A dependent partition-valued process for multitask clustering and time evolving network modelling

The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes, but these, being Markov processes, are restricted to linear or tree structured covariate spaces. We define a partition-valued process on an arbitrary covariate space using Gaussian processes. We use the process to construct a multitask clustering model which partitions datapoints in a similar way across multiple data sources, and a time series model of network data which allows cluster assignments to vary over time. We describe sampling algorithms for inference and apply our method to defining cancer subtypes based on different types of cellular characteristics, finding regulatory modules from gene expression data from multiple human populations, and discovering time varying community structure in a social network.

The effect of dislocations on the efficiency of InGaN/GaN solar cells

Two solar cells based on an InGaN/GaN p-i-n hetero-junction, but having different dislocation densities, were fabricated and characterized. The structures were grown on c-plane (0001) GaN-on-sapphire templates with different threading dislocation (TD) densities of 5×108 and 5×109 cm-2. Structural characterization revealed the presence of V-defects in the InGaN epilayer. Since each V-defect was associated with a TD, the structural as well as the optical properties worsened with a higher TD density in the GaN/sapphire template. It was also found that additional dislocations were generated in the p-GaN layer over the V-defects in the InGaN layer. Because of its superior structural quality, the peak external quantum efficiency (EQE) of the low TD density sample was three times higher than that of the high TD density sample. © 2013 Elsevier B.V.