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Title: Non-Adaptive Adaptive Sampling on Turnstile Streams
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National Science Foundation
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  1. A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS.
  2. We generalize the local-feature size definition of adaptive sampling used in surface reconstruction to relate it to an alternative metric on Euclidean space. In the new metric, adaptive samples become uniform samples, making it simpler both to give adaptive sampling versions of homological inference results and to prove topological guarantees using the critical points theory of distance functions. This ultimately leads to an algorithm for homology inference from samples whose spacing depends on their distance to a discrete representation of the complement space.