Universally consistent vertex classification for latent positions graphs

In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function $\kappa$, provided that the latent positions are i.i.d. from some distribution F. We then consider the exploitation task of vertex classification where the link function $\kappa$ belongs to the class of universal kernels and class labels are observed for a number of vertices tending to infinity and that the remaining vertices are to be classified. We show that minimization of the empirical $\varphi$-risk for some convex surrogate $\varphi$ of 0-1 loss over a class of linear classifiers with increasing complexities yields a universally consistent classifier, that is, a classification rule with error converging to Bayes optimal for any distribution F.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1112 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Scalability of Hydrodynamic Simulations

Many hydrodynamic processes can be studied in a way that is scalable over a vastly relevant physical parameter space. We systematically examine this scalability, which has so far only briefly discussed in astrophysical literature. We show how the scalability is limited by various constraints imposed by physical processes and initial conditions. Using supernova remnants in different environments and evolutionary phases as application examples, we demonstrate the use of the scaling as a powerful tool to explore the interdependence among relevant parameters, based on a minimum set of simulations. In particular, we devise a scaling scheme that can be used to adaptively generate numerous seed remnants and plant them into 3D hydrodynamic simulations of the supernova-dominated interstellar medium.Comment: 12 pages, 1 figure, submitted to MNRAS; comments are welcom

A nonparametric two-sample hypothesis testing problem for random dot product graphs

We consider the problem of testing whether two finite-dimensional random dot product graphs have generating latent positions that are independently drawn from the same distribution, or distributions that are related via scaling or projection. We propose a test statistic that is a kernel-based function of the adjacency spectral embedding for each graph. We obtain a limiting distribution for our test statistic under the null and we show that our test procedure is consistent across a broad range of alternatives.Comment: 24 pages, 1 figure

Innovative spatial timber structures: workshops with physical modeling explorations from small to full scale

Architects and Engineers are educated and work within two separate cultures yet they are both concerned with conceptual structural design. The collaboration between the professions is especially important when designing buildings where the structure to a great degree forms the spaces, as in the cases of form generating structures such as gridshells, reciprocal frames, space trusses etc . This paper describes several specialist research based workshops developed at KA over the last two years that use physical modelling of 1:1 innovative timber load-bearing structures such as gridshells and reciprocal frames