2,304 research outputs found
The Clumping Transition in Niche Competition: a Robust Critical Phenomenon
We show analytically and numerically that the appearance of lumps and gaps in
the distribution of n competing species along a niche axis is a robust
phenomenon whenever the finiteness of the niche space is taken into account. In
this case depending if the niche width of the species is above or
below a threshold , which for large n coincides with 2/n, there are
two different regimes. For the lumpy pattern emerges
directly from the dominant eigenvector of the competition matrix because its
corresponding eigenvalue becomes negative. For the lumpy
pattern disappears. Furthermore, this clumping transition exhibits critical
slowing down as is approached from above. We also find that the number
of lumps of species vs. displays a stair-step structure. The positions
of these steps are distributed according to a power-law. It is thus
straightforward to predict the number of groups that can be packed along a
niche axis and it coincides with field measurements for a wide range of the
model parameters.Comment: 16 pages, 7 figures;
http://iopscience.iop.org/1742-5468/2010/05/P0500
On the eigenvalues of distance powers of circuits
Taking the d-th distance power of a graph, one adds edges between all pairs
of vertices of that graph whose distance is at most d. It is shown that only
the numbers -3, -2, -1, 0, 1, 2d can be integer eigenvalues of a circuit
distance power. Moreover, their respective multiplicities are determined and
explicit constructions for corresponding eigenspace bases containing only
vectors with entries -1, 0, 1 are given.Comment: 14 page
Hierarchical Parallel Matrix Multiplication on Large-Scale Distributed Memory Platforms
Matrix multiplication is a very important computation kernel both in its own
right as a building block of many scientific applications and as a popular
representative for other scientific applications. Cannon algorithm which dates
back to 1969 was the first efficient algorithm for parallel matrix
multiplication providing theoretically optimal communication cost. However this
algorithm requires a square number of processors. In the mid 1990s, the SUMMA
algorithm was introduced. SUMMA overcomes the shortcomings of Cannon algorithm
as it can be used on a non-square number of processors as well. Since then the
number of processors in HPC platforms has increased by two orders of magnitude
making the contribution of communication in the overall execution time more
significant. Therefore, the state of the art parallel matrix multiplication
algorithms should be revisited to reduce the communication cost further. This
paper introduces a new parallel matrix multiplication algorithm, Hierarchical
SUMMA (HSUMMA), which is a redesign of SUMMA. Our algorithm reduces the
communication cost of SUMMA by introducing a two-level virtual hierarchy into
the two-dimensional arrangement of processors. Experiments on an IBM BlueGene-P
demonstrate the reduction of communication cost up to 2.08 times on 2048 cores
and up to 5.89 times on 16384 cores.Comment: 9 page
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
In this paper we consider the problem of human pose estimation in real-world
videos of swimmers. Swimming channels allow filming swimmers simultaneously
above and below the water surface with a single stationary camera. These
recordings can be used to quantitatively assess the athletes' performance. The
quantitative evaluation, so far, requires manual annotations of body parts in
each video frame. We therefore apply the concept of CNNs in order to
automatically infer the required pose information. Starting with an
off-the-shelf architecture, we develop extensions to leverage activity
information - in our case the swimming style of an athlete - and the continuous
nature of the video recordings. Our main contributions are threefold: (a) We
apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a
baseline in our very challenging aquatic environment and discuss its error
modes, (b) we propose an extension to input swimming style information into the
fully convolutional architecture and (c) modify the architecture for continuous
pose estimation in videos. With these additions we achieve reliable pose
estimates with up to +16% more correct body joint detections compared to the
baseline architecture.Comment: 10 pages, 9 figures, accepted at WACV 201
Modeling 1D distributed-memory dense kernels for an asynchronous multifrontal sparse solver
To solve sparse systems of linear equations, multifrontal methods rely on dense partial LU decompositions of so-called frontal matrices; we consider a parallel asynchronous setting in which several frontal matrices can be factored simultaneously. In this context, to address performance and scalability issues of acyclic pipelined asynchronous factorization kernels, we study models to revisit properties of left and right-looking variants of partial decompositions, study the use of several levels of blocking, before focusing on communication issues. The general purpose sparse solver MUMPS has been modified to implement the proposed algorithms and confirm the properties demonstrated by the models
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