112 research outputs found
Nonlinear Frechet derivative and its De Wolf approximation
We introduce and derive the nonlinear Frechet derivative for the acoustic wave equation. It turns out that the high order Frechet derivatives can be realized by consecutive applications of the scattering operator and a zero-order
propagator to the source. We prove that the higher order Frechet derivatives are not negligible and the linear Frechet derivative may not be appropriate in many cases, especially when forward scattering is involved for large scale
perturbations. Then we derive the De Wolf approximation (multiple forescattering and single backscattering approximation) for the nonlinear Frechet derivative. We
split the linear derivative operator (i.e. the scattering operator) onto forward and backward derivatives, and then reorder and renormalize the nonlinear derivative series before making the approximation by dropping the multiple backscattering terms. Numerical simulations for a Gaussian ball model show significant difference between the linear and nonlinear Frechet derivatives.University of California, Santa Cruz (Wavelet Transform on Propagation and Imaging for seismic exploration Research Consortium); Massachusetts Institute of Technology. Earth Resources Laborator
Exploring the Design Space of Immersive Urban Analytics
Recent years have witnessed the rapid development and wide adoption of
immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft
HoloLens. These immersive devices have the potential to significantly extend
the methodology of urban visual analytics by providing critical 3D context
information and creating a sense of presence. In this paper, we propose an
theoretical model to characterize the visualizations in immersive urban
analytics. Further more, based on our comprehensive and concise model, we
contribute a typology of combination methods of 2D and 3D visualizations that
distinguish between linked views, embedded views, and mixed views. We also
propose a supporting guideline to assist users in selecting a proper view under
certain circumstances by considering visual geometry and spatial distribution
of the 2D and 3D visualizations. Finally, based on existing works, possible
future research opportunities are explored and discussed.Comment: 23 pages,11 figure
What Makes a Data-GIF Understandable?
GIFs are enjoying increasing popularity on social media as a format for
data-driven storytelling with visualization; simple visual messages are
embedded in short animations that usually last less than 15 seconds and are
played in automatic repetition. In this paper, we ask the question, "What makes
a data-GIF understandable?" While other storytelling formats such as data
videos, infographics, or data comics are relatively well studied, we have
little knowledge about the design factors and principles for "data-GIFs". To
close this gap, we provide results from semi-structured interviews and an
online study with a total of 118 participants investigating the impact of
design decisions on the understandability of data-GIFs. The study and our
consequent analysis are informed by a systematic review and structured design
space of 108 data-GIFs that we found online. Our results show the impact of
design dimensions from our design space such as animation encoding, context
preservation, or repetition on viewers' understanding of the GIF's core
message. The paper concludes with a list of suggestions for creating more
effective Data-GIFs
KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics
Visual analytics (VA) systems have been widely used to facilitate
decision-making and analytical reasoning in various application domains. VA
involves visual designs, interaction designs, and data mining, which is a
systematic and complex paradigm. In this work, we focus on the design of
effective visualizations for complex data and analytical tasks, which is a
critical step in designing a VA system. This step is challenging because it
requires extensive knowledge about domain problems and visualization to design
effective encodings. Existing visualization designs published in top venues are
valuable resources to inspire designs for problems with similar data structures
and tasks. However, those designs are hard to understand, parse, and retrieve
due to the lack of specifications. To address this problem, we build KB4VA, a
knowledge base of visualization designs in VA systems with comprehensive labels
about their analytical tasks and visual encodings. Our labeling scheme is
inspired by a workshop study with 12 VA researchers to learn user requirements
in understanding and retrieving professional visualization designs in VA
systems. The theme extends Vega-Lite specifications for describing advanced and
composited visualization designs in a declarative manner, thus facilitating
human understanding and automatic indexing. To demonstrate the usefulness of
our knowledge base, we present a user study about design inspirations for VA
tasks. In summary, our work opens new perspectives for enhancing the
accessibility and reusability of professional visualization designs
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