Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation

Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays however, sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly- or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing a co-morbidity network

Terahertz response of patterned epitaxial graphene

We study the interaction between polarized terahertz (THz) radiation and micro-structured large-area graphene in transmission geometry. In order to efficiently couple the radiation into the two-dimensional material, a lateral periodic patterning of a closed graphene sheet by intercalation doping into stripes is chosen. We observe unequal transmittance of the radiation polarized parallel and perpendicular to the stripes. The relative contrast, partly enhanced by Fabry-Perot oscillations reaches 20 %. The effect even increases up to 50 % when removing graphene stripes in analogy to a wire grid polarizer. The polarization dependence is analyzed in a large frequency range from < 80 GHz to 3 THz, including the plasmon-polariton resonance. The results are in excellent agreement with theoretical calculations based on the electronic energy spectrum of graphene and the electrodynamics of the patterned structureThe authors thank J. Jobst for fruitful discussions. The research was performed in the framework of the Sonderforschungsbereich 953 "Synthetic carbon allotropes", funded by Deutsche Forschungsgemeinschaft. We acknowledge support from the EC under Graphene Flagship (contract no. CNECT-ICT-604391)

Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process

Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes, provided that turnover times and detection thresholds of the tests are low enough. If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern.Comment: Supplementary material is included in the manuscript PD

Terahertz response of patterned epitaxial graphene

We study the interaction between polarized terahertz (THz) radiation and micro-structured large-area graphene in transmission geometry. In order to efficiently couple the radiation into the two-dimensional material, a lateral periodic patterning of a closed graphene sheet by intercalation doping into stripes is chosen. We observe unequal transmittance of the radiation polarized parallel and perpendicular to the stripes. The relative contrast, partly enhanced by Fabry–Perot oscillations reaches 20%. The effect even increases up to 50% when removing graphene stripes in analogy to a wire grid polarizer. The polarization dependence is analyzed in a large frequency range from <80 GHz to 3 THz, including the plasmon–polariton resonance. The results are in excellent agreement with theoretical calculations based on the electronic energy spectrum of graphene and the electrodynamics of the patterned structure

Integration strategies in the visualization of multifaceted spatial data

In this thesis, I explore integration strategies in the visualization of multifaceted spatial data. First, I chart the design space of visual and functional integration in the scope of a taxonomy. Visual integration describes how representations of the different data facets can be visually composed. Functional integration, describes the various ways in which events can be coordinated across the visually integrated representations. In the second part of this thesis, I present contributions to the field of visualization in the context of concrete integration applications for exploration and presentation scenarios. Thereby I first address a set of challenges in the scope of a decision making scenario in lighting design. In this context, I propose LiteVis, a system that integrates representations of the simulation parameter space with representations of all relevant aspects of the simulation output. The integration of these heterogeneous aspects together with a novel ranking visualization are the key to enabling an efficient exploration and comparison of lighting parametrizations. In presentation scenarios, on the other side, the generation of insights often cannot rely on user interaction and therefore needs a different approach. The challenge is to generate visually appealing, yet information-rich representations for mainly passive observation. Integration can thereby be applied as a substitute for interaction. In this context, I address two different challenges in the domain of molecular visualization. First, I investigate how to convey relations between different representations of a molecular data set, such as a virus. I thereby propose a novel technique for creating transitions between representations that are re-usable for different data sets, and can be combined in a modular fashion. The second challenge concerns the presentation of transitions between development states of molecular models, where the actual biochemical process of the transition is not exactly known or it is too complex to represent. In order to overcome a potential indication of false relationship information, a technique is introduced that proposes the continuous abstraction to the lowest common denominator in terms of available visual detail, at which the relationship between two models can be accurately conveyed. Finally, I reflect on the taxonomy in respect to the presented techniques. The results demonstrate that integration is a versatile tool in overcoming key challenges in the visualization of multifaceted spatial data.14

Visual analytics and rendering for tunnel crack analysis : a methodological approach for integrating geometric and attribute data

The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature.Austrian Science Fund (FWF)Vienna Science and Technology Fund (WWTF

When does a disaster become a systemic event? Estimating indirect economic losses from natural disasters

Reliable estimates of indirect economic losses arising from natural disasters are currently out of scientific reach. To address this problem, we propose a novel approach that combines a probabilistic physical damage catastrophe model with a new generation of macroeconomic agent-based models (ABMs). The ABM moves beyond the state of the art by exploiting large data sets from detailed national accounts, census data, and business information, etc., to simulate interactions of millions of agents representing \backslashemph{each} natural person or legal entity in a national economy. The catastrophe model introduces a copula approach to assess flood losses, considering spatial dependencies of the flood hazard. These loss estimates are used in a damage scenario generator that provides input for the ABM, which then estimates indirect economic losses due to the event. For the first time, we are able to link environmental and economic processes in a computer simulation at this level of detail. We show that moderate disasters induce comparably small but positive short- to medium-term, and negative long-term economic impacts. Large-scale events, however, trigger a pronounced negative economic response immediately after the event and in the long term, while exhibiting a temporary short- to medium-term economic boost. We identify winners and losers in different economic sectors, including the fiscal consequences for the government. We quantify the critical disaster size beyond which the resilience of an economy to rebuild reaches its limits. Our results might be relevant for the management of the consequences of systemic events due to climate change and other disasters

Multiscale Visualization and Scale-Adaptive Modification of DNA Nanostructures

International audienceWe present an approach to represent DNA nanostructures in varying forms of semantic abstraction, describe ways to smoothly transition between them, and thus create a continuous multiscale visualization and interaction space for applications in DNA nanotechnology. This new way of observing, interacting with, and creating DNA nanostructures enables domain experts to approach their work in any of the semantic abstraction levels, supporting both low-level manipulations and high-level visualization and modifications. Our approach allows them to deal with the increasingly complex DNA objects that they are designing, to improve their features, and to add novel functions in a way that no existing single-scale approach offers today. For this purpose we collaborated with DNA nanotechnology experts to design a set of ten semantic scales. These scales take the DNA's chemical and structural behavior into account and depict it from atoms to the targeted architecture with increasing levels of abstraction. To create coherence between the discrete scales, we seamlessly transition between them in a well-defined manner. We use special encodings to allow experts to estimate the nanoscale object's stability. We also add scale-adaptive interactions that facilitate the intuitive modification of complex structures at multiple scales. We demonstrate the applicability of our approach on an experimental use case. Moreover, feedback from our collaborating domain experts confirmed an increased time efficiency and certainty for analysis and modification tasks on complex DNA structures. Our method thus offers exciting new opportunities with promising applications in medicine and biotechnology