08+09S | 09W | 10S | 10W | 11S | 11W | 12S | 12W | 13S | 13W | 14S | 14W | 15S | 15W | 16S | 16W | 17S | 17W | 18S | 18W | 19S

Summer Term 2019, Doctoral School Events
2019-03-22 Doctoral School Seminar (Inst. Mathematik, Heinrichstr. 36, 15:00—16:00, KFU)
Sandra Marschke (KFU, advisor W. Ring): Modeling, identification, and optimization of violin bridges [show abstract]
Josef Strini (TU, advisor S. Thonhauser): On a dividend problem with random funding [show abstract]
2019-05-10 Doctoral School Seminar (Seminarraum 2 des Instituts für Geometrie, Kopernikusgasse 24, 9:30—12:00, TU)
Leonardo Alese (TU, advisor J. Wallner): Closing curves by rearranging arcs [show abstract]
Jana Fuchsberger (KFU, advisor G. Haase): Simulating a Heart Valve using a Varying Permeability Approach [show abstract]
Junseok Oh (KFU, advisor A. Geroldinger): On minimal product-one sequences of maximal length over dihedral and dicyclic groups. [show abstract]
2019-06-14 Doctoral School Seminar (Seminarraum 2 des Instituts für Geometrie, Kopernikusgasse 24, 10:30—11:30, TU)
Irene Parada (TU, advisor O. Aichholzer): On the complexity of extending drawings of graphs

Abstract: Given a drawing D(G) of a graph G, we study the problem of adding missing edges to it, such that certain properties of the drawing are not destroyed in the process. We first consider simple drawings and show that it is NP-hard to decide if we can add certain k edges. The maximization version of the problem (finding a maximum amount of edges that we can add from a given set of prospective edges) is also NP-hard and hard to approximate. Similar hardness results can be found for 1-plane drawings. For that class, the problem becomes easier (it is fixed-parameter tractable) if any missing edge can be added.

Thomas Kuenzer (TU, advisor S. Hörmann): Spatial PCA for functional random fields

Abstract: Functional spatial data is a subject of growing importance in statistics. The applications of such high-dimensional functional data on spatial grids range from climate data to hyperspectral imaging of the earth's surface. We propose a novel concept of functional principal components (FPCs) for spatial data. We consider second-order stationary functional data on a spatial grid of dimension r, where in each grid point a random element in L2([0,1]) can be observed. By making use of the information contained in the spatial dependence structure of a sample, our method of spatial functional principal components (SFPC) offers both a quantitative and qualitative improvement to the dimension reduction technique provided by the ordinary FPCs.