Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

A social learning model of conflict and cooperation in human societies

Patterns of conflict and cooperation both within and between societies may be related to the degree of cultural similarity within and between the same societies. A simple model of social learning is used to predict patterns of conflict and cooperation in hypothetical societies that differ in the roles of relatives and nonrelatives in the enculturation of children. The model is illustrated by comparing its predictions to known differences in the patterns of conflict between males inpatrilocal and matrilocal societies.     

The role of context, colour and location cues in socially learned novel food source preferences in starlings, Sternus vulgaris

Although the opportunity for errors in social learning is widely recognised, as yet little research has been directed towards understanding specific inaccuracies, biases and limitations in social learning and the mechanisms that give rise to them. In two experiments I ask how starlings, Sternus vulgaris, identify exemplars of novel feeders previously learned about socially. I find that starlings have a stronger response to feeders in the same context as that in which social learning took place, compared to identical and nonidentical feeders in a different context. Within a context that matches where social learning took place, starlings prefer feeders that show the same location and colour as the feeder demonstrated by the demonstrator starling, and show no preference when colour and location cues are dissociated. This suggests that starlings are relatively accurate social learners, since they show strong responses to novel foraging options only if they match the context, colour and location of options learned about socially, and they do so after very few trials. Furthermore, the responses of the subjects were compatible with conditioned learning-like mechanisms, which provide a useful basis for the further investigation of the origins and implications of errors in social learning.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.