Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data

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dc.contributor.authorZhao, Henan
dc.contributor.authorChen, Jian
dc.date.accessioned2019-10-25T16:06:52Z
dc.date.available2019-10-25T16:06:52Z
dc.date.issued2019-05-06
dc.description.abstractWe present study results from two experiments to empirically validate that separable bivariate pairs for univariate representations of large-magnitude-range vectors are more efficient than integral pairs. The first experiment with 20 participants compared: one integral pair, three separable pairs, and one redundant pair, which is a mix of the integral and separable features. Participants performed three local tasks requiring reading numerical values, estimating ratio, and comparing two points. The second 18-participant study compared three separable pairs using three global tasks when participants must look at the entire field to get an answer: find a specific target in 20 seconds, find the maximum magnitude in 20 seconds, and estimate the total number of vector exponents within 2 seconds. Our results also reveal the following: separable pairs led to the most accurate answers and the shortest task execution time, while integral dimensions were among the least accurate; it achieved high performance only when a pop-out separable feature (here color) was added. To reconcile this finding with the existing literature, our second experiment suggests that the higher the separability, the higher the accuracy; the reason is probably that the emergent global scene created by the separable pairs reduces the subsequent search space.en_US
dc.description.sponsorshipThe work is supported in part by NSF IIS-1302755, NSF CNS-1531491, and NIST-70NANB13H181. The user study was funded by NSF grants with the OSU IRB approval number 2018B0080. Non-User Study design work was supported by grant from NIST-70NANB13H181.en_US
dc.description.urihttps://arxiv.org/abs/1905.02586en_US
dc.format.extent15 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2mr8c-yjcv
dc.identifier.citationHenan Zhao and Jian Chen; Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data 2019; Cite as:arXiv:1905.02586v1 [cs.GR]en_US
dc.identifier.urihttp://hdl.handle.net/11603/15976
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectSeparable and integral dimension pairsen_US
dc.subjectbivariate glyphen_US
dc.subject3D glyphen_US
dc.subjectquantitative visualizationen_US
dc.subjectlarge-magnitude-rangeen_US
dc.titlePicturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Dataen_US
dc.typeTexten_US

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