15,759 research outputs found

### SMCTC : sequential Monte Carlo in C++

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation

### Magnetostrictive behaviour of thin superconducting disks

Flux-pinning-induced stress and strain distributions in a thin disk
superconductor in a perpendicular magnetic field is analyzed. We calculate the
body forces, solve the magneto-elastic problem and derive formulas for all
stress and strain components, including the magnetostriction $\Delta R/R$. The
flux and current density profiles in the disk are assumed to follow the Bean
model. During a cycle of the applied field the maximum tensile stress is found
to occur approximately midway between the maximum field and the remanent state.
An effective relationship between this overall maximum stress and the peak
field is found.Comment: 8 pages, 6 figures, submitted to Supercond. Sci. Technol., Proceed.
of MEM03 in Kyot

### SMCTC: Sequential Monte Carlo in C++

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature. Recent developments illustrate that these techniques have much more general applicability, and can be applied very effectively to statistical inference problems. Unfortunately, these methods are often perceived as being computationally expensive and difficult to implement. This article seeks to address both of these problems. A C++ template class library for the efficient and convenient implementation of very general Sequential Monte Carlo algorithms is presented. Two example applications are provided: a simple particle filter for illustrative purposes and a state-of-the-art algorithm for rare event estimation.

### An Update on NASA's Lunar Dust Mitigation Strategy

It is well known that the Apollo lu-nar surface missions experienced a number of issues related to dust which are sometimes referred to as The Dust Problem. The jagged, electrostatically charged lunar dust particles can foul mechanisms and alter thermal properties. They tend to abrade textiles and scratch surfaces. NASA and other interested par-ties require an integrated, end-to-end dust mitigation strategy to enable sustainable lunar architectures

### Pointwise Convergence in Probability of General Smoothing Splines

Establishing the convergence of splines can be cast as a variational problem
which is amenable to a $\Gamma$-convergence approach. We consider the case in
which the regularization coefficient scales with the number of observations,
$n$, as $\lambda_n=n^{-p}$. Using standard theorems from the
$\Gamma$-convergence literature, we prove that the general spline model is
consistent in that estimators converge in a sense slightly weaker than weak
convergence in probability for $p\leq \frac{1}{2}$. Without further assumptions
we show this rate is sharp. This differs from rates for strong convergence
using Hilbert scales where one can often choose $p>\frac{1}{2}$

### Convergence and Rates for Fixed-Interval Multiple-Track Smoothing Using $k$-Means Type Optimization

We address the task of estimating multiple trajectories from unlabeled data.
This problem arises in many settings, one could think of the construction of
maps of transport networks from passive observation of travellers, or the
reconstruction of the behaviour of uncooperative vehicles from external
observations, for example. There are two coupled problems. The first is a data
association problem: how to map data points onto individual trajectories. The
second is, given a solution to the data association problem, to estimate those
trajectories. We construct estimators as a solution to a regularized
variational problem (to which approximate solutions can be obtained via the
simple, efficient and widespread $k$-means method) and show that, as the number
of data points, $n$, increases, these estimators exhibit stable behaviour. More
precisely, we show that they converge in an appropriate Sobolev space in
probability and with rate $n^{-1/2}$

### A Critical Behaviour of Anomalous Currents, Electric-Magnetic Universality and CFT_4

We discuss several aspects of superconformal field theories in four
dimensions (CFT_4), in the context of electric-magnetic duality. We analyse the
behaviour of anomalous currents under RG flow to a conformal fixed point in
N=1, D=4 supersymmetric gauge theories. We prove that the anomalous dimension
of the Konishi current is related to the slope of the beta function at the
critical point. We extend the duality map to the (nonchiral) Konishi current.
As a byproduct we compute the slope of the beta function in the strong coupling
regime. We note that the OPE of $T_{\mu\nu}$ with itself does not close, but
mixes with a special additional operator $\Sigma$ which in general is the
Konishi current. We discuss the implications of this fact in generic
interacting conformal theories. In particular, a SCFT_4 seems to be naturally
equipped with a privileged off-critical deformation $\Sigma$ and this allows us
to argue that electric-magnetic duality can be extended to a neighborhood of
the critical point. We also stress that in SCFT_4 there are two central
charges, c and c', associated with the stress tensor and $\Sigma$,
respectively; c and c' allow us to count both the vector multiplet and the
matter multiplet effective degrees of freedom of the theory.Comment: harvmac tex, 28 pages, 3 figures. Version to be published in Nucl.
Phys.

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