1,377,408 research outputs found
Validating module network learning algorithms using simulated data
In recent years, several authors have used probabilistic graphical models to
learn expression modules and their regulatory programs from gene expression
data. Here, we demonstrate the use of the synthetic data generator SynTReN for
the purpose of testing and comparing module network learning algorithms. We
introduce a software package for learning module networks, called LeMoNe, which
incorporates a novel strategy for learning regulatory programs. Novelties
include the use of a bottom-up Bayesian hierarchical clustering to construct
the regulatory programs, and the use of a conditional entropy measure to assign
regulators to the regulation program nodes. Using SynTReN data, we test the
performance of LeMoNe in a completely controlled situation and assess the
effect of the methodological changes we made with respect to an existing
software package, namely Genomica. Additionally, we assess the effect of
various parameters, such as the size of the data set and the amount of noise,
on the inference performance. Overall, application of Genomica and LeMoNe to
simulated data sets gave comparable results. However, LeMoNe offers some
advantages, one of them being that the learning process is considerably faster
for larger data sets. Additionally, we show that the location of the regulators
in the LeMoNe regulation programs and their conditional entropy may be used to
prioritize regulators for functional validation, and that the combination of
the bottom-up clustering strategy with the conditional entropy-based assignment
of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio
Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Segmentation of ultrasound images is an essential task in both diagnosis and
image-guided interventions given the ease-of-use and low cost of this imaging
modality. As manual segmentation is tedious and time consuming, a growing body
of research has focused on the development of automatic segmentation
algorithms. Deep learning algorithms have shown remarkable achievements in this
regard; however, they need large training datasets. Unfortunately, preparing
large labeled datasets in ultrasound images is prohibitively difficult.
Therefore, in this study, we propose the use of simulated ultrasound (US)
images for training the U-Net deep learning segmentation architecture and test
on tissue-mimicking phantom data collected by an ultrasound machine. We
demonstrate that the trained architecture on the simulated data is
transferrable to real data, and therefore, simulated data can be considered as
an alternative training dataset when real datasets are not available. The
second contribution of this paper is that we train our U- Net network on
envelope and B-mode images of the simulated dataset, and test the trained
network on real envelope and B- mode images of phantom, respectively. We show
that test results are superior for the envelope data compared to B-mode image.Comment: Accepted in EMBC 201
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
In this contribution, we investigate the potential of hyperspectral data
combined with either simulated ground penetrating radar (GPR) or simulated
(sensor-like) soil-moisture data to estimate soil moisture. We propose two
simulation approaches to extend a given multi-sensor dataset which contains
sparse GPR data. In the first approach, simulated GPR data is generated either
by an interpolation along the time axis or by a machine learning model. The
second approach includes the simulation of soil-moisture along the GPR profile.
The soil-moisture estimation is improved significantly by the fusion of
hyperspectral and GPR data. In contrast, the combination of simulated,
sensor-like soil-moisture values and hyperspectral data achieves the worst
regression performance. In conclusion, the estimation of soil moisture with
hyperspectral and GPR data engages further investigations.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C)
2018 IEE
Likelihood Analysis of Cosmic Shear on Simulated and VIRMOS-DESCART Data
We present a maximum likelihood analysis of cosmological parameters from
measurements of the aperture mass up to 35 arcmin, using simulated and real
cosmic shear data. A four-dimensional parameter space is explored which
examines the mean density \Omega_M, the mass power spectrum normalization
\sigma_8, the shape parameter \Gamma and the redshift of the sources z_s.
Constraints on \Omega_M and \sigma_8 (resp. \Gamma and z_s) are then given by
marginalizing over \Gamma and z_s (resp. \Omega_M and \sigma_8). For a flat
LCDM cosmologies, using a photometric redshift prior for the sources and \Gamma
\in [0.1,0.4], we find \sigma_8=(0.57\pm0.04) \Omega_M^{(0.24\mp 0.18)
\Omega_M-0.49} at the 68% confidence level (the error budget includes
statistical noise, full cosmic variance and residual systematic). The estimate
of \Gamma, marginalized over \Omega_M \in [0.1,0.4], \sigma_8 \in [0.7,1.3] and
z_s constrained by photometric redshifts, gives \Gamma=0.25\pm 0.13 at 68%
confidence. Adopting h=0.7, a flat universe, \Gamma=0.2 and \Omega_m=0.3 we
find \sigma_8=0.98 \pm0.06 . Combined with CMB, our results suggest a non-zero
cosmological constant and provide tight constraints on \Omega_M and \sigma_8.
We finaly compare our results to the cluster abundance ones, and discuss the
possible discrepancy with the latest determinations of the cluster method. In
particular we point out the actual limitations of the mass power spectrum
prediction in the non-linear regime, and the importance for its improvement.Comment: 11 pages, submitted to A&
A database ATP simulated waveforms of shunt reactor switching cases with vacuum breakers on motor circuits
This paper presents a database ATP (Alternative Transient Program) simulated waveforms for shunt reactor switching cases with vacuum breakers in motor circuits following interruption of the starting current. The targeted objective is to provide multiple reignition simulated data for diagnostic and prognostic algorithms development, but also to help ATP users with practical study cases and component data compilation for shunt reactor switching. This method can be easily applied with different data for the different dielectric curves of circuit-breakers and networks. This paper presents design details, discusses some of the available cases and the advantages of such simulated data
Effects of Uncertainty in Climate Inputs on Simulated Evapotranspiration and Runoff in the Western Arctic
Hydrological models require accurate precipitation and air temperature inputs in order to adequately depict water fluxes and storages across Arctic regions. Biases such as gauge undercatch, as well as uncertainties in numerical weather prediction reanalysis data that propagate through water budget models, limit the ability to accurately model the terrestrial arctic water cycle. A hydrological model forced with three climate datasets and three methods of estimating potential evapotranspiration (PET) was used to better understand the impact of these processes on simulated water fluxes across the Western Arctic Linkage Experiment (WALE) domain. Climate data were drawn from the NCEPâNCAR reanalysis (NNR) (NCEP1), a modified version of the NNR (NCEP2), and the WillmottâMatsuura (WM) dataset. PET methods applied in the model were Hamon, PenmanâMonteith, and PenmanâMonteith using adjusted vapor pressure data.
High vapor pressures in the NNR lead to low simulated evapotranspiration (ET) in model runs using the PenmanâMonteith PET method, resulting in increased runoff. Annual ET derived from simulations using PenmanâMonteith PET was half the magnitude of ET simulated when the Hamon method was used. Adjustments made to the reanalysis vapor pressure data increased the simulated ET flux, reducing simulated runoff. Using the NCEP2 or WM climate data, along with the PenmanâMonteith PET function, results in agreement to within 7% between the simulated and observed runoff across the Yukon River basin. The results reveal the high degree of uncertainty present in climate data and the range of water fluxes generated from common model drivers. This suggests the need for thorough evaluations of model requirements and potential biases in forcing data, as well as corroborations with observed data, in all efforts to simulate arctic water balances
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