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Sökning: WFRF:(Sala Alessandra)

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  • Atzmueller, Martin, et al. (författare)
  • International Workshop on Mining Attributed Networks (MATNET 2018) Chairs' Welcome
  • 2018
  • Ingår i: WWW '18. - New York, New York, USA : Association for Computing Machinery (ACM). - 9781450356404 ; , s. 1231-1233
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Attributed network models have seen an increasing success in recent years, thanks to their informative power and to their ability to model complex networked relations that characterize most real-world phenomena. Their use has been attractive to communities in different disciplines such as computer science, physics, social science, as well as in interdisciplinary research environments. The use of such models has been also supported by the increasing easiness in collecting multirelational data from the Web, e.g., from online social media platforms, crowdsourced data, online knowledge bases; within this view, the World Wide Web is an inestimable source of information, which can be conveniently represented with feature-rich network models, e.g., enclosing temporal aspects of the data, quantitative and/or qualitative properties of nodes, different relations between a common set of entities, different existence probabilities, or modeling connection between different entity types.
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  • Kourousias, George, et al. (författare)
  • Compressive Sensing for Dynamic XRF Scanning
  • 2020
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • X-Ray Fluorescence (XRF) scanning is a widespread technique of high importance and impact since it provides chemical composition maps crucial for several scientific investigations. There are continuous requirements for larger, faster and highly resolved acquisitions in order to study complex structures. Among the scientific applications that benefit from it, some of them, such as wide scale brain imaging, are prohibitively difficult due to time constraints. However, typically the overall XRF imaging performance is improving through technological progress on XRF detectors and X-ray sources. This paper suggests an additional approach where XRF scanning is performed in a sparse way by skipping specific points or by varying dynamically acquisition time or other scan settings in a conditional manner. This paves the way for Compressive Sensing in XRF scans where data are acquired in a reduced manner allowing for challenging experiments, currently not feasible with the traditional scanning strategies. A series of different compressive sensing strategies for dynamic scans are presented here. A proof of principle experiment was performed at the TwinMic beamline of Elettra synchrotron. The outcome demonstrates the potential of Compressive Sensing for dynamic scans, suggesting its use in challenging scientific experiments while proposing a technical solution for beamline acquisition software.
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