KnowTD─An Actionable Knowledge Representation System for Thermodynamics.

We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainable solutions with a guarantee of correctness. The actionable knowledge representation system that we have created for this purpose is called KnowTD. It is based on an ontology of thermodynamics that represents knowledge of thermodynamic theory, material properties, and thermodynamic problems.

Visual Parameter Space Analysis for Optimizing the Quality of Industrial Nonwovens.

Technical textiles, in particular, nonwovens used, for example, in medical masks, have become increasingly important in our daily lives. The quality of these textiles depends on the manufacturing process parameters that cannot be easily optimized in live settings. In this article, we present a visual analytics framework that enables interactive parameter space exploration and parameter optimization in industrial production processes of nonwovens.

Knowledge Rocks: Adding Knowledge Assistance to Visualization Systems.

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions.

Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data.

Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA the axes can still be annotated meaningfully.

Security in Process: Visually Supported Triage Analysis in Industrial Process Data.

Operation technology networks, i.e. hard- and software used for monitoring and controlling physical/industrial processes, have been considered immune to cyber attacks for a long time.

Dynamic Nested Tracking Graphs.

This work describes an approach for the interactive visual analysis of large-scale simulations, where numerous superlevel set components and their evolution are of primary interest. The approach first derives, at simulation runtime, a specialized Cinema database that consists of images of component groups, and topological abstractions. This database is processed by a novel graph operation-based nested tracking graph algorithm (GO-NTG) that dynamically computes NTGs for component groups based on size, overlap, persistence, and level thresholds.

Exploring Variability within Ensembles of Decadal Climate Predictions.

Ensemble simulations are used in climate research to account for natural variability. For medium-term decadal predictions, each simulation run is initialized with real observations from a different day resulting in a set of possible climatic futures. Understanding the variability and the predictive power in this wealth of data is still a challenging task.

Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks.

Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes.

Rules and Self-Organizing Properties of Post-embryonic Plant Organ Cell Division Patterns.

Plants form new organs with patterned tissue organization throughout their lifespan. It is unknown whether this robust post-embryonic organ formation results from stereotypic dynamic processes, in which the arrangement of cells follows rigid rules. Here, we combine modeling with empirical observations of whole-organ development to identify the principles governing lateral root formation in Arabidopsis.

Reconstruction and Visualization of Coordinated 3D Cell Migration Based on Optical Flow.

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities.