236 research outputs found
A Tale of Two Distances
There are two basic ways to measure physical distances in cosmology: One
based on standard candles and one based on standard rulers. Comparing current
data for each method allows us to rule out axion-photon mixing and
dust-extinction as the sources of supernova dimming and generally protects the
case for cosmic acceleration from attacks based on loss of photons. The
combined data constrains the energy densities in a LCDM model to 0.19 < Omega_m
< 0.32 and 0.47 < Omega_Lambda < 0.82 (at 2\sigma) without recourse to any
further data sets. Future data will improve on these limits and allow us to
place constraints on more exotic physics.Comment: 4 pages, 1 figure, uses moriond.sty. To be published in the
Proceedings of the XXXIX Rencontres de Moriond 'Exploring the Universe
Inflationary preheating and primordial black holes
Preheating after inflation may over-produce primordial black holes (PBH's) in
many regions of parameter space. As an example we study two-field models with a
massless self-interacting inflaton, taking into account second order field and
metric backreaction effects as spatial averages. We find that a complex quilt
of parameter regions above the Gaussian PBH over-production threshold emerges
due to the enhancement of curvature perturbations on all scales. It should be
possible to constrain realistic models of inflation through PBH over-production
although many issues, such as rescattering and non-Gaussianity, remain unsolved
or unexplored.Comment: 7 pages, 6 figures. Fig 6 is added to show the weak dependence of the
mass variance on the initial conditio
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video
https://vimeo.com/23451009
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