Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties.

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large data sets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is often difficult or impossible to achieve, as prior knowledge may be unavailable. Ordinarily representative selection of training molecules from such data sets is achieved through random sampling.

The contribution of active body movement to visual development in evolutionary robots.

Inspired by the pioneering work by Held and Hein (1963) on the development of kitten visuo-motor systems, we explore the role of active body movement in the developmental process of the visual system by using robots. The receptive fields in an evolved mobile robot are developed during active or passive movement with a Hebbian learning rule. In accordance to experimental observations in kittens, we show that the receptive fields and behavior of the robot developed under active condition significantly differ from those developed under passive condition.

Characterization of the corrinoid iron-sulfur protein tetrachloroethene reductive dehalogenase of Dehalobacter restrictus.

The membrane-bound tetrachloroethene reductive dehalogenase (PCE-RDase) (PceA; EC 1.97.1.