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Sökning: WFRF:(Schwing Alex)

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1.
  • Huerta, E. A., et al. (författare)
  • Enabling real-time multi-messenger astrophysics discoveries with deep learning
  • 2019
  • Ingår i: Nature reviews physics. - : Springer Science and Business Media LLC. - 2522-5820. ; 1:10, s. 600-608
  • Forskningsöversikt (refereegranskat)abstract
    • Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics. A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.
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2.
  • Lambert, Megan L., et al. (författare)
  • Function and flexibility of object exploration in kea and new caledonian crows
  • 2017
  • Ingår i: Royal Society Open Science. - : The Royal Society. - 2054-5703. ; 4:9
  • Tidskriftsartikel (refereegranskat)abstract
    • A range of non-human animals frequently manipulate and explore objects in their environment, which may enable them to learn about physical properties and potentially form more abstract concepts of properties such as weight and rigidity. Whether animals can apply the information learned during their exploration to solve novel problems, however, and whether they actually change their exploratory behavior to seek functional information about objects have not been fully explored. We allowed kea (Nestor notabilis) and New Caledonian crows (Corvus moneduloides) to explore sets of novel objects both before and after encountering a task in which some of the objects could function as tools. Following this, subjects were given test trials in which they could choose among the objects they had explored to solve a tool-use task. Several individuals from both species performed above chance on these test trials, and only did so after exploring the objects, compared with a control experiment with no prior exploration phase. These results suggest that selection of functional tools may be guided by information acquired during exploration. Neither kea nor crows changed the duration or quality of their exploration after learning that the objects had a functional relevance, suggesting that birds do not adjust their behavior to explicitly seek this information.
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