14,506 research outputs found
Integration-free Learning of Flow Maps
We present a method for learning neural representations of flow maps from
time-varying vector field data. The flow map is pervasive within the area of
flow visualization, as it is foundational to numerous visualization techniques,
e.g. integral curve computation for pathlines or streaklines, as well as
computing separation/attraction structures within the flow field. Yet
bottlenecks in flow map computation, namely the numerical integration of vector
fields, can easily inhibit their use within interactive visualization settings.
In response, in our work we seek neural representations of flow maps that are
efficient to evaluate, while remaining scalable to optimize, both in
computation cost and data requirements. A key aspect of our approach is that we
can frame the process of representation learning not in optimizing for samples
of the flow map, but rather, a self-consistency criterion on flow map
derivatives that eliminates the need for flow map samples, and thus numerical
integration, altogether. Central to realizing this is a novel neural network
design for flow maps, coupled with an optimization scheme, wherein our
representation only requires the time-varying vector field for learning,
encoded as instantaneous velocity. We show the benefits of our method over
prior works in terms of accuracy and efficiency across a range of 2D and 3D
time-varying vector fields, while showing how our neural representation of flow
maps can benefit unsteady flow visualization techniques such as streaklines,
and the finite-time Lyapunov exponent
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
Reviews Sci-Tech Book News Reviews 12
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Analysis of Dynamic Brain Imaging Data
Modern imaging techniques for probing brain function, including functional
Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,
and magnetoencephalography, generate large data sets with complex content. In
this paper we develop appropriate techniques of analysis and visualization of
such imaging data, in order to separate the signal from the noise, as well as
to characterize the signal. The techniques developed fall into the general
category of multivariate time series analysis, and in particular we extensively
use the multitaper framework of spectral analysis. We develop specific
protocols for the analysis of fMRI, optical imaging and MEG data, and
illustrate the techniques by applications to real data sets generated by these
imaging modalities. In general, the analysis protocols involve two distinct
stages: `noise' characterization and suppression, and `signal' characterization
and visualization. An important general conclusion of our study is the utility
of a frequency-based representation, with short, moving analysis windows to
account for non-stationarity in the data. Of particular note are (a) the
development of a decomposition technique (`space-frequency singular value
decomposition') that is shown to be a useful means of characterizing the image
data, and (b) the development of an algorithm, based on multitaper methods, for
the removal of approximately periodic physiological artifacts arising from
cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files.
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9804003
Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma
A novel algorithm and implementation of real-time identification and tracking
of blob-filaments in fusion reactor data is presented. Similar spatio-temporal
features are important in many other applications, for example, ignition
kernels in combustion and tumor cells in a medical image. This work presents an
approach for extracting these features by dividing the overall task into three
steps: local identification of feature cells, grouping feature cells into
extended feature, and tracking movement of feature through overlapping in
space. Through our extensive work in parallelization, we demonstrate that this
approach can effectively make use of a large number of compute nodes to detect
and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion
simulation data, we observed linear speedup on 1024 processes and completed
blob detection in less than three milliseconds using Edison, a Cray XC30 system
at NERSC.Comment: 14 pages, 40 figure
ExploroBOT: Rapid Exploration with Chart Automation
General-purpose visualization tools are used by people with varying degrees of data literacy. Often the user is not a professional analyst or data scientist and uses the tool infrequently, to support an aspect of their job. This can present difficulties as the user’s unfamiliarity with visualization practice and infrequent use of the tool can result in long processing time, inaccurate data representations or inappropriate visual encodings. To address this problem, we developed a visual analytics application called exploroBOT. The exploroBOT automatically generates visualizations and the exploration guidance path (an associated network of decision points, mapping nodes where visualizations change). These combined approaches enable users to explore visualizations based on a degree of “interestingness”. The user-driven approach draws on the browse/explore metaphor commonly applied in social media applications and is supported by guided navigation. In this paper we describe exploroBOT and present an evaluation of the tool
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