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A neural network was applied to a large, structurally heterogeneous data set of mutagens and non-mutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network. The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set. By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some of these clusters were structurally homogeneous and contained known mutagenic and non-mutagenic structural classes. This analysis provided a useful means of demonstrating how the network was classifying the data.

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Researchers and educators in computer science and other domains are increasingly turning to distributed test beds that offer access to a variety of resources, including networking, computation, storage, sensing, and actuation. The provisioning of resources from their owners to interested experimenters requires establishing sufficient mutual trust between these parties. Building such trust directly between researchers and resource owners will not scale as the number of experimenters and resource owners grows. The NSF GENI (Global Environment for Network Innovation) project has focused on establishing scalable mechanisms for maintaining such trust based on common approaches for authentication, authorization and accountability. Such trust reflects the actual trust relationships and agreements among humans or real-world organizations. We describe here GENI's approaches for federated trust based on mutually trusted authorities, and implemented via cryptographically signed credentials and shared policies.