2,502 research outputs found
Adaptive Binning of X-ray data with Weighted Voronoi Tesselations
We present a technique to adaptively bin sparse X-ray data using weighted
Voronoi tesselations (WVTs). WVT binning is a generalisation of Cappellari &
Copin's (2001) Voronoi binning algorithm, developed for integral field
spectroscopy. WVT binning is applicable to many types of data and creates
unbiased binning structures with compact bins that do not lead the eye. We
apply the algorithm to simulated data, as well as several X-ray data sets, to
create adaptively binned intensity images, hardness ratio maps and temperature
maps with constant signal-to-noise ratio per bin. We also illustrate the
separation of diffuse gas emission from contributions of unresolved point
sources in elliptical galaxies. We compare the performance of WVT binning with
other adaptive binning and adaptive smoothing techniques. We find that the CIAO
tool csmooth creates serious artefacts and advise against its use to interpret
diffuse X-ray emission.Comment: 14 pages; submitted to MNRAS; code freely available at
http://www.phy.ohiou.edu/~diehl/WVT/index.html with user manual, examples and
high-resolution version of this pape
Constellation Queries over Big Data
A geometrical pattern is a set of points with all pairwise distances (or,
more generally, relative distances) specified. Finding matches to such patterns
has applications to spatial data in seismic, astronomical, and transportation
contexts. For example, a particularly interesting geometric pattern in
astronomy is the Einstein cross, which is an astronomical phenomenon in which a
single quasar is observed as four distinct sky objects (due to gravitational
lensing) when captured by earth telescopes. Finding such crosses, as well as
other geometric patterns, is a challenging problem as the potential number of
sets of elements that compose shapes is exponentially large in the size of the
dataset and the pattern. In this paper, we denote geometric patterns as
constellation queries and propose algorithms to find them in large data
applications. Our methods combine quadtrees, matrix multiplication, and
unindexed join processing to discover sets of points that match a geometric
pattern within some additive factor on the pairwise distances. Our distributed
experiments show that the choice of composition algorithm (matrix
multiplication or nested loops) depends on the freedom introduced in the query
geometry through the distance additive factor. Three clearly identified blocks
of threshold values guide the choice of the best composition algorithm.
Finally, solving the problem for relative distances requires a novel
continuous-to-discrete transformation. To the best of our knowledge this paper
is the first to investigate constellation queries at scale
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
Querying Probabilistic Neighborhoods in Spatial Data Sets Efficiently
In this paper we define the notion
of a probabilistic neighborhood in spatial data: Let a set of points in
, a query point , a distance metric \dist,
and a monotonically decreasing function be
given. Then a point belongs to the probabilistic neighborhood of with respect to with probability f(\dist(p,q)). We envision
applications in facility location, sensor networks, and other scenarios where a
connection between two entities becomes less likely with increasing distance. A
straightforward query algorithm would determine a probabilistic neighborhood in
time by probing each point in .
To answer the query in sublinear time for the planar case, we augment a
quadtree suitably and design a corresponding query algorithm. Our theoretical
analysis shows that -- for certain distributions of planar -- our algorithm
answers a query in time with high probability
(whp). This matches up to a logarithmic factor the cost induced by
quadtree-based algorithms for deterministic queries and is asymptotically
faster than the straightforward approach whenever .
As practical proofs of concept we use two applications, one in the Euclidean
and one in the hyperbolic plane. In particular, our results yield the first
generator for random hyperbolic graphs with arbitrary temperatures in
subquadratic time. Moreover, our experimental data show the usefulness of our
algorithm even if the point distribution is unknown or not uniform: The running
time savings over the pairwise probing approach constitute at least one order
of magnitude already for a modest number of points and queries.Comment: The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-44543-4_3
Implementation and application of adaptive mesh refinement for thermochemical mantle convection studies
Numerical modeling of mantle convection is challenging. Owing to the multiscale nature of mantle
dynamics, high resolution is often required in localized regions, with coarser resolution being sufficient
elsewhere. When investigating thermochemical mantle convection, high resolution is required to resolve
sharp and often discontinuous boundaries between distinct chemical components. In this paper, we present
a 2-D finite element code with adaptive mesh refinement techniques for simulating compressible thermochemical
mantle convection. By comparing model predictions with a range of analytical and previously
published benchmark solutions, we demonstrate the accuracy of our code. By refining and coarsening
the mesh according to certain criteria and dynamically adjusting the number of particles in each element,
our code can simulate such problems efficiently, dramatically reducing the computational requirements
(in terms of memory and CPU time) when compared to a fixed, uniform mesh simulation. The resolving
capabilities of the technique are further highlighted by examining plume‐induced entrainment in a thermochemical
mantle convection simulation
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