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As proposed by the editors, we give statements to two of the questions formulated above, briefly sketch some basics of a role-based approach to socio-technical innovation and give some examples, as to why a role-based approach might be helpful for observing and contributing to the field of Social Robotics.

The extraction and display of iso-surfaces is a standard method for the visualization of volume data sets. In this paper we present a novel approach that utilizes a hierarchy on both the input and the output data. For the generation of a coarse base mesh, we construct a hierarchy of volumes and extract an iso-surface from the coarsest resolution with a standard Marching Cubes algorithm. We additionally apply a simple mesh decimation algorithm to improve the shape of the triangles. We iteratively fit this mesh to the iso-surface at the finer volume levels. To be able to reconstruct fine detail of the iso-surface we thereby adaptively subdivide the mesh. To evenly distribute the vertices of the triangle mesh over the iso-surface and generate a triangle mesh with evenly shaped triangles, we have integrated a mesh smoothing algorithm into the fitting process. The advantage of this approach is that it generates a mesh with subdivision connectivity which can be utilized by several multiresolution algorithms such as compression and progressive transmission. As applications of our method we show how to reconstruct the surface of archeological artifacts and the reconstruction of the brain surface for the simulation of the brain shift phenomenon.

Background: Diffusion tensor imaging provides information about the location of white matter tracts within the human brain. For neurosurgery, this imaging technique is of major interest in order to minimize the risk of postoperative neurological deficits. In preoperative planning, fiber tracking algorithms based on streamline propagation are used in order to reconstruct major fiber tracts. The resulting streamline bundles approximate the course of the underlying white matter structures and indicate their shape and location in 3 dimensions as well as the spatial relation with respect to surrounding anatomy. However, for intraoperative application in combination with the neuronavigation system, these streamline representations are not adequate. Hulls encompassing the streamline bundles are necessary, since the boundary curves of hulls can be superimposed on the operating room (OR) microscope view for guidance in neurosurgery. Methods: In this work, we present a novel hull approach which is based on rasterization and isosurface extraction, combined with surface filtering techniques. The advantages of this approach are its robustness and the possibility to control the tightness of wrapping. Results: The approach makes it possible to generate precise hulls for different tract systems, which can be used as a basis for intraoperative visualization in the OR microscope. Distance measurements further confirm the accuracy of the hulls.

Diffusion tensor imaging provides information about structure and location of white matter tracts within the human brain which is of particular interest for neurosurgery. The reconstruction of neuronal structures from diffusion tensor data is commonly solved by tracking algorithms based on streamline propagation. These approaches generate streamline bundles that approximate the course of neuronal fibers. For medical application, a 3D representation of streamline bundles provides valuable information for pre-operative planning. However, for intra-operative visualization, surfaces wrapping eloquent structures are required for integration into the OR microscope. In order to provide hulls tightly encompassing the neuronal structures obtained from fiber tracking, we propose an approach based on tetrahedralization. This technique reuses the sampling points derived from fiber tracking and therefore provides precise hulls which serve as basis for intra-operative visualization.

Recently, viewpoint resolution methods which make conflicts productive have gained popularity in requirements engineering for organizational information systems. However, when extending such methods beyond organizational boundaries to social networks, sociological research indicates that a delicate balance of trust in individuals, confidence in the network as a whole, and watchful distrust becomes a key success factor. We capture these relationships in the so-called TCD (Trust-Confidence-Distrust) approach and demonstrate how this approach can be supported by a dynamic requirements engineering environment that combines the structural analysis of strategic dependencies and rationales, with the interaction between planning, tracing, and communicative action. An example drawn from an ongoing case study in entrepreneurship networks illustrates our approach.