Partial Differential Equations are infinite dimensional encoded representations of physical processes. However, imbibing multiple observation data towards a coupled representation presents significant challenges. We present a fully convolutional architecture that captures the invariant structure of the domain to reconstruct the observable system. The proposed architecture is significantly low-weight compared to other networks for such problems. Our intent is to learn coupled dynamic processes interpreted as deviations from true kernels representing isolated processes for model-adaptivity. Experimental analysis shows that our architecture is robust and transparent in capturing process kernels and system anomalies. We also show that high weights representation is not only redundant but also impacts network interpretability. Our design is guided by domain knowledge, with isolated process representations serving as ground truths for verification. These allow us to identify redundant kernels and their manifestations in activation maps to guide better designs that are both interpretable and explainable unlike traditional deep-nets.

The first AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2013) was be held November 6-9, 2013 in Palm Springs, California. Three workshops took place on Saturday, November 9th: Crowdsourcing at Scale (full day), Human and Machine Learning in Games (full day) and Scaling Speech, Language Understanding and Dialogue through Crowdsourcing (half day).

The aim of the Disco: Human and Machine Learning in Games workshop was to extend upon the focus of two past workshops and explore the intersection of entertainment, learning and human computation. The goal of the workshop was to examine both human learning and machine learning in games and human computation. Human computation methods let machines learn from humans where games can provide humans the opportunity to learn. The workshop was thus devoted to I learn, in Latin disco, for machines and humans alike. The First AAAI Conference on Human Computation and Crowdsourcing Was Held in the Southern California Desert Community of Palm Springs.