Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron.

Multilayer perceptron is composed of massive distributed neural processors interconnected. The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information.

Temporal event searches based on event maps and relationships.

To satisfy a user's need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e.

Anchor Link Prediction across Attributed Networks via Network Embedding.

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g.

On the Effectiveness of Least Squares Generative Adversarial Networks.

Unsupervised learning with generative adversarial networks (GANs) has proven to be hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process.