24,233 research outputs found
Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression
In this work we perform a meta-analysis of neuroimaging data, consisting of
locations of peak activations identified in 162 separate studies on emotion.
Neuroimaging meta-analyses are typically performed using kernel-based methods.
However, these methods require the width of the kernel to be set a priori and
to be constant across the brain. To address these issues, we propose a fully
Bayesian nonparametric binary regression method to perform neuroimaging
meta-analyses. In our method, each location (or voxel) has a probability of
being a peak activation, and the corresponding probability function is based on
a spatially adaptive Gaussian Markov random field (GMRF). We also include
parameters in the model to robustify the procedure against miscoding of the
voxel response. Posterior inference is implemented using efficient MCMC
algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1
(2006) 145--168]. Our method allows the probability function to be locally
adaptive with respect to the covariates, that is, to be smooth in one region of
the covariate space and wiggly or even discontinuous in another. Posterior
miscoding probabilities for each of the identified voxels can also be obtained,
identifying voxels that may have been falsely classified as being activated.
Simulation studies and application to the emotion neuroimaging data indicate
that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments
With continued advances in Geographic Information Systems and related
computational technologies, statisticians are often required to analyze very
large spatial datasets. This has generated substantial interest over the last
decade, already too vast to be summarized here, in scalable methodologies for
analyzing large spatial datasets. Scalable spatial process models have been
found especially attractive due to their richness and flexibility and,
particularly so in the Bayesian paradigm, due to their presence in hierarchical
model settings. However, the vast majority of research articles present in this
domain have been geared toward innovative theory or more complex model
development. Very limited attention has been accorded to approaches for easily
implementable scalable hierarchical models for the practicing scientist or
spatial analyst. This article is submitted to the Practice section of the
journal with the aim of developing massively scalable Bayesian approaches that
can rapidly deliver Bayesian inference on spatial process that are practically
indistinguishable from inference obtained using more expensive alternatives. A
key emphasis is on implementation within very standard (modest) computing
environments (e.g., a standard desktop or laptop) using easily available
statistical software packages without requiring message-parsing interfaces or
parallel programming paradigms. Key insights are offered regarding assumptions
and approximations concerning practical efficiency.Comment: 20 pages, 4 figures, 2 table
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Temporally coherent 4D reconstruction of complex dynamic scenes
This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 . Video available at:
https://www.youtube.com/watch?v=bm_P13_-Ds
The Dominance of Metal-Rich Streams in Stellar Halos: A Comparison Between Substructure in M31 and Lambda-CDM Models
Extensive photometric and spectroscopic surveys of the Andromeda galaxy (M31)
have discovered tidal debris features throughout M31's stellar halo. We present
stellar kinematics and metallicities in fields with identified substructure
from our on-going SPLASH survey of M31 red giant branch stars with the DEIMOS
spectrograph on the Keck II 10-m telescope. Radial velocity criteria are used
to isolate members of the kinematically-cold substructures. The substructures
are shown to be metal-rich relative to the rest of the dynamically hot stellar
population in the fields in which they are found. We calculate the mean
metallicity and average surface brightness of the various kinematical
components in each field, and show that, on average, higher surface brightness
features tend to be more metal-rich than lower surface brightness features.
Simulations of stellar halo formation via accretion in a cosmological context
are used to illustrate that the observed trend can be explained as a natural
consequence of the observed dwarf galaxy mass-metallicity relation. A
significant spread in metallicity at a given surface brightness is seen in the
data; we show that this is due to time effects, namely the variation in the
time since accretion of the tidal streams' progenitor onto the host halo. We
show that in this theoretical framework a relationship between the
alpha-enhancement and surface brightness of tidal streams is expected, which
arises from the varying times of accretion of the progenitor satellites onto
the host halo. Thus, measurements of the alpha-enrichment, metallicity, and
surface brightness of tidal debris can be used to reconstruct the luminosity
and time of accretion onto the host halo of the progenitors of tidal streams.Comment: 11 pages, 6 figures, published in Ap
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