From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

Visual Event Cueing in Linked Spatiotemporal Data

abstract: The media disperses a large amount of information daily pertaining to political events social movements, and societal conflicts. Media pertaining to these topics, no matter the format of publication used, are framed a particular way. Framing is used not for just guiding audiences to desired beliefs, but also to fuel societal change or legitimize/delegitimize social movements. For this reason, tools that can help to clarify when changes in social discourse occur and identify their causes are of great use. This thesis presents a visual analytics framework that allows for the exploration and visualization of changes that occur in social climate with respect to space and time. Focusing on the links between data from the Armed Conflict Location and Event Data Project (ACLED) and a streaming RSS news data set, users can be cued into interesting events enabling them to form and explore hypothesis. This visual analytics framework also focuses on improving intervention detection, allowing users to hypothesize about correlations between events and happiness levels, and supports collaborative analysis.Dissertation/ThesisMasters Thesis Computer Science 201

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Dataremix: Aesthetic Experiences of Big Data and Data Abstraction

This PhD by published work expands on the contribution to knowledge in two recent large-scale transdisciplinary artistic research projects: ATLAS in silico and INSTRUMENT | One Antarctic Night and their exhibited and published outputs. The thesis reflects upon this practice-based artistic research that interrogates data abstraction: the digitization, datafication and abstraction of culture and nature, as vast and abstract digital data. The research is situated in digital arts practices that engage a combination of big (scientific) data as artistic material, embodied interaction in virtual environments, and poetic recombination. A transdisciplinary and collaborative artistic practice, x-resonance, provides a framework for the hybrid processes, outcomes, and contributions to knowledge from the research. These are purposefully and productively situated at the objective | subjective interface, have potential to convey multiple meanings simultaneously to a variety of audiences and resist disciplinary definition. In the course of the research, a novel methodology emerges, dataremix, which is employed and iteratively evolved through artistic practice to address the research questions: 1) How can a visceral and poetic experience of data abstraction be created? and 2) How would one go about generating an artistically-informed (scientific) discovery? Several interconnected contributions to knowledge arise through the first research question: creation of representational elements for artistic visualization of big (scientific) data that includes four new forms (genomic calligraphy, algorithmic objects as natural specimens, scalable auditory data signatures, and signal objects); an aesthetic of slowness that contributes an extension to the operative forces in Jevbratt’s inverted sublime of looking down and in to also include looking fast and slow; an extension of Corby’s objective and subjective image consisting of “informational and aesthetic components” to novel virtual environments created from big 3 (scientific) data that extend Davies’ poetic virtual spatiality to poetic objective | subjective generative virtual spaces; and an extension of Seaman’s embodied interactive recombinant poetics through embodied interaction in virtual environments as a recapitulation of scientific (objective) and algorithmic processes through aesthetic (subjective) physical gestures. These contributions holistically combine in the artworks ATLAS in silico and INSTRUMENT | One Antarctic Night to create visceral poetic experiences of big data abstraction. Contributions to knowledge from the first research question develop artworks that are visceral and poetic experiences of data abstraction, and which manifest the objective | subjective through art. Contributions to knowledge from the second research question occur through the process of the artworks functioning as experimental systems in which experiments using analytical tools from the scientific domain are enacted within the process of creation of the artwork. The results are “returned” into the artwork. These contributions are: elucidating differences in DNA helix bending and curvature along regions of gene sequences specified as either introns or exons, revealing nuanced differences in BLAST results in relation to genomics sequence metadata, and cross-correlation of astronomical data to identify putative variable signals from astronomical objects for further scientific evaluation

Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects

Final report TransForum WP-046 : images of sustainable development of Dutch agriculture and green space

In the project “Images of sustainable development of Dutch agriculture and green space” three PhD candidates studied the topic of images in sustainable development. Frans Hermans focused on the topic of societal images and their role and influence in innovation projects. The title of his subproject was “Social learning for sustainability in dynamic agricultural innovation networks.” Joost Vervoort explored the topic of “visualisation”, that is, using and producing images for specific purposes, in the context of innovation projects and programmes, in a subproject called “Step into the system: interactive media strategies for the exchange of insights on social-ecological change.” Finally, Dirk van Apeldoorn took a complex adaptive systems approach to images. He modelled various agro-ecosystems to compare images of those systems with the behaviour of those systems. His subproject was called “Modeling resilience of agro-ecosystems.