1,101 research outputs found
The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors
We consider the horseshoe estimator due to Carvalho, Polson and Scott (2010)
for the multivariate normal mean model in the situation that the mean vector is
sparse in the nearly black sense. We assume the frequentist framework where the
data is generated according to a fixed mean vector. We show that if the number
of nonzero parameters of the mean vector is known, the horseshoe estimator
attains the minimax risk, possibly up to a multiplicative constant. We
provide conditions under which the horseshoe estimator combined with an
empirical Bayes estimate of the number of nonzero means still yields the
minimax risk. We furthermore prove an upper bound on the rate of contraction of
the posterior distribution around the horseshoe estimator, and a lower bound on
the posterior variance. These bounds indicate that the posterior distribution
of the horseshoe prior may be more informative than that of other one-component
priors, including the Lasso.Comment: This version differs from the final published version in pagination
and typographical detail; Available at
http://projecteuclid.org/euclid.ejs/141813426
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
Convolutional Neural Networks (ConvNets) are one of the most promising
methods for identifying strong gravitational lens candidates in survey data. We
present two ConvNet lens-finders which we have trained with a dataset composed
of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed
sources. One ConvNet is trained with single \textit{r}-band galaxy images,
hence basing the classification mostly on the morphology. While the other
ConvNet is trained on \textit{g-r-i} composite images, relying mostly on
colours and morphology. We have tested the ConvNet lens-finders on a sample of
21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and
compared the results with our previous ConvNet lens-finder on the same sample.
The new lens-finders achieve a higher accuracy and completeness in identifying
gravitational lens candidates, especially the single-band ConvNet. Our analysis
indicates that this is mainly due to improved simulations of the lensed
sources. In particular, the single-band ConvNet can select a sample of lens
candidates with purity, retrieving 3 out of 4 of the confirmed
gravitational lenses in the LRG sample. With this particular setup and limited
human intervention, it will be possible to retrieve, in future surveys such as
Euclid, a sample of lenses exceeding in size the total number of currently
known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA
Neurotransmitters as food supplements: the effects of GABA on brain and behavior
Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human cortex. The food supplement version of GABA is widely available online. Although many consumers claim that they experience benefits from the use of these products, it is unclear whether these supplements confer benefits beyond a placebo effect. Currently, the mechanism of action behind these products is unknown. It has long been thought that GABA is unable to cross the blood–brain barrier (BBB), but the studies that have assessed this issue are often contradictory and range widely in their employed methods. Accordingly, future research needs to establish the effects of oral GABA administration on GABA levels in the human brain, for example using magnetic resonance spectroscopy. There is some evidence in favor of a calming effect of GABA food supplements, but most of this evidence was reported by researchers with a potential conflict of interest. We suggest that any veridical effects of GABA food supplements on brain and cognition might be exerted through BBB passage or, more indirectly, via an effect on the enteric nervous system. We conclude that the mechanism of action of GABA food supplements is far from clear, and that further work is needed to establish the behavioral effects of GABA
Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
The volume of data that will be produced by new-generation surveys requires
automatic classification methods to select and analyze sources. Indeed, this is
the case for the search for strong gravitational lenses, where the population
of the detectable lensed sources is only a very small fraction of the full
source population. We apply for the first time a morphological classification
method based on a Convolutional Neural Network (CNN) for recognizing strong
gravitational lenses in square degrees of the Kilo Degree Survey (KiDS),
one of the current-generation optical wide surveys. The CNN is currently
optimized to recognize lenses with Einstein radii arcsec, about
twice the -band seeing in KiDS. In a sample of colour-magnitude
selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN
retrieves 761 strong-lens candidates and correctly classifies two out of three
of the known lenses. The misclassified lens has an Einstein radius below the
range on which the algorithm is trained. We down-select the most reliable 56
candidates by a joint visual inspection. This final sample is presented and
discussed. A conservative estimate based on our results shows that with our
proposed method it should be possible to find massive LRG-galaxy
lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario
this number can grow considerably (to maximally 2400 lenses), when
widening the colour-magnitude selection and training the CNN to recognize
smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA
Growth-rate dependent and nutrient-specific gene expression resource allocation in fission yeast
Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determinedtheimportanceof the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombegrownon non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart fromRNA polymerase II-dependent transcription.Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass,showedmostly condition-specificregulation.In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our 19basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression
Testing the assumptions of linear prediction analysis in normal vowels
This paper develops an improved surrogate data test to show experimental evidence, for all the simple vowels of US English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or non-Gaussianity common to more recent, complex, efforts at dynamical modelling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/non-Gaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/non-Gaussian modelling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance
Origin of the X-ray Emission in the Nuclei of FR Is
We investigate the X-ray origin in FRIs using the multi-waveband high
resolution data of eight FR I sources, which have very low Eddington ratios. We
fit their multi-waveband spectrum using a coupled accretion-jet model. We find
that X-ray emission in the source with the highest L_X (~1.8*10^-4 L_Edd) is
from the advection-dominated accretion flow (ADAF). Four sources with moderate
L_X(~several*10^-6 L_Edd) are complicated. The X-ray emission of one FR I is
from the jet, and the other three is from the sum of the jet and ADAF. The
X-ray emission in the three least luminous sources (L_X<1.0*10^-6L_Edd) is
dominated by the jet. These results roughly support the predictions of Yuan and
Cui(2005) where they predict that when the X-ray luminosity of the system is
below a critical value, the X-radiation will not be dominated by the emission
from the ADAF any longer, but by the jet. We also find that the accretion rates
in four sources must be higher than the Bondi rates, which implies that other
fuel supply (e.g., stellar winds) inside the Bondi radius should be important.Comment: 6 pages. To published in Journal of Physics, in proceedings of "The
Universe under the Microscope - Astrophysics at High Angular Resolution" (Bad
Honnef, Germany, April 2008), eds. R. Schoedel, A. Eckart, S. Pfalzner, and
E. Ro
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