A new approach to the assessment of stochastic errors of radio source position catalogues

Assessing the external stochastic errors of radio source position catalogues derived from VLBI observations is important for tasks such as estimating the quality of the catalogues and their weighting during combination. One of the widely used methods to estimate these errors is the three-cornered-hat technique, which can be extended to the N-cornered-hat technique. A critical point of this method is how to properly account for the correlations between the compared catalogues. We present a new approach to solving this problem that is suitable for simultaneous investigations of several catalogues. To compute the correlation between two catalogues $A$ and $B$, the differences between these catalogues and a third arbitrary catalogue $C$ are computed. Then the correlation between these differences is considered as an estimate of the correlation between catalogues $A$ and $B$. The average value of these estimates over all catalogues $C$ is taken as a final estimate of the target correlation. In this way, an exhaustive search of all possible combinations allows one to compute the paired correlations between all catalogues. As an additional refinement of the method, we introduce the concept of weighted correlation coefficient. This technique was applied to nine recently published radio source position catalogues. We found large systematic differences between catalogues, that significantly impact determination of their stochastic errors. Finally, we estimated the stochastic errors of the nine catalogues

Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators

In this paper we present some preliminary work towards the development of a new evolutionary subsampling technique for solving the non-intrusive load monitoring (NILM) problem. The NILM problem concerns using predictive algorithms to analyse whole-house energy usage measurements, so that individual appliance energy usages can be disaggregated. The motivation is to educate home owners about their energy usage. However, by their very nature, the datasets used in this research are massively imbalanced in their target value distributions. Consequently standard machine learning techniques, which often rely on optimising for root mean squared error (RMSE), typically fail. We therefore propose the target-weighted RMSE (TW-RMSE) metric as an alternative fitness function for optimising load disaggregators, and show in a simple initial study in which random search is utilised that TW-RMSE is a metric that can be optimised, and therefore has the potential to be included in a larger evolutionary subsampling-based solution to this problem