The vision of network-centric operations is to increase operational capabilities through networked collaboration. NATO and its member nations state this vision in strategic documents at a very high level of abstraction. While suitable for giving an overall feel, current documentation renders the steps toward implementing those visions largely unsupported. We outline a method, based on agile requirements engineering, for converting high-level strategic visions into capabilities, whose form lend themselves to incremental implementation. We illustrate the use of this method in two cases that concern both operational capabilities and technical capabilities. We also  show how  the method enables one to prioritize  which capabilities to develop first. We conclude that some form of explicit methodology to span the gap between strategic visions and implementing those visions is necessary.

Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.