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William W. L. Cheung
Researcher at University of British Columbia
Publications - 449
Citations - 26928
William W. L. Cheung is an academic researcher from University of British Columbia. The author has contributed to research in topics: Climate change & Fisheries management. The author has an hindex of 67, co-authored 415 publications receiving 20469 citations. Previous affiliations of William W. L. Cheung include Hong Kong Baptist University & Centre for Environment, Fisheries and Aquaculture Science.
Papers
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Catches [of the Chinese distant-water fleet]
Daniel Pauly,Dyhia Belhabib,William W. L. Cheung,Andrés M. Cisneros-Montemayor,Sarah Harper,Vicky W. L. Lam,Yining Mai,Frédéric Le Manach,Ka Man Mok,Liesbeth van der Meer,Soohyun Shon,Wilf Swartz,U. Rashid Sumaila,R. Watson,Yunlei Zhai,Dirk Zeller +15 more
Meeting report Conservation physiology of marine fishes: advancing the predictive capacity of models
Christian Jorgensen,Myron A. Peck,Fabio Antognarelli,Ernesto Azzurro,Michael T. Burrows,William W. L. Cheung,Andrea Cucco,Rebecca E. Holt,Stefano Marras,David J. McKenzie,Julian D. Metcalfe,Angel Pérez-Ruzafa,Matteo Sinerchia,John F. Steffensen,Lorna R. Teal,Paolo Domenici +15 more
TL;DR: In this paper, the details added to affiliations 1,4,6,8 and 10 were checked and the publisher location for ref. [10] was updated.
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Mining from distributed and abstracted data
TL;DR: Existing techniques related to distributed data mining in abstraction‐based data mining are reviewed, open research challenges on mining tasks performed on distributed and abstracted data are discussed, and how global data models could be learnt based on local data models are described.
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Bioenergetic influence on the historical development and decline of industrial fisheries
Jérôme Guiet,Jérôme Guiet,Eric D. Galbraith,Eric D. Galbraith,Eric D. Galbraith,Daniele Bianchi,William W. L. Cheung +6 more
TL;DR: In this paper, the authors provide objective descriptions of these catch peaks, which generally progressed from high-to low-latitude LMEs, and attribute the temporal progression to a combination of economic and ecological factors.
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Context-Aware Time Series Imputation for Multi-Analyte Clinical Data
TL;DR: A novel framework based on a bidirectional LSTM in which patients' health states are explicitly captured by learning a "global context vector" from the entire clinical time series that obtains a normalized root mean square deviation (nRMSD) of 0.1998, which is 10.6% better than that of state-of-the-art models.