Hiding Images within Images.

We present a system to hide a full color image inside another of the same size with minimal quality loss to either image. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources.

Acceleration of the effect of solute on the entropy-volume cross fluctuation density in aqueous 2-butoxyethanol, 1-propanol, and glycerol: the fourth derivative of Gibbs energy.

Using a differential pressure perturbation calorimetry developed by us recently [K. Yoshida, S. Baluja, A.

VisualRank: applying PageRank to large-scale image search.

Because of the relative ease in understanding and processing text, commercial image-search systems often rely on techniques that are largely indistinguishable from text-search. Recently, academic studies have demonstrated the effectiveness of employing image-based features to provide alternative or additional signals. However, it remains uncertain whether such techniques will generalize to a large number of popular web queries, and whether the potential improvement to search quality warrants the additional computational cost.

Evolution of an artificial neural network based autonomous land vehicle controller.

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail.