531 research outputs found

    Unprecedented Alexandrium blooms in a previously low biotoxin risk area of Tasmania, Australia.

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    During October 2012, a shipment of blue mussels (Mytilus galloprovincialis) from the poorly monitored east coast of Tasmania, Australia, was tested by Japanese import authorities and found to be contaminated with unacceptable levels of Paralytic Shellfish Toxins (PSTs; 10 mg/kg). Subsequently local oysters, scallops, clams, the viscera of abalone and rock lobsters were also found to be contaminated. This led to a global product recall and loss to the local economy of AUD 23M. Following low toxicity during 2013 and 2014 and implementation of minimal shellfish farm closures, a more severe bloom event occurred during July-November 2015 and again June-September 2016 (up to 300,000 Alexandrium cells/L; 24 mg/kg PST in mussels, 6 mg/kg in Crassostrea gigas oysters), also causing 4 human illnesses resulting in hospitalization after consumption of wild shellfish. While Alexandrium tamarense had been detected in low concentrations in southeastern Australia since 1987, all cultured strains belonged to the mostly non-toxic group 5 (now designated A. australiense; detected since 1987) and weakly toxic group 4 (A. pacificum; detected in 1997). In contrast, the 2012 to 2016 outbreaks were dominated by highly toxic group 1 (A. fundyense) never detected previously in the Australian region. Molecular analyses suggest that A. fundyense may have been a cryptic ribotype previously present in Tasmania, but newly stimulated by altered water column stratification conditions driven by changing rainfall and temperature patterns. Increased seafood and plankton monitoring of the area now include the implementation of Alexandrium qPCR, routine Neogenℱ immunological and HPLC PST tests, but ultimately may also drive change in harvesting strategies and aquaculture species selection by the local seafood industry

    Antineutrinos from Earth: A reference model and its uncertainties

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    We predict geoneutrino fluxes in a reference model based on a detailed description of Earth's crust and mantle and using the best available information on the abundances of uranium, thorium, and potassium inside Earth's layers. We estimate the uncertainties of fluxes corresponding to the uncertainties of the element abundances. In addition to distance integrated fluxes, we also provide the differential fluxes as a function of distance from several sites of experimental interest. Event yields at several locations are estimated and their dependence on the neutrino oscillation parameters is discussed. At Kamioka we predict N(U+Th)=35 +- 6 events for 10^{32} proton yr and 100% efficiency assuming sin^2(2theta)=0.863 and delta m^2 = 7.3 X 10^{-5} eV^2. The maximal prediction is 55 events, obtained in a model with fully radiogenic production of the terrestrial heat flow.Comment: 24 pages, ReVTeX4, plus 7 postscript figures; minor formal changes to match version to be published in PR

    Database Learning: Toward a Database that Becomes Smarter Every Time

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    In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM SIGMOD conference 201

    VerdictDB: Universalizing Approximate Query Processing

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    Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171×\times speedup (18.45×\times on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201
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