Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's signal structure, characterized as the open-loop causal dependencies among manifest variables and represented by its dynamical structure function. Although this notion of structure is among the weakest available, previous work has shown that if no a priori structural information is known about the system, not even the Boolean structure of the dynamical structure function is identifiable. Consequently, one method previously suggested for obtaining the necessary a priori structural information is to leverage knowledge about target specificity of the controlled inputs. This work extends these results to demonstrate precisely the a priori structural information that is both necessary and sufficient to reconstruct the network from input-output data. This extension is important because it significantly broadens the applicability of the identifiability conditions, enabling the design of network reconstruction experiments that were previously impossible due to practical constraints on the types of actuation mechanisms available to the engineer or scientist. The work is motivated by the proteomics problem of reconstructing the Per-Arnt-Sim Kinase pathway used in the metabolism of sugars.

Video streaming on the Internet is increasingly using Dynamic Adaptive Streaming over HTTP (DASH), which allows a client to dynamically adjust its video quality by choosing the appropriate quality level for each segment based on the current download rate. In this paper we examine the impact of Scalable Video Coding (SVC) on the client's quality selection policy. Given a variable download rate, when should the client try to maximize the current segment's video quality, and when should it instead play it safe and ensure a minimum level of quality for future segments? We use a combination of analysis, dynamic programming, and simulation to show that a client should use a diagonal quality selection policy, which combines prefetching with backfilling to balance both of these concerns. We also illustrate the conditions that affect the slope of the diagonal policy.

No abstract

No abstract

No abstract