3,915 research outputs found
An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments
X-ray standing-wave photoemission experiments involving multilayered samples
are emerging as unique probes of the buried interfaces that are ubiquitous in
current device and materials research. Such data require for their analysis a
structure optimization process comparing experiment to theory that is not
straightforward. In this work, we present a new computer program for optimizing
the analysis of standing-wave data, called SWOPT, that automates this
trial-and-error optimization process. The program includes an algorithm that
has been developed for computationally expensive problems: so-called black-box
simulation optimizations. It also includes a more efficient version of the Yang
X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S.,
Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which
is about an order of magnitude faster than the original version. Human
interaction is not required during optimization. We tested our optimization
algorithm on real and hypothetical problems and show that it finds better
solutions significantly faster than a random search approach. The total
optimization time ranges, depending on the sample structure, from minutes to a
few hours on a modern laptop computer, and can be up to 100x faster than a
corresponding manual optimization. These speeds make the SWOPT program a
valuable tool for realtime analyses of data during synchrotron experiments
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from an
observational joint distribution. One can use these graphs to predict the
outcome of interventional experiments, from which data are often not available.
We show that if the observational distribution follows a structural equation
model with an additive noise structure, the directed acyclic graph becomes
identifiable from the distribution under mild conditions. This constitutes an
interesting alternative to traditional methods that assume faithfulness and
identify only the Markov equivalence class of the graph, thus leaving some
edges undirected. We provide practical algorithms for finitely many samples,
RESIT (Regression with Subsequent Independence Test) and two methods based on
an independence score. We prove that RESIT is correct in the population setting
and provide an empirical evaluation
Forward Flux Sampling for rare event simulations
Rare events are ubiquitous in many different fields, yet they are notoriously
difficult to simulate because few, if any, events are observed in a conventiona
l simulation run. Over the past several decades, specialised simulation methods
have been developed to overcome this problem. We review one recently-developed
class of such methods, known as Forward Flux Sampling. Forward Flux Sampling
uses a series of interfaces between the initial and final states to calculate
rate constants and generate transition paths, for rare events in equilibrium or
nonequilibrium systems with stochastic dynamics. This review draws together a
number of recent advances, summarizes several applications of the method and
highlights challenges that remain to be overcome.Comment: minor typos in the manuscript. J.Phys.:Condensed Matter (accepted for
publication
Gamma-based clustering via ordered means with application to gene-expression analysis
Discrete mixture models provide a well-known basis for effective clustering
algorithms, although technical challenges have limited their scope. In the
context of gene-expression data analysis, a model is presented that mixes over
a finite catalog of structures, each one representing equality and inequality
constraints among latent expected values. Computations depend on the
probability that independent gamma-distributed variables attain each of their
possible orderings. Each ordering event is equivalent to an event in
independent negative-binomial random variables, and this finding guides a
dynamic-programming calculation. The structuring of mixture-model components
according to constraints among latent means leads to strict concavity of the
mixture log likelihood. In addition to its beneficial numerical properties, the
clustering method shows promising results in an empirical study.Comment: Published in at http://dx.doi.org/10.1214/10-AOS805 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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