Titlebook: Ways of Regulating Drugs in the 19th and 20th Centuries; Jean-Paul Gaudillière (Director),Volker Hess (Chai Book 2013 Palgrave Macmillan,

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Harry M. Markst concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.978-3-031-00639-5978-3-031-01767-4Series ISSN 1935-3235 Series E-ISSN 1935-3243

adroit

Jeremy A. Greeneication of the various types of profilable metadata, discusses popular data profiling tasks,and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relati

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小淡水魚

Alberto Cambrosio,Peter Keating,Andrei Mogoutovs); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpi?t?enko., .Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dsti

DEMUR

Harry M. Marks and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the rea

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Ilana L?wyly few well-characterized samples (between ten and in the hundreds) are available. Hence, it is important to consider approaches that we can tailor and use for small data sets. The relevant question then becomes how we can effectively learn from existing, limited data to guide the next experiments i

grandiose

Viviane Quirke tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have e

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Christian Bonahty gain due to subsequent noise reduction. To circumvent this problem, the method described in this chapter uses univariate microaggregation instead of its multivariate counterpart. As a result, utility gains with respect to standard differential privacy are obtained regardless of the number of reco

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