4,759 research outputs found
Smallest small-world network
Efficiency in passage times is an important issue in designing networks, such
as transportation or computer networks. The small-world networks have
structures that yield high efficiency, while keeping the network highly
clustered. We show that among all networks with the small-world structure, the
most efficient ones have a single ``center'', from which all shortcuts are
connected to uniformly distributed nodes over the network. The networks with
several centers and a connected subnetwork of shortcuts are shown to be
``almost'' as efficient. Genetic-algorithm simulations further support our
results.Comment: 5 pages, 6 figures, REVTeX
A Probabilistic Interpretation of Sampling Theory of Graph Signals
We give a probabilistic interpretation of sampling theory of graph signals.
To do this, we first define a generative model for the data using a pairwise
Gaussian random field (GRF) which depends on the graph. We show that, under
certain conditions, reconstructing a graph signal from a subset of its samples
by least squares is equivalent to performing MAP inference on an approximation
of this GRF which has a low rank covariance matrix. We then show that a
sampling set of given size with the largest associated cut-off frequency, which
is optimal from a sampling theoretic point of view, minimizes the worst case
predictive covariance of the MAP estimate on the GRF. This interpretation also
gives an intuitive explanation for the superior performance of the sampling
theoretic approach to active semi-supervised classification.Comment: 5 pages, 2 figures, To appear in International Conference on
Acoustics, Speech, and Signal Processing (ICASSP) 201
The Parameter-Less Self-Organizing Map algorithm
The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network
algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a
learning rate and annealing schemes for learning rate and neighbourhood size.
We discuss the relative performance of the PLSOM and the SOM and demonstrate
some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally
we discuss some example applications of the PLSOM and present a proof of
ordering under certain limited conditions.Comment: 29 pages, 27 figures. Based on publication in IEEE Trans. on Neural
Network
{\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks
We present a further development of a method for accelerating the calculation
of CMB power spectra, matter power spectra and likelihood functions for use in
cosmological Bayesian inference. The algorithm, called {\sc CosmoNet}, is based
on training a multilayer perceptron neural network. We compute CMB power
spectra (up to ) and matter transfer functions over a hypercube in
parameter space encompassing the confidence region of a selection of
CMB (WMAP + high resolution experiments) and large scale structure surveys (2dF
and SDSS). We work in the framework of a generic 7 parameter non-flat
cosmology. Additionally we use {\sc CosmoNet} to compute the WMAP 3-year, 2dF
and SDSS likelihoods over the same region. We find that the average error in
the power spectra is typically well below cosmic variance for spectra, and
experimental likelihoods calculated to within a fraction of a log unit. We
demonstrate that marginalised posteriors generated with {\sc CosmoNet} spectra
agree to within a few percent of those generated by {\sc CAMB} parallelised
over 4 CPUs, but are obtained 2-3 times faster on just a \emph{single}
processor. Furthermore posteriors generated directly via {\sc CosmoNet}
likelihoods can be obtained in less than 30 minutes on a single processor,
corresponding to a speed up of a factor of . We also demonstrate the
capabilities of {\sc CosmoNet} by extending the CMB power spectra and matter
transfer function training to a more generic 10 parameter cosmological model,
including tensor modes, a varying equation of state of dark energy and massive
neutrinos. {\sc CosmoNet} and interfaces to both {\sc CosmoMC} and {\sc
Bayesys} are publically available at {\tt
www.mrao.cam.ac.uk/software/cosmonet}.Comment: 8 pages, submitted to MNRA
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