8,771 research outputs found
A robust high-sensitivity algorithm for automated detection of proteins in two-dimensional electrophoresis gels
The automated interpretation of two-dimensional gel electrophoresis images used in protein separation and analysis presents a formidable problem in the detection and characterization of ill-defined spatial objects. We describe in this paper a hierarchical algorithm that provides a robust, high-sensitivity solution to this problem, which can be easily adapted to a variety of experimental situations. The software implementation of this algorithm functions as part of a complete package designed for general protein gel analysis applications
On the Differential Privacy of Bayesian Inference
We study how to communicate findings of Bayesian inference to third parties,
while preserving the strong guarantee of differential privacy. Our main
contributions are four different algorithms for private Bayesian inference on
proba-bilistic graphical models. These include two mechanisms for adding noise
to the Bayesian updates, either directly to the posterior parameters, or to
their Fourier transform so as to preserve update consistency. We also utilise a
recently introduced posterior sampling mechanism, for which we prove bounds for
the specific but general case of discrete Bayesian networks; and we introduce a
maximum-a-posteriori private mechanism. Our analysis includes utility and
privacy bounds, with a novel focus on the influence of graph structure on
privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and
Bayesian linear regression illustrate the application of our mechanisms.Comment: AAAI 2016, Feb 2016, Phoenix, Arizona, United State
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph
A recently proposed methodology called the Horizontal Visibility Graph (HVG)
[Luque {\it et al.}, Phys. Rev. E., 80, 046103 (2009)] that constitutes a
geometrical simplification of the well known Visibility Graph algorithm [Lacasa
{\it et al.\/}, Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to
study the distinction between deterministic and stochastic components in time
series [L. Lacasa and R. Toral, Phys. Rev. E., 82, 036120 (2010)].
Specifically, the authors propose that the node degree distribution of these
processes follows an exponential functional of the form , in which is the node degree and is a
positive parameter able to distinguish between deterministic (chaotic) and
stochastic (uncorrelated and correlated) dynamics. In this work, we investigate
the characteristics of the node degree distributions constructed by using HVG,
for time series corresponding to chaotic maps and different stochastic
processes. We thoroughly study the methodology proposed by Lacasa and Toral
finding several cases for which their hypothesis is not valid. We propose a
methodology that uses the HVG together with Information Theory quantifiers. An
extensive and careful analysis of the node degree distributions obtained by
applying HVG allow us to conclude that the Fisher-Shannon information plane is
a remarkable tool able to graphically represent the different nature,
deterministic or stochastic, of the systems under study.Comment: Submitted to PLOS On
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