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This paper reviews water policy responses to drought in Australia, focusing on the Murray-Darling Basin (MDB) during the two decades from 1997. This period, which includes the decade long Millennium drought, brought a much sharper focus to discussions of scarcity and value of water. The drought initially focused attention on rising salinity and environmental water availability, as action on both was supported by strong science, and resonated politically. The drought became a crisis in 2006. Short-term planning focused on ensuring communities did not run out of water. For the longer term, the national government responded by announcing a major package of reform measures addressing sustainability and underlying scarcity, and recognising climate change. The package strengthened MDB water market infrastructure, upgraded water resource planning and the ability of irrigators to manage their water assets more flexibly, established new sustainable diversion limits and provided funding to ensure the environment received a larger share of basin water resources. But its completeness as a package can be attributed not only to the severity of drought, but also to political leadership, a disrupting strategy in the form of national legislation and a strong national budget that provided financial resources. The drought provided a crisis, but other ingredients were necessary to ensure effective action. [ABSTRACT FROM AUTHOR]

The article discusses sleep deprivation in children. The author argues that there is little evidence to support a link between sleep deprivation and child development, despite the anxiety of parents and teachers that children are not getting enough sleep. Topics include an historical overview of various studies investigating the detrimental effects of sleep deprivation in children, the link between sleep deprivation and obesity, and learning difficulties in sleep-deprived children.

Abstract: Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top- outlier images, and the images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images. [Copyright &y& Elsevier]