Establishing Processing Priorities: Recommendations from a 2017 Study of Practices in US Repositories

Building upon archival scholarship and previous solutions addressing backlog collections, this study seeks to identify a comprehensive, integrated, and effective strategy to establish and maintain processing priorities. This study is based on supporting research, which includes the results of a survey of archivists and the findings of five focus group discussions about processing priorities. Using these findings, the authors (a) consider whether this focus on an old problem has motivated archivists to find innovative solutions; (b) determine whether archivists are using these tools; (c) consider whether and how archivists have changed processing priority practices and policies; and (d) seek to clarify current metrics to establish overall processing priorities

XOR-Sampling for Network Design with Correlated Stochastic Events

Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent. In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated. We use Markov Random Fields to model the correlation and define a new stochastic network design framework. We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler. The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.Comment: In Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17). The first two authors contribute equall