Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes

We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system's dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.Comment: Proceedings of the 52th IEEE International Conference on Decision and Control (CDC), Firenze, Italy, December 201

Worship in small churches

The article advocates and elaborates a theological point of view with respect to worship that is in the interest of the small church and that the small church is in a unique position to advocate. Specific rubrical proposals are made. \u27The means of grace, i.e., the Word and Sacraments, are the central and only indispensable elements in worship. The smaller amount of human resources available to small churches can lead them to focus on these means of grace. This focus is an outstanding strength\u27

Feeding driet chicory root to pigs decrease androstenone accumulation in fat by increasing hepatic 3β hydroxysteroid dehydrogenase expression

The present study investigated the in vivo effect of chicory root on testicular steroid concentrations and androstenone metabolizing enzymes in entire male pigs. Furthermore, the effect on skatole and indole concentrations in plasma and adipose tissue was investigated. The pigs were divided into two groups; one receiving experimental feed containing 10% dried chicory root for 16 days before slaughter, the control group was fed a standard diet. Plasma, adipose and liver tissue samples were collected at slaughter. Plasma was analyzed for the concentration of testosterone, estradiol, insulin-like growth factor 1 (IGF-1), skatole and indole. Adipose tissue was analyzed for the concentration of androstenone, skatole and indole, while the liver tissue was analyzed for mRNA and protein expressions of 3β-hydroxysteroid dehydrogenase (3β-HSD), sulfotransferase 2A1 and heat-shock protein 70 (HSP70). The results showed that the androstenone concentrations in the adipose tissue of chicory fed pigs were significantly (p < 0.05) lower and indole concentrations were higher (p < 0.05) compared to control fed pigs. Moreover the chicory root fed pigs had increased mRNA and protein expression of 3β-HSD and decreased HSP70 expression (p < 0.05). Testosterone and IGF-1 concentrations in plasma as well as skatole concentrations in adipose tissue were not altered by dietary intake of chicory root. It is concluded that chicory root in the dietreduces the concentration of androstenone in adipose tissue via induction of 3�-HSD, and that these changes were not due to increased cellular stress

Derivative observations in Gaussian Process models of dynamic systems

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1)It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training size - traditionally a problem for Gaussian process models

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.The research leading to these results has received funding from the EC’s Seventh Framework Programme (FP7/2007-2013) under grant agreement #270327, ONR MURI grant N00014-09-1-1052, Intel Labs, and the Department of Computing, Imperial College London.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TPAMI.2013.21