Parallel Computation of Piecewise Linear Morse-Smale Segmentations

This paper presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper.Comment: Journal: IEEE Transactions on Visualization and Computer Graphics / Submitted: 22-Jun-2022 / Accepted: 13-Mar-202

A Framework for Uncertainty-Aware Visual Analytics of Proteins

Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate from various sources and can have a significant effect on algorithms and decisions based upon such models. Incorporating uncertainty in state-of-the-art visualization approaches for molecular data is an important issue to ensure that scientists analyzing the data are aware of the inherent uncertainty present in the representation of the molecular data. In this work, we introduce a framework that allows biochemists to explore molecular data in a familiar environment while including uncertainty information within the visualizations. Our framework is based on an anisotropic description of proteins that can be propagated along with required computations, providing multiple views that extend prominent visualization approaches to visually encode uncertainty of atom positions, allowing interactive exploration. We show the effectiveness of our approach by applying it to multiple real-world datasets and gathering user feedback

A Framework for Uncertainty-Aware Visual Analytics of Proteins

Due to the limitations of existing experimental methods for capturing stereochemical molecular data, there usually is an inherent level of uncertainty present in models describing the conformation of macromolecules. This uncertainty can originate from various sources and can have a significant effect on algorithms and decisions based upon such models. Incorporating uncertainty in state-of-the-art visualization approaches for molecular data is an important issue to ensure that scientists analyzing the data are aware of the inherent uncertainty present in the representation of the molecular data. In this work, we introduce a framework that allows biochemists to explore molecular data in a familiar environment while including uncertainty information within the visualizations. Our framework is based on an anisotropic description of proteins that can be propagated along with required computations, providing multiple views that extend prominent visualization approaches to visually encode uncertainty of atom positions, allowing interactive exploration. We show the effectiveness of our approach by applying it to multiple real-world datasets and gathering user feedback