207 research outputs found
Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours without Edges (ACWE)
This paper presents an intramethod ensemble for coronal hole (CH) detection
based on the Active Contours Without Edges (ACWE) segmentation algorithm. The
purpose of this ensemble is to develop a confidence map that defines, for all
on disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood that
each region belongs to a CH based on that region's proximity to, and
homogeneity with, the core of identified CH regions. CHs are regions of open
magnetic field lines, resulting in high speed solar wind. Accurate detection of
CHs is vital for space weather prediction. By relying on region homogeneity,
and not intensity (which can vary due to various factors including line of
sight changes and stray light from nearby bright regions), to define the final
confidence of any given region, this ensemble is able to provide robust,
consistent delineations of the CH regions. Using the metrics of global
consistency error (GCE), local consistency error (LCE), intersection over union
(IOU), and the structural similarity index measure (SSIM), the method is shown
to be robust to different spatial resolutions and different intensity
resolutions. Furthermore, using the same metrics, the method is shown to be
robust across short timescales, indicating self-consistent segmentations.
Finally, the accuracy of the segmentations and confidence maps are validated by
considering the skewness (i.e., unipolarity) of the underlying magnetic field
PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers
The aim of this paper is to generalize the PAC-Bayesian theorems proved by
Catoni in the classification setting to more general problems of statistical
inference. We show how to control the deviations of the risk of randomized
estimators. A particular attention is paid to randomized estimators drawn in a
small neighborhood of classical estimators, whose study leads to control the
risk of the latter. These results allow to bound the risk of very general
estimation procedures, as well as to perform model selection
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Propranolol treatment of infantile hemangioma endothelial cells: A molecular analysis
Infantile hemangiomas (IHs) are non-malignant, largely cutaneous vascular tumors affecting approximately 5–10% of children to varying degrees. During the first year of life, these tumors are strongly proliferative, reaching an average size ranging from 2 to 20 cm. These lesions subsequently stabilize, undergo a spontaneous slow involution and are fully regressed by 5 to 10 years of age. Systemic treatment of infants with the non-selective β-adrenergic receptor blocker, propranolol, has demonstrated remarkable efficacy in reducing the size and appearance of IHs. However, the mechanism by which this occurs is largely unknown. In this study, we sought to understand the molecular mechanisms underlying the effectiveness of β blocker treatment in IHs. Our data reveal that propranolol treatment of IH endothelial cells, as well as a panel of normal primary endothelial cells, blocks endothelial cell proliferation, migration, and formation of the actin cytoskeleton coincident with alterations in vascular endothelial growth factor receptor-2 (VEGFR-2), p38 and cofilin signaling. Moreover, propranolol induces major alterations in the protein levels of key cyclins and cyclin-dependent kinase inhibitors, and modulates global gene expression patterns with a particular affect on genes involved in lipid/sterol metabolism, cell cycle regulation, angiogenesis and ubiquitination. Interestingly, the effects of propranolol were endothelial cell-type independent, affecting the properties of IH endothelial cells at similar levels to that observed in neonatal dermal microvascular and coronary artery endothelial cells. This data suggests that while propranolol markedly inhibits hemangioma and normal endothelial cell function, its lack of endothelial cell specificity hints that the efficacy of this drug in the treatment of IHs may be more complex than simply blockage of endothelial function as previously believed
A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
This paper addresses the problem of Monte Carlo approximation of posterior
probability distributions. In particular, we have considered a recently
proposed technique known as population Monte Carlo (PMC), which is based on an
iterative importance sampling approach. An important drawback of this
methodology is the degeneracy of the importance weights when the dimension of
either the observations or the variables of interest is high. To alleviate this
difficulty, we propose a novel method that performs a nonlinear transformation
on the importance weights. This operation reduces the weight variation, hence
it avoids their degeneracy and increases the efficiency of the importance
sampling scheme, specially when drawing from a proposal functions which are
poorly adapted to the true posterior.
For the sake of illustration, we have applied the proposed algorithm to the
estimation of the parameters of a Gaussian mixture model. This is a very simple
problem that enables us to clearly show and discuss the main features of the
proposed technique. As a practical application, we have also considered the
popular (and challenging) problem of estimating the rate parameters of
stochastic kinetic models (SKM). SKMs are highly multivariate systems that
model molecular interactions in biological and chemical problems. We introduce
a particularization of the proposed algorithm to SKMs and present numerical
results.Comment: 35 pages, 8 figure
Some inequalities on generalized entropies
We give several inequalities on generalized entropies involving Tsallis
entropies, using some inequalities obtained by improvements of Young's
inequality. We also give a generalized Han's inequality.Comment: 15 page
Paraphrastic Reformulations in Spoken Corpora
International audienceOur work addresses the automatic detection of paraphrastic reformulation in French spoken corpora. The proposed approach is syn-tagmatic. It is based on specific markers and the specificities of the spoken language. Manual multi-dimensional annotation performed by two annotators provides fine-grained reference data. An automatic method is proposed in order to decide whether sentences contain or not paraphras-tic relations. The obtained results show up to 66.4% precision. Analysis of the manual annotations indicates that few paraphrastic segments show morphological modifications (inflection, derivation or compounding) and that the syntactic equivalence between the segments is seldom respected, as these usually belong to different syntactic categories
Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective
We consider the problem of computing the maximum-reward motion in a reward field in an online setting. We assume that the robot has a limited perception range, and it discovers the reward field on the fly. We analyze the performance of a simple, practical lattice-based algorithm with respect to the perception range. Our main result is that, with very little perception range, the robot can collect as much reward as if it could see the whole reward field, under certain assumptions. Along the way, we establish novel connections between this class of problems and certain fundamental problems of nonequilibrium statistical mechanics . We demonstrate our results in simulation examples
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