New Generation Discovery: A Systematic View for Its Development, Issues and Future
KAUST DepartmentUniversity Library
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AbstractCollecting, storing, discovering, and locating are integral parts of the composition of the library. To fully utilize the library and achieve its ultimate value, the construction and production of discovery has always been a central part of the library’s practice and identity. That is the reason why the new generation (also called the next-generation discovery) discovery gets such striking effect since it came into library automation arena. However, when we talk about the new generation of discovery in the library domain, we should see it in the entirety of the library as one of its organic parts and consider its progress along with the evolution of the whole library world. We should have a deeper understanding about its relationship and interaction with the internet, the rapidly changing digital environment, and the elements and the chain of library services. To address above issues, this paper overviews the different versions of the definition for the new generation discovery by combining our own understanding. The paper also gives our own description for its properties and characteristics. The paper points out what challenges, which extends the technology domain to commercial interests and business strategy, are faced by the discovery applications, and how library and library professionals deal with those challenges. Finally, the paper elaborates on the promise brought by the new discovery development and what the next exploration might be for its future.
DescriptionThis paper is presented for the international conference: “Change and Challenge: Redefine the Future of Academic Libraries” on November 4-5th, 2012 in Beijing.
JournalSSRN Electronic Journal
Conference/Event nameChange and Challenge: Redefine the Future of Academic Libraries
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Controlling the local false discovery rate in the adaptive LassoSampson, J. N.; Chatterjee, N.; Carroll, R. J.; Muller, S. (Oxford University Press (OUP), 2013-04-09)The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn-δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.