We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

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The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem [52] faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort ([28]) proposed SC-Share, a performancedriven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run -is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature?. In this paper, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process [11,10] in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.