The Digital Assets Repository (DAR) is an Institutional Repository developed at the Bibliotheca Alexandrina to manage the full lifecycle of a digital asset: its creation and ingestion, its metadata management, storage and archival in addition to the necessary mechanisms for publishing and dissemination. DAR was designed with a focus on integrating DAR with different sources of digital objects and metadata in addition to integration with applications built on top of the repository. As a modern repository, the system architecture demonstrates a modular design relying on components that are best of the breed, a flexible content model for digital objects based on current standards and heavily relying on RDF triples to define relations. In this paper we will demonstrate the building blocks of DAR as an example of a modern repository, discussing how the system addresses the challenges that face an institution in consolidating its assets and a focus on solving scalability issues.

Snack food and wholesale bakery manufacturers in the market for dividers, depositors and rounders have a long list of considerations. These include automation, reliability, scalability, integration, flexibility, communication, customization, ease of maintenance and sanitation, and different packaging capabilities, according to companies that produce such machines.

This paper considers a Bayesian approach to graph-based semi-supervised learning. We show that if the graph parameters are suitably scaled, the graph-posteriors converge to a continuum limit as the size of the unlabeled data set grows. This consistency result has profound algorithmic implications: we prove that when consistency holds, carefully designed Markov chain Monte Carlo algorithms have a uniform spectral gap, independent of the number of unlabeled inputs. Numerical experiments illustrate and complement the theory.

Abbott, Martin L

Secaucus : Springer, 2006

Barker Library - TK3105.V37 2019