Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian Process (GP) combined with a Numerical Weather Prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a Censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the Mean Absolute Error (MAE), the proposed model provides around 4% to 11% improvement in forecasting accuracy compared to an Artificial Neural Network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

A visualization plot of a data set of molecular data is a useful tool for gaining insight into a set of molecules. In chemoinformatics, most visualization plots are of molecular descriptors, and the statistical model most often used to produce a visualization is principal component analysis (PCA). This paper takes PCA, together with four other statistical models (NeuroScale, GTM, LTM, and LTM-LIN), and evaluates their ability to produce clustering in visualizations not of molecular descriptors but of molecular fingerprints. Two different tasks are addressed: understanding structural information (particularly combinatorial libraries) and relating structure to activity. The quality of the visualizations is compared both subjectively (by visual inspection) and objectively (with global distance comparisons and local k-nearest-neighbor predictors). On the data sets used to evaluate clustering by structure, LTM is found to perform significantly better than the other models. In particular, the clusters in LTM visualization space are consistent with the relationships between the core scaffolds that define the combinatorial sublibraries. On the data sets used to evaluate clustering by activity, LTM again gives the best performance but by a smaller margin. The results of this paper demonstrate the value of using both a nonlinear projection map and a Bernoulli noise model for modeling binary data.

SummaryDifferences in lipid metabolism associate with age‐related disease development and lifespan. Inflammation is a common link between metabolic dysregulation and aging. Saturated fatty acids (FAs) initiate pro‐inflammatory signalling from many cells including monocytes; however, no existing studies have quantified age‐associated changes in individual FAs in relation to inflammatory phenotype. Therefore, we have determined the plasma concentrations of distinct FAs by gas chromatography in 26 healthy younger individuals (age < 30 years) and 21 healthy FA individuals (age > 50 years). Linear mixed models were used to explore the association between circulating FAs, age and cytokines. We showed that plasma saturated, poly‐ and mono‐unsaturated FAs increase with age. Circulating TNF‐α and IL‐6 concentrations increased with age, whereas IL‐10 and TGF‐β1 concentrations decreased. Oxidation of MitoSOX Red was higher in leucocytes from FA adults, and plasma oxidized glutathione concentrations were higher. There was significant colinearity between plasma saturated FAs, indicative of their metabolic relationships. Higher levels of the saturated FAs C18:0 and C24:0 were associated with lower TGF‐β1 concentrations, and higher C16:0 were associated with higher TNF‐α concentrations. We further examined effects of the aging FA profile on monocyte polarization and metabolism in THP1 monocytes. Monocytes preincubated with C16:0 increased secretion of pro‐inflammatory cytokines in response to phorbol myristate acetate‐induced differentiation through ceramide‐dependent inhibition of PPARγ activity. Conversely, C18:1 primed a pro‐resolving macrophage which was PPARγ dependent and ceramide dependent and which required oxidative phosphorylation. These data suggest that a midlife adult FA profile impairs the switch from proinflammatory to lower energy, requiring anti‐inflammatory macrophages through metabolic reprogramming.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed t o represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.