1,740 research outputs found
Multivariate Pointwise Information-Driven Data Sampling and Visualization
With increasing computing capabilities of modern supercomputers, the size of
the data generated from the scientific simulations is growing rapidly. As a
result, application scientists need effective data summarization techniques
that can reduce large-scale multivariate spatiotemporal data sets while
preserving the important data properties so that the reduced data can answer
domain-specific queries involving multiple variables with sufficient accuracy.
While analyzing complex scientific events, domain experts often analyze and
visualize two or more variables together to obtain a better understanding of
the characteristics of the data features. Therefore, data summarization
techniques are required to analyze multi-variable relationships in detail and
then perform data reduction such that the important features involving multiple
variables are preserved in the reduced data. To achieve this, in this work, we
propose a data sub-sampling algorithm for performing statistical data
summarization that leverages pointwise information theoretic measures to
quantify the statistical association of data points considering multiple
variables and generates a sub-sampled data that preserves the statistical
association among multi-variables. Using such reduced sampled data, we show
that multivariate feature query and analysis can be done effectively. The
efficacy of the proposed multivariate association driven sampling algorithm is
presented by applying it on several scientific data sets.Comment: 25 page
Local/global analysis of the stationary solutions of some neural field equations
Neural or cortical fields are continuous assemblies of mesoscopic models,
also called neural masses, of neural populations that are fundamental in the
modeling of macroscopic parts of the brain. Neural fields are described by
nonlinear integro-differential equations. The solutions of these equations
represent the state of activity of these populations when submitted to inputs
from neighbouring brain areas. Understanding the properties of these solutions
is essential in advancing our understanding of the brain. In this paper we
study the dependency of the stationary solutions of the neural fields equations
with respect to the stiffness of the nonlinearity and the contrast of the
external inputs. This is done by using degree theory and bifurcation theory in
the context of functional, in particular infinite dimensional, spaces. The
joint use of these two theories allows us to make new detailed predictions
about the global and local behaviours of the solutions. We also provide a
generic finite dimensional approximation of these equations which allows us to
study in great details two models. The first model is a neural mass model of a
cortical hypercolumn of orientation sensitive neurons, the ring model. The
second model is a general neural field model where the spatial connectivity
isdescribed by heterogeneous Gaussian-like functions.Comment: 38 pages, 9 figure
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Many analyses of neuroimaging data involve studying one or more regions of
interest (ROIs) in a brain image. In order to do so, each ROI must first be
identified. Since every brain is unique, the location, size, and shape of each
ROI varies across subjects. Thus, each ROI in a brain image must either be
manually identified or (semi-) automatically delineated, a task referred to as
segmentation. Automatic segmentation often involves mapping a previously
manually segmented image to a new brain image and propagating the labels to
obtain an estimate of where each ROI is located in the new image. A more recent
approach to this problem is to propagate labels from multiple manually
segmented atlases and combine the results using a process known as label
fusion. To date, most label fusion algorithms either employ voting procedures
or impose prior structure and subsequently find the maximum a posteriori
estimator (i.e., the posterior mode) through optimization. We propose using a
fully Bayesian spatial regression model for label fusion that facilitates
direct incorporation of covariate information while making accessible the
entire posterior distribution. We discuss the implementation of our model via
Markov chain Monte Carlo and illustrate the procedure through both simulation
and application to segmentation of the hippocampus, an anatomical structure
known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure
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Visualization-driven Structural and Statistical Analysis of Turbulent Flows
Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In- teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro- vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa- cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul- tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool called VAPOR, which is in routine use by scientists internationally. In data from a 10243 simulation of a forced turbulent flow, VAPOR allowed us to perform a visual data exploration of the flow properties at interac- tive speeds, leading to the discovery of novel scientific properties of the flow, in the form of two distinct vortical structure populations. These structures would have been very difficult (if not impossible) to find with statistical overviews or other existing visualization-driven analysis ap- proaches. This kind of intelligent, focused analysis/refinement approach will become even more important as computational science moves to- wards petascale applications
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