks, including identifying and ranking similarities, identifying and ranking variations, and correlating topological features. This project addresses two important scientific questions: how to effectively use topology to extract features from ensembles; and how to design visualizations for domain experts that efficiently communicate the features. To extract features from an ensemble, the project will investigate new methods of robustly comparing and contrasting the topology of multiple ensemble realizations. Then, in collaboration with domain scientists, it will design new visualization methods for efficiently and effectively comparing and exploring the features and variations within ensembles. The project web site provides additional information and will include access to developed tools, data sets, and educational content.

Manuscripts

Magic: Marching Cubes Isosurface Uncertainty Visualization For Gaussian Uncertain Data With Spatial Correlation Tushar M. Athawale, Kenneth Moreland, David Pugmire, Chris R. Johnson, Paul Rosen, Matthew Norman, Antigoni Georgiadou, and Alireza Entezari IEEE Transactions on Computer Graphics and Visualization, 2026

Beyond One-Size-Fits-All: User Strategies For Simplification Technique And Level Selection In Responsive Line Charts Rifat Ara Proma, and Paul Rosen EuroVis Short Papers, 2026

Designing For Engagement: A Comparison Of Canvas And A Visual Peer Review Dashboard Alon Friedman, Ly Dinh, Md Dilshadur Rahman, and Paul Rosen ACM Transactions on Computing Education, 2026

Visual Stenography: Feature Recreation And Preservation In Sketches Of Line Charts Rifat Ara Proma, Michael Correll, Ghulam Jilani Quadri, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics, 2026

Evaluating Line Chart Strategies For Mitigating Density Of Temporal Data: The Impact On Trend, Trust, And Prediction Rifat Ara Proma, Ghulam Jilani Quadri, and Paul Rosen Lecture Notes in Computer Science, 2026

Gasp: A Gradient-Aware Shortest Path Algorithm For Boundary-Confined Visualization Of 2-Manifold Reeb Graphs Sefat Rahman, Tushar M. Athawale, and Paul Rosen Topological Data Analysis and Visualization (TopoInVis), 2025

Uncertainty Visualization Of Critical Points Of 2D Scalar Fields For Parametric And Nonparametric Probabilistic Models Tushar M. Athawale, Zhe Wang, David Pugmire, Kenneth Moreland, Qian Gong, Scott Klasky, Chris R. Johnson, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 2025

Enhancing Student Feedback Using Predictive Models In Visual Literacy Courses Alon Friedman, Kevin Hawley, Paul Rosen, and MD Dilshadur Rahman IEEE Global Engineering Education Conference, 2024

Do You See What I See? Eliciting High-Level Visualization Comprehension Ghulam Jilani Quadri, Zeyu Wang, Zhehao Wang, Jennifer Adorno Nieves, Paul Rosen, and Danielle Albers Szafir ACM SIGCHI Conference on Human Factors in Computing Systems, 2024

CLAMS: Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering Hyeon Jeon, Ghulam Jilani Quadri, Hyunwook Lee, Paul Rosen, Danielle Albers Szafir, and Jinwook Seo IEEE Transaction on Computer Graphics and Visualization (IEEE VIS), 2024

A Comparative Study Of The Perceptual Sensitivity Of Topological Visualizations To Feature Variations Tushar M. Athawale, Bryan Triana, Tanmay Kotha, David Pugmire, and Paul Rosen IEEE Transaction on Computer Graphics and Visualization (IEEE VIS), 2024

Automatic Scatterplot Design Optimization For Clustering Identification Ghulam Jilani Quadri, Jennifer Adorno Nieves, Brenton M. Wiernik, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics (TVCG), 2023

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing Hamza Elhamdadi, Shaun Canavan, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics (IEEE VIS), 2022

Through The Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis Of Peer Review Text Zachariah Beasley, Alon Friedman, and Paul Rosen IEEE Computer Graphics and Applications (CG\&A) Special Issue on Visualization Education and Teaching Visualization Literacy, 2021

A Survey Of Perception-Based Visualization Studies By Task Ghulam Jilani Quadri, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022

Polarity In The Classroom: An Application Leveraging Peer Sentiment Towards Scalable Assessment Zachariah Beasley, Les Piegl, and Paul Rosen IEEE Transactions on Learning Technologies, 2021

Modeling The Influence Of Visual Density On Cluster Perception In Scatterplots Using Topology Ghulam Jilani Quadri, and Paul Rosen IEEE Transactions on Visualization and Computer Graphics (IEEE InfoVis), 2021

Linesmooth: An Analytical Framework For Evaluating The Effectiveness Of Smoothing Techniques On Line Charts Paul Rosen, and Ghulam Jilani Quadri IEEE Transactions on Visualization and Computer Graphics (IEEE VAST), 2021

An Efficient Data Retrieval Parallel Reeb Graph Algorithm Mustafa Hajij, and Paul Rosen Algorithms: Special Issue on Topological Data Analysis, 2020

Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology Mustafa Hajij, Paul Rosen, and Elizabeth Munch International Conference on Intelligent Data Science Technologies and Applications (IDSTA), 2020

Topolines: Topological Smoothing For Line Charts Paul Rosen, Ashley Suh, Christopher Salgado, and Mustafa Hajij EuroVis ’20 Proceedings of the Eurographics / IEEE VGTC Conference on Visualization: Short Papers, 2020

Leveraging Peer Feedback to Improve Visualization Education Zachariah Beasley, Alon Friedman, Les Piegl, and Paul Rosen Pacific Vis, 2020

Visual Inspection Of Dbs Efficacy Brad E Hollister, Gordon Duffley, Chris Butson, Chris R. Johnson, and Paul Rosen IEEE SciVis Short Papers, 2019

You Can’T Publish Replication Studies (And How To Anyways) : Position Paper Ghulam Jilani Quadri, and Paul Rosen Vis X Vision Workshop at IEEE VIS, 2019

Theses

• Rifat Ara Proma, Reading Between the Lines: Toward Human-Centered Clutter Reduction in Line Charts, May 2026
• Curtis Davis, Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation during Dimensionality Reduction, Oct 2021
  < https://www.proquest.com/docview/2605303723 >
• Ghulam Jilani Quadri, Constructing Frameworks for Task-Optimized Visualizations, Oct 2021
  < https://www.proquest.com/docview/2600333411 >
  2022 VGTC Best Dissertation Award
• Elhamdadi, Hamza, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, July 2021
  < https://www.proquest.com/docview/2563672460 >
• Tanmay Kotha, Establishing Topological Data Analysis: A Comparison of Visualization Techniques, Oct 2020
  < https://www.proquest.com/docview/2456448127 >

Presentations

• Paul Rosen, Effective Reeb Graph Visualization, ERC Tori Workshop, Wimereux, France, July 2026
• Paul Rosen. People and Patterns: Aligning Visualization Intent with Human Interpretation, Linköping University, Norrköping, Sweden, Mar. 2026
• Paul Rosen, Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making. International Symposium on Visual Computing, Virtual, Nov. 2025
• Paul Rosen, GASP: A Gradient-Aware Shortest Path Algorithm for Boundary-Confined 2-Manifold Reeb Graph Visualization, TopoInVis Workshop @ IEEE VIS, Vienna, AT, Nov. 2025
• Paul Rosen, Topology and InfoVis: Scatterplot, Line Charts, and Graphs. SIAM Mathematics of Data Science Conference Minisymposium on Topological Data Visualization, Atlanta, GA, Oct. 2024
• Tushar Athawale, Uncertainty Visualization Of Critical Points Of 2D Scalar Fields For Parametric And Nonparametric Probabilistic Models, IEEE VIS, Oct. 2024.
• Ghulam Jilani Quadri, Do You See What I See? Eliciting High-Level Visualization Comprehension, ACM CHI, May 2024
• Paul Rosen, Topology in InfoVis: Scatterplots, Line Charts, Graphs, and Dimension Reduction, TDA in VIS Summer School, Norrkoping, Sweden (given virtually), Aug. 2023
• Paul Rosen, Improving Visualization Through Shape–A Discussion on Perception, Confidence, and Trust, Tufts University, Medford, MA, Apr. 2023
• Paul Rosen, Perception, Confidence, and Trust in Large Data Visualization, Computer Graphics Forum, Idaho Fall, IA, Apr. 2023
• Paul Rosen, Improving Visualization Through ShapeA Discussion of Topology-based Methods in Visualization, Campus Alliance for Advanced Visualization (CAAV) Seminar Series, Virtual, Mar. 2023
• Paul Rosen, Through the Looking Glass: Insights into Visualization Pedagogy through Sentiment Analysis of Peer Review Text, IEEE VIS, Oct. 2022
• Ghulam Jilani Quadri, A Survey of Perception-Based Visualization Studies by Task, IEEE VIS, Oct. 2022
• Md Dilshadur Rahman, A Qualitative Evaluation and Taxonomy of Student Annotations on Bar Charts, VisComm Workshop @ IEEE VIS, Oct. 2022
• Bhavana Doppalapudi, Untangling Force-Directed Layouts Using Persistent Homology, TopoInVis Workshop @ IEEE VIS, Oct. 2022
• Paul Rosen, Taming the Uncertainty Induced During Data Transformation and Visual Encoding, Dagstuhl Seminar 22331 (Virtual), Aug. 2022
• Paul Rosen, Optimizing and Interacting with Information Visualizations Using Topological Data Analysis, Topological Data Visualization Workshop, May 2022
• Paul Rosen, Improving the Visualization of Large and Complex Data Through Topology-based Design and Interaction, University of Utah, Feb. 2022
• Hamza Elhamdadi, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, IEEE VIS, Oct. 2021
• Ghulam Jilani Quadri, Modeling the Perception for Effective Visualization, VIS Summer Camp Student Seminar Series, Virtual, May 2021
• Hamza Elhamdadi, AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing, VIS Virtual Summer Camp Student Seminar Series, June 2021
• Paul Rosen, LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts, IEEE VAST, Oct. 2020
• Ghulam Jilani Quadri, Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology, IEEE InfoVis, Oct. 2020
• Mustafa Hajij, Fast And Scalable Complex Network Descriptor Using Pagerank And Persistent Homology, IDSTA, October 2020
• Zachariah Beasley, Leveraging Peer Feedback to Improve Visualization Education, PacificVis, June 2020
• Ashley Suh, TopoLines: Topological Smoothing for Line Charts, EuroVis, May 2020.
• Brad Hollister, Visual Inspection of DBS Efficacy, IEEE VIS Short Paper Track, Oct. 2019
• Ghulam Jilani Quadri, You Can’t Publish Replication Studies (and How to Anyways): Position Paper, Vis X Vision, Oct. 2019

Software

GASP: A Gradient-Aware Shortest Path Algorithm for Boundary-Confined 2-Manifold Reeb Graph Visualization
< https://github.com/shape-vis/gasp >

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
< https://github.com/USFDataVisualization/AffectiveTDA >

A Survey of Perception-Based Visualization Studies by Task
< https://usfdatavisualization.github.io/VisPerceptionSurvey/ >

LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
< https://github.com/USFDataVisualization/LineSmooth >
< https://usfdatavisualization.github.io/LineSmoothDemo/ >

Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology
< https://github.com/USFDataVisualization/TopoClusterPerception >
< https://usfdatavisualization.github.io/TopoClusterPerceptionDemo/ >

TopoLines: Topological Smoothing for Line Charts
< https://github.com/USFDataVisualization/TopoLines >
< https://usfdatavisualization.github.io/TopoLines/ >

Personnel

Sefat Rahman (Graduate RA, Jan 2023-May 2026)
Scientific Computing and Imaging Institute
University of Utah
sefat DOT rahman AT utah DOT edu

Rifat Ara Proma (Graduate RA, Aug 2024-May 2026)
Scientific Computing and Imaging Institute
University of Utah
rifat DOT proma AT utah DOT edu

Nicolas Baret (REU, May 2025-Aug 2025)
Scientific Computing and Imaging Institute
University of Utah
u1340304 AT utah DOT edu

Annabelle Warner (REU, Jan 2025-May 2025)
Scientific Computing and Imaging Institute
University of Utah
annabelle DOT warner AT utah DOT edu
Kahlert School of Computing 2025 Undergrad Research Award

Bhavana Doppalapudi (Graduate RA, Jan 2020-July 2022)
Computer Science & Engineering
University of South Florida
bdoppalapudi AT usf DOT edu

Keshon Primus (Undergrad RA, May 2022-July 2022)
Computer Science & Engineering
University of South Florida
keshonprimus AT usf DOT edu
Subsequent Position: Student at University of South Florida

Francis Hahn (REU, May 2020-May 2022)
Computer Science & Engineering
University of South Florida
fhahn AT usf DOT edu
Subsequent Position: Graduate student at University of South Florida

Bryan Triana (REU, Sept 2021-May 2022)
Computer Science and Engineering
University of South Florida
bryantriana AT usf DOT edu
Subsequent Position: Software Engineer at Google

Ghulam Jilani Quadri, PhD, Dec 2021
Graduate RA, Aug 2020 – Dec 2021
Computer Science & Engineering
University of South Florida
ghulamjilani AT usf DOT edu
Subsequent Position: CI Fellows Postdoc at UNC Chapel Hill

Hamza Elhamdadi, MS Aug 2021
Graduate RA, Aug 2020 – Aug 2021
Computer Science & Engineering
University of South Florida
hme1 AT usf DOT edu 
Subsequent Position: PhD Student at UMass Amherst

Raquel Garcia, BS
REU, May 2020-May 2021
Computer Science & Engineering
University of South Florida
raquelgarcia AT usf DOT edu
Subsequent Position: Software Engineer at Microsoft

Tanmay Kotha, MS, Dec 2020
Graduate RA, Aug 2019 – July 2020
Computer Science & Engineering
University of South Florida
tanmay AT usf DOT edu 
Subsequent Position: Software Engineering at Amazon

Curtis Davis, BS/MS, Dec 2021
REU, May 2020-Dec 2020
Computer Science & Engineering
University of South Florida
ctd AT usf DOT edu
Subsequent Position: Masters student at the University of South Florida

Collin Chimbwanda, BS
REU, May 2020-Dec 2020
Computer Science & Engineering
University of South Florida
cchimbwanda AT usf DOT edu

Collaborators

Alon Friedman
Chris R. Johnson
Zachariah Beasley
Ashley Suh
Shaun Canavan
Mustafa Hajij
Junyi Tu
Tushar Athawale
Dave Pugmire

Acknowledgments

This material is based upon work supported or partially supported by the National Science Foundation under Grant No. 2316496, project titled “CAREER: Discovering Structure in Uncertainty: Using Topology for Interactive Visualization of Uncertainty”. 

Any opinions, findings, and conclusions or recommendations expressed in this project are those of author(s) and do not necessarily reflect the views of the National Science Foundation. 

• Paul Rosen – University of South Florida
• Bei Wang – University of Utah
• Chris Johnson – University of Utah
• Betsy Mills – San Jose State University
• Jeff Kern – National Radio Astronomy Observatory

Collaborators

• Anil Seth – University of Utah
• Adam Ginsburg – National Radio Astronomy Observatory
• Julia Kamenetzky – Westminster College, Salt Lake City

About

ALMA-TDA is a persistent homology based engine for noise removal in ALMA data cubes. ALMA-TDA uses a data structure known as the contour tree to summarize and simplify the data. The figure to the right shows an example of the process. ALMA-TDA takes a scalar function from a data cube, computes a topological skeleton in the form of a contour tree, and simplifies that skeleton and the scalar field. The feature removal only impacts regions that connect pairs of critical points. In this way, ALMA-TDA provides the minimum perturbation of the field required to remove unwanted features. 
(a) An image of a 2D scalar function before simplification. (b) 3D height map of the contours corresponding to the scalar function shown in (a). (c) The contour tree structures that capture the features (i.e., relationships among local minima, local maxima, and saddle points). (d)-(f): The image, 3D height map, and the contour tree after simplifying the features.

Get the Software!

Binaries of our software, including a usage manual, can be downloaded from: https://github.com/SCIInstitute/ALMA-TDA/releases
Source code may be accessed at: https://github.com/SCIInstitute/ALMA-TDA

Demonstration of Simplification

The following example (data provided by Anil Seth) demonstrates our ability to simplify volumes. Each pair represents a layer before (left) and after (right) simplification using the Contour Tree.
       
       
       
       
       
       

Publications

• Paul Rosen, Bei Wang, Anil Seth, Betsy Mills, Adam Ginsburg, Julia Kamenetzky, Jeff Kern, Chris R. Johnson, Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes, under submission to IEEE InfoVis, 2017.
  
  Preprint available at: https://cspaul.com/projects/alma-tda/ALMA-TDA-InfoVis17.pdf
• Paul Rosen, Junyi Tu, and Les Piegl, A Hybrid Solution to Calculating Augmented Join Trees of 2D Scalar Fields in Parallel}, accepted to CAD Conference and Exhibition, extend abstract, 2017.
  
  Preprint available at: https://cspaul.com/projects/alma-tda/ALMA-TDA-CAD17.pdf
• Paul Rosen, Junyi Tu, and Les Piegl, A Hybrid Solution to Parallel Calculation of Augmented Join Trees of Scalar Fields in Any Dimension, under submission to the Journal of Computer Aided Design and Applications, 2017.
  
  Preprint available at: https://cspaul.com/projects/alma-tda/ALMA-TDA-CADandA.pdf
• Bei Wang, Analysis and Visualization of ALMA Data Cubes, Workshop for Women in Computational Topology (WinCompTop), Poster, 2016
  
  Poster available at: https://cspaul.com/projects/alma-tda/16-08-Wang-WinCompTop.pdf

Presentations

• Paul Rosen, Opportunities to Advance Visualization Capabilities for ALMA Data Cubes through Topological Data Analysis, Socorro NM, Jan 2016
• Paul Rosen, Feature Extraction & Data Cube Visualization Through Topological Data Analysis, Future Science Development Workshop, Charlottesville VA, Aug 2016
• Bei Wang, Advancing the Visualization Capabilities of ALMA Data Cubes through Topological Data Analysis, High Energy and Astrophysics (HEAP) Seminar Series, University of Utah, Salt Lake City UT, Sept 2016
• Paul Rosen, Feature Extraction & Data Cube Visualization Through Topological Data Analysis, CASA Users Group, Socorro NM (presented remotely), Sept 2016
• Paul Rosen, Exploring Applications of Persistent Homology in Signal and Image Processing, USF Computer Vision Group, Oct 2016

Panel

• Panel on ALMA TDA, Salt Lake City UT, Jan 2017
  • Participants: Paul Rosen, Bei Wang, Ayla Khan, Betsy Mills, Chris Johnson, Adam Ginsburg, Julia Kamenetzky

Mission

Science Case

Radio astronomy is currently undergoing a revolution driven by new observing capabilities. The current generation of radio and millimeter telescopes, particularly the Atacama Large Millimeter Array (ALMA), offers enormous advances in capabilities, including significantly increased sensitivity, spatial and spectral resolution, and spectral bandwidth. While these advances represent an unprecedented opportunity to advance scientific understanding, they also pose a significant challenge. Although the higher sensitivity and resolution they provide in some cases yield new detections of sources with well-ordered structure that is easy to interpret with current tools (e.g., HL Tau [9]), these advances equally often lead to the detection of structure with increased spatial and spectral complexity (e.g., new molecules in the chemically-rich massive star forming region Sgr B2, outflows in the nuclear region of the nearby galaxy NGC 253, and rich kinematic structure in the giant molecular cloud “The Brick” [1, 2, 12]). The new complexity present in current spectral line datasets challenges not only the existing tools for fundamental analysis of these datasets, but also users’ ability to explore and visualize their data.
Whether scientists can navigate and correctly interpret this new complexity will determine their success in addressing a number of important scientific questions. Among the topics driven by the detection of more complex structures are ISM turbulence [4, 13], the star formation process [7], filaments [11], molecular cloud structure and kinematics [12], and the kinematics of nearby galaxies [5, 6, 8] and high redshift galaxies [10, 14].
An even greater challenge arises from our ability to detect an increased number of spectral lines in more and more sources. There simply are no tools capable of simultaneously visualizing, comparing, and analyzing the dozens to hundreds of data cubes for all of the detected spectral lines in a given source. Such standard methods of both visualizing and exploring data as moment maps, channel maps, playing cube like a video, or 3D models, cannot scale up to the case of large numbers of lines, even in non-complex, well-ordered cases, such as rotating disks, or expanding stellar shells. Users become overwhelmed by, for example, comparing these typical diagnostics for two lines, side by side or one at a time. In the richest sources with thousands of lines, such comparisons will simply be impossible—it becomes necessary to resort to methods that entirely throw away either the spectral information such as Principle Component Analysis (PCA) of moment maps or the spatial information that requires model fitting of complex spectra. As a result, both exploration and analysis of the data becomes not only time consuming, but potentially incomplete.

References

DarkView: Parameter Space Visualization Tool

DarkView is a parameter space visualization tool under development in collaboration with Pearl Sandick for the purpose of parameter space exploration in particle physics.

About

DarkView is a parameter space visualization tool under development in collaboration with Pearl Sandick for the purpose of parameter space exploration in particle physics.

Software

Version 0.13

• Source Code (567 KB)
• Windows 64-bit (4.6 MB)
• Mac OS X (8.5 MB)

Version 0.11

• Source Code (725 KB)
• Windows 64-bit (4.6 MB)
• Mac OS X (7.8 MB)

Version 0.1

• Source Code (455 KB)
• Windows 32-bit (4.0 MB)
• Windows 64-bit (4.6 MB)
• Mac OS X (7.8 MB)

Building may also require:

• Qt 4.8

Sample Data

• Sample Data 1: Input (1.1 MB) | Output (850 KB)
• Sample Data 2: Input (130 KB) | Output (25 KB)
• Sample Data 3: Input (82 KB) | Output (380 KB)

Release History

4/11/2013: Version 0.13

• Minor bug fixes
• Mac OSX performance problems corrected

3/26/2013: Version 0.12

• Minor bug fixes
• Help and about context menus added
• Scale added to scatterplot view

2/10/2013: Version 0.11

• Minor bug fixes
• Pearson Correlation now used for coloring
• Progressive rendering for more responsive visualization
• Parser for labels allows new tags to produce greek characters (\sigma, \nu, and \gamma)

1/5/2013: Version 0.1 (Initial Release)

Rectilinear Texture Warping For Fast Adaptive Shadow Mapping
Paul Rosen
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2012

Abstract

Video

Downloads

Citation

Bibtex

@inproceedings{rosen2012rectilinear,
  title = {Rectilinear Texture Warping For Fast Adaptive Shadow Mapping},
  author = {Rosen, Paul},
  booktitle = {Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and
    Games},
  series = {I3D},
  pages = {151--158},
  year = {2012},
  keywords = {adaptive sampling, rendering, shadow algorithms},
  abstract = {Conventional shadow mapping relies on uniform sampling for producing hard
    shadow in an efficient manner. This approach trades image quality in favor of
    efficiency. A number of approaches improve upon shadow mapping by combining multiple
    shadow maps or using complex data structures to produce shadow maps with multiple
    resolutions. By sacrificing some performance, these adaptive methods produce shadows
    that closely match ground truth. This paper introduces Rectilinear Texture Warping (RTW)
    for efficiently generating adaptive shadow maps. RTW images combine the advantages of
    conventional shadow mapping - a single shadow map, quick construction, and constant time
    pixel shadow tests, with the primary advantage of adaptive techniques - shadow map
    resolutions which more closely match those requested by output images. RTW images
    consist of a conventional texture paired with two 1-D warping maps that form a
    rectilinear grid defining the variation in sampling rate. The quality of shadows
    produced with RTW shadow maps of standard resolutions, i.e. 2,048x2,048 texture for
    1080p output images, approaches that of raytraced results while low overhead permits
    rendering at hundreds of frames per second.}
}

TC: Node Trajectory Compressor

TC is a set of compression algorithms designed for lossy compression of time varying node positional data. The original algorithms were designed in collaboration with Voicu Popescu though he is not involved in the current software development. Details of the algorithms used are presented in the [[publications:Rosen.2011.TVCG|paper]] reporting our results.

About

TC is a set of compression algorithms designed for lossy compression of time varying node positional data. The original algorithms were designed in collaboration with Voicu Popescu though he is not involved in the current software development. Details of the algorithms used are presented in the paper reporting our results.

Software

• Source Code and Sample Data (122 KB)

Building may also require:

• Qt 4.8
• TCLAP

Release History

• 1/16/2013: Version 0.11 Released
• 1/10/2013: Version 0.1 (Initial) Release