Learning flexible representations of stochastic processes on graphs

Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks. We propose a parameterization of such linear operations using functional calculus to achieve arbitrarily low learning complexity. The proposed approach is shown to model richer behaviors and display greater flexibility in learning representations than product graph methods

CAN PEOPLE DISTINGUISH PÂTÉ FROM DOG FOOD?

Considering the similarity of its ingredients, canned dog food could be a suitable and inexpensive substitute for pâté or processed blended meat products such as Spam or liverwurst. However, the social stigma associated with the human consumption of pet food makes an unbiased comparison challenging. To prevent bias, Newman's Own dog food was prepared with a food processor to have the texture and appearance of a liver mousse. In a double-blind test, subjects were presented with five unlabeled blended meat products, one of which was the prepared dog food. After ranking the samples on the basis of taste, subjects were challenged to identify which of the five was dog food. Although 72% of subjects ranked the dog food as the worst of the five samples in terms of taste (Newell and MacFarlane multiple comparison, P<0.05), subjects were not better than random at correctly identifying the dog food.Consumer/Household Economics, Demand and Price Analysis,

Beginnings

1998 English Department Creative Writing Contest Winne