Ashley Suh awarded CREU for research

Student Ashley Suh was awarded a $3,000 stipend for her research project, “Using Persistent Homology to Drive Interactive Graph Drawing,” from the Collaborative Research Experience for Undergraduates (CREU). In addition to this, she will receive up to $1,500 for student travel and/or research supplies. The proposal for funding was submitted by Dr. Paul Rosen, who will be Suh’s faculty mentor throughout her research.

Suh and Rosen’s project involves working to develop a new method for drawing and interacting with graphs, such as for a social network. The challenge with many graphs is that their highly interconnected nature causes them to look like a hairball when drawn. Their project uses a technique called “persistent homology” to identify important structures in the data. Those structures are then interactively selected and used to “pull apart” the hairball, enabling clearer analysis of the graph.

The CREU program is sponsored by the Computing Research Association Committee on the Status of Women in Computing Research (CRA-W). Its intention is, according to their website, “to increase the number of women and underrepresented groups enrolled undergraduate studies in the fields of computer science and computer engineering by exposing them to the joy and potential of research.” The criteria for choosing which projects are funded include the stipulation that the project must “enable student empowerment, leadership development, confidence building, and skill building.”

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Interpreting Galilean Invariant Vector Field Analysis via Extended Robustness

The topological notion of robustness introduces mathematically rigorous approaches to interpret vector field data. Robustness quantifies the structural stability of critical points with respect to perturbations and has been shown to be useful for increasing the visual interpretability of vector fields. However, critical points, which are essential components of vector field topology, are defined with respect to a chosen frame of reference. The classical definition of robustness, therefore, depends also on the chosen frame of reference. We define a new Galilean invariant robustness framework that enables the simultaneous visualization of robust critical points across the dominating reference frames in different regions of the data. We also demonstrate a strong connection between such a robustness-based framework with the one recently proposed by Bujack et al., which is based on the determinant of the Jacobian. Our results include notable observations regarding the definition of stable features within the vector field data.

Interpreting Galilean Invariant Vector Field Analysis via Extended Robustness
B Wang, R Bujack, P Rosen, P Skraba, H Bhatia, H Hagen
Topology-based Methods in Visualization (TopoInVis)

Using data indexing for remote visualization of point cloud data

We present a new approach for accessing and visualizing point-based data in CAD applications. Instead of developing a traditional database around spatial data structures, our approach augments a data indexing engine to enable quick access to data. The primary advantage of an indexing engine is flexibility. The approach enables both range queries for accessing data spatially and resolution queries to access data at appropriate spatial resolutions. Our approach is robust to very large datasets, naturally supporting remote visualization and near-real-time input data streams. We demonstrate our approach on 2 large datasets, one 45M points, the other 53M points.

Using data indexing for remote visualization of point cloud data
P Rosen, LA Piegl
Computer-Aided Design and Applications 14 (6), 789-795

Generating point clouds for slicing free-form objects for 3-D printing

3-D printing, also known as additive manufacturing, has gained a lot of attention both within and outside CAD research. Even popular media have touted the technology as one of the game changer technologies of the 21st century. Simply stated, most printing devices add material to an unfinished part, layer by layer, until the entire object is completed. In order to make this happen, the object is sliced into thin layers which are produced and glued together. Since NURBS are the standard form of modeling tools, the process entails converting the NURBS into an STL model (piecewise triangular model) which is then sliced into a set of closed polygonal loops. In order to avoid the many problems with the STL-based slicing, in this paper we investigate a point cloud-based approach to direct slicing of NURBS based models. It uses the original NURBS model and converts the model into a point cloud, based on layer thickness and accuracy requirements, for direct slicing. The only major computational requirement is point evaluation which can be done error free and in an inexpensive manner. The generation of the point cloud is the main topic of this paper.

Generating point clouds for slicing free-form objects for 3-D printing
W Oropallo, LA Piegl, P Rosen, K Rajab
Computer-Aided Design and Applications 14 (2), 242-249

IEEE Pacific Visualization Symposium 2016 Best Paper Award

Paul Rosen, in collaboration with Primoz Skraba (Jozef Stefan Institute), Bei Wang (University of Utah), Guoning Chen (University of Houston), Harsh Bhatia (Lawrence Livermore National Laboratory), and Valerio Pascucci (University of Utah), was awarded the best paper award at the IEEE Pacific Visualization Symposium 2016 for their paper titled, “Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion”.

This IEEE sponsored international visualization symposium is held in the Asia-Pacific region, with the objective to foster greater exchange between visualization researchers and practitioners, and to draw more researchers in the Asia-Pacific region to enter this rapidly growing area of research.

More information on the IEEE Pacific Visualization Symposium can be found here.

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International Conference on Information Visualization Theory and Applications Awards

Paul Rosen and his PhD student from University of Utah, Hoa Nguyen, received two awards at the 2016 International Conference on Information Visualization Theory and Applications.

Paul Rosen and Hoa Nguyen received the Best Paper Award for their publication “Improved Identification of Data Correlations through Correlation Coordinate Plots”. They also received the Best PhD Project Award for “Data Scalable Approach for Identifying Correlation in Large and Multi-dimensional Data”. According to the conference awards page, “Papers receiving these awards were selected from a set of outstanding papers, based on the quantitative and qualitative classifications as well as comments provided by the program committee reviewers, their final classification as full paper and their oral presentation at the conference”.

More information on the awards, as well as previous award winners, can be found here.

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Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with a guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. We apply our method to synthetic and simulation datasets to demonstrate its effectiveness.

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion
P Skraba, P Rosen, B Wang, G Chen, H Bhatia, V Pascucci
Transactions on Visualization and Computer Graphics

Rosen awarded ALMA Development Project grant from the National Radio Astronomy Observatory

Paul Rosen along with Bei Wang (University of Utah), Chris Johnson (University of Utah), Jeff Kern (NRAO), and Betsy Mills (NRAO) received a 1-year ALMA Development Project grant from the National Radio Astronomy Observatory for $185k. The grant is titled “Feature Extraction and Visualization of AMLA Data Cubes through Topological Data Analysis”.

The project is a feasibility study for applying forms of data analysis and visualization never before tested by the ALMA community. Through contour tree-based Topological Data Analysis, we seek to improve upon existing data cube analysis and visualization. This will come in the form of improved accuracy and speed in finding features, which are robust to noise, and a better visual description of features once identified. We will build prototype software, which creates visualizations that help in characterizing and analyzing the spectra of complex spectral line sources within a given data cube.

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Rosen Receives NSF Award

Paul Rosen along with Bei Wang (University of Utah) received a NSF grant with additional collaborative award for Carlos Scheidegger (University of Arizona) for 4 years totaling $1.03M. The grant is titled “III: Medium: Collaborative Research: Topological Data Analysis for Large Network Visualization.” Rosen’s portion of this grant, to be subcontracted from the University of Utah, is $325K.

This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically.

For more information about the award, click here and for more information about the award with amendments, click here.

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Ten challenges in 3D printing

Three dimensional printing has gained considerable interest lately due to the proliferation of inexpensive devices as well as open source software that drive those devices. Public interest is often followed by media coverage that tends to sensationalize technology. Based on popular articles, the public may create the impression that 3D printing is the Holy Grail; we are going to print everything as one piece, traditional manufacturing is at the brink of collapse, and exotic applications, such as cloning a human body by 3D bio-printing, are just around the corner. The purpose of this paper is to paint a more realistic picture by identifying ten challenges that clearly illustrate the limitations of this technology, which makes it just as vulnerable as anything else that had been touted before as the next game changer.

Ten challenges in 3D printing
W Oropallo, LA Piegl
Engineering with Computers 32 (1), 135-148