4,388 research outputs found
Effect of current corrugations on the stability of the tearing mode
The generation of zonal magnetic fields in laboratory fusion plasmas is
predicted by theoretical and numerical models and was recently observed
experimentally. It is shown that the modification of the current density
gradient associated with such corrugations can significantly affect the
stability of the tearing mode. A simple scaling law is derived that predicts
the impact of small stationary current corrugations on the stability parameter
. The described destabilization mechanism can provide an explanation
for the trigger of the Neoclassical Tearing Mode (NTM) in plasmas without
significant MHD activity.Comment: Accepted to Physics of Plasma
Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation
The Expected Value of Perfect Partial Information (EVPPI) is a
decision-theoretic measure of the "cost" of parametric uncertainty in decision
making used principally in health economic decision making. Despite this
decision-theoretic grounding, the uptake of EVPPI calculations in practice has
been slow. This is in part due to the prohibitive computational time required
to estimate the EVPPI via Monte Carlo simulations. However, recent developments
have demonstrated that the EVPPI can be estimated by non-parametric regression
methods, which have significantly decreased the computation time required to
approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian
Process regression is suggested, but this can still be prohibitively expensive.
Applying fast computation methods developed in spatial statistics using
Integrated Nested Laplace Approximations (INLA) and projecting from a
high-dimensional into a low-dimensional input space allows us to decrease the
computation time for fitting these high-dimensional Gaussian Processes, often
substantially. We demonstrate that the EVPPI calculated using our method for
Gaussian Process regression is in line with the standard Gaussian Process
regression method and that despite the apparent methodological complexity of
this new method, R functions are available in the package BCEA to implement it
simply and efficiently
High-Dimensional Inference with the generalized Hopfield Model: Principal Component Analysis and Corrections
We consider the problem of inferring the interactions between a set of N
binary variables from the knowledge of their frequencies and pairwise
correlations. The inference framework is based on the Hopfield model, a special
case of the Ising model where the interaction matrix is defined through a set
of patterns in the variable space, and is of rank much smaller than N. We show
that Maximum Lik elihood inference is deeply related to Principal Component
Analysis when the amp litude of the pattern components, xi, is negligible
compared to N^1/2. Using techniques from statistical mechanics, we calculate
the corrections to the patterns to the first order in xi/N^1/2. We stress that
it is important to generalize the Hopfield model and include both attractive
and repulsive patterns, to correctly infer networks with sparse and strong
interactions. We present a simple geometrical criterion to decide how many
attractive and repulsive patterns should be considered as a function of the
sampling noise. We moreover discuss how many sampled configurations are
required for a good inference, as a function of the system size, N and of the
amplitude, xi. The inference approach is illustrated on synthetic and
biological data.Comment: Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
(2011) to appea
Additive Nonparametric Reconstruction of Dynamical Systems from Time Series
We present a nonparametric way to retrieve a system of differential equations
in embedding space from a single time series. These equations can be treated
with dynamical systems theory and allow for long term predictions. We
demonstrate the potential of our approach for a modified chaotic Chua
oscillator.Comment: accepted for Phys. Rev. E, Rapid Com
Extensive telomere repeat arrays in mouse are hypervariable
In this study we have analysed mouse telomeres by Pulsed Field Gel Electrophoresis (PFGE). A number of specific restriction fragments hybridising to a (TTA-GGG)4 probe in the size range 50-150kb can be detected. These fragments are devoid of sites for most restriction enzymes suggesting that they comprise simple repeats; we argue that most of these are likely to be (TTAGGG)n. Each discrete fragment corresponds to the telomere of an individual chromosome and segregates as a Mendelian character. However, new size variants are being generated in the germ line at very high rates such that inbred mice are heterozygous at all telomeres analysable. In addition we show that specific small (approximately 4-12kb) fragments can be cleaved within some terminal arrays by the restriction enzyme MnII which recognises 5'(N7)GAGG3'. Like the complete telomere-repeat arrays (TRA's) these fragments form new variants at high rates and possibly by the same process. We speculate on the mechanisms that may be involved
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
The method is embedded into a kernel regression machine that can model general
nonlinear functions, not being a priori limited to additive models. This is the
first kernel-based variable selection method applicable to large datasets. It
sidesteps the typical poor scaling properties of kernel methods by mapping the
inputs into a relatively low-dimensional space of random features. The
algorithm discovers the variables relevant for the regression task together
with learning the prediction model through learning the appropriate nonlinear
random feature maps. We demonstrate the outstanding performance of our method
on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201
Multivariate side-band subtraction using probabilistic event weights
A common situation in experimental physics is to have a signal which can not
be separated from a non-interfering background through the use of any cut. In
this paper, we describe a procedure for determining, on an event-by-event
basis, a quality factor (-factor) that a given event originated from the
signal distribution. This procedure generalizes the "side-band" subtraction
method to higher dimensions without requiring the data to be divided into bins.
The -factors can then be used as event weights in subsequent analysis
procedures, allowing one to more directly access the true spectrum of the
signal.Comment: 17 pages, 9 figure
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