18,359 research outputs found
Event-based computer simulation model of Aspect-type experiments strictly satisfying Einstein's locality conditions
Inspired by Einstein-Podolsky-Rosen-Bohm experiments with photons, we
construct an event-based simulation model in which every essential element in
the ideal experiment has a counterpart. The model satisfies Einstein's criteria
of local causality and does not rely on concepts of quantum and probability
theory. We consider experiments in which the averages correspond to those of a
singlet and product state of a system of two particles. The data is
analyzed according to the experimental procedure, employing a time window to
identify pairs. We study how the time window and the passage time of the
photons, which depends on the relative angle between their polarization and the
polarizer's direction, influences the correlations, demonstrating that the
properties of the optical elements in the observation stations affect the
correlations although the stations are separated spatially and temporarily. We
show that the model can reproduce results which are considered to be
intrinsically quantum mechanical
Bootstrap and permutation tests of independence for point processes
Motivated by a neuroscience question about synchrony detection in spike train
analysis, we deal with the independence testing problem for point processes. We
introduce non-parametric test statistics, which are rescaled general
-statistics, whose corresponding critical values are constructed from
bootstrap and randomization/permutation approaches, making as few assumptions
as possible on the underlying distribution of the point processes. We derive
general consistency results for the bootstrap and for the permutation w.r.t. to
Wasserstein's metric, which induce weak convergence as well as convergence of
second order moments. The obtained bootstrap or permutation independence tests
are thus proved to be asymptotically of the prescribed size, and to be
consistent against any reasonable alternative. A simulation study is performed
to illustrate the derived theoretical results, and to compare the performance
of our new tests with existing ones in the neuroscientific literature
A comparative study of nonparametric methods for pattern recognition
The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
What are the limits of automated Twitter sentiment classification? We analyze
a large set of manually labeled tweets in different languages, use them as
training data, and construct automated classification models. It turns out that
the quality of classification models depends much more on the quality and size
of training data than on the type of the model trained. Experimental results
indicate that there is no statistically significant difference between the
performance of the top classification models. We quantify the quality of
training data by applying various annotator agreement measures, and identify
the weakest points of different datasets. We show that the model performance
approaches the inter-annotator agreement when the size of the training set is
sufficiently large. However, it is crucial to regularly monitor the self- and
inter-annotator agreements since this improves the training datasets and
consequently the model performance. Finally, we show that there is strong
evidence that humans perceive the sentiment classes (negative, neutral, and
positive) as ordered
Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search)
We consider the issue of reporting the result of search experiment in the
most unbiased and efficient way, i.e. in a way which allows an easy
interpretation and combination of results and which do not depend on whether
the experimenters believe or not to having found the searched-for effect. Since
this work uses the language of Bayesian theory, to which most physicists are
not used, we find that it could be useful to practitioners to have in a single
paper a simple presentation of Bayesian inference, together with an example of
application of it in search of rare processes.Comment: 36 pages, 11 figures, Latex files using cernart.cls (included). This
paper and related work are also available at
http://www-zeus.roma1.infn.it/~agostini/prob+stat.htm
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