Abstract
In order to detect network motifs we need to evaluate the exceptionality of subgraphs in a given network. This is usually done by comparing subgraph frequencies on both the original and an ensemble of random networks keeping certain structural properties. The classical null model implies preserving the degree sequence. In this paper our focus is on a richer model that approximately fixes the frequency of subgraphs of size K - 1 to compute motifs of size K. We propose a method for generating random graphs under this model, and we provide algorithms for its efficient computation. We show empirical results of our proposed methodology on neurobiological networks, showcasing its efficiency and its differences when comparing to the traditional null model. © Springer International Publishing AG 2017.

Abstract
Concurrent C0 is an imperative programming language in the C family with session-typed messagepassing concurrency. The previously proposed semantics implements asynchronous (non-blocking) output; we extend it here with non-blocking input. A key idea is to postpone message reception as much as possible by interpreting receive commands as a request for a message. We implemented our ideas as a translation from a blocking intermediate language to a non-blocking language. Finally, we evaluated our techniques with several benchmark programs and show the results obtained. While the abstract measure of span always decreases (or remains unchanged), only a few of the examples reap a practical benefit.