Declarative Transport: No More Transport Protocols to Design, Only Policies to Specify

Transport protocols are an integral part of the inter-process communication (IPC) service used by application processes to communicate over the network infrastructure. With almost 30 years of research on transport, one would have hoped that we have a good handle on the problem. Unfortunately, that is not true. As the Internet continues to grow, new network technologies and new applications continue to emerge putting transport protocols in a never-ending flux as they are continuously adapted for these new environments. In this work, we propose a clean-slate transport architecture that renders all possible transport solutions as simply combinations of policies instantiated on a single common structure. We identify a minimal set of mechanisms that once instantiated with the appropriate policies allows any transport solution to be realized. Given our proposed architecture, we contend that there are no more transport protocols to design—only policies to specify. We implement our transport architecture in a declarative language, Network Datalog (NDlog), making the specification of different transport policies easy, compact, reusable, dynamically configurable and potentially verifiable. In NDlog, transport state is represented as database relations, state is updated/queried using database operations, and transport policies are specified using declarative rules. We identify limitations with NDlog that could potentially threaten the correctness of our specification. We propose several language extensions to NDlog that would significantly improve the programmability of transport policies.

Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process. Hence multiple solutions exist, and it is desirable to restrict the solution space to a smaller subset of feasible solutions. For example, not all human body poses are feasible due to anthropometric constraints. Since the space of feasible solutions may not admit a closed form description, the proposed framework seeks to exploit machine learning techniques to learn an approximation that is smoothly parameterized over such a space. One such technique is Gaussian Process Latent Variable Modelling. Scaled conjugate gradient is then used find the best matching pose in the space of feasible solutions when given an input image. The formulation allows easy incorporation of various constraints, e.g. temporal consistency and anthropometric constraints. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The estimation accuracy of the Specialized Mapping Architecture is at least one standard deviation worse than the proposed approach in the experiments with synthetic data. In experiments with real video of humans performing gestures, the proposed approach produces qualitatively better estimation results.