Search results for "Probability"
showing 10 items of 3417 documents
ConvLSTM Neural Networks for seismic event prediction in Chile
2021
Predicting seismic risk is a challenging task in order to avoid catastrophic effects. In this work, two models based on Convolutional Network (CNN) and Long Short Term Memory (LSTM) networks are proposed to predict the seismic risk in Chile. In particular, a ConvLSTM and a Multi-column ConvLSTM network are used for the prediction of the average number of seismic events greater than 2,8 magnitude on the Richter scale, in the Chilean regions of Coquimbo and Araucania between the years 2010 and 2017. For this model, the values of the intensity function estimated through an ETAS model and the accumulated displacement prior to a the seismic events are used as inputs. In particular, given the spa…
Deep Vein Thrombosis
2015
Venous thromboembolism (VTE) comprises deep vein thrombosis (DVT) and pulmonary embolism (PE). DVT occurs at an incidence of 1/1,000 and risk factors include immobilization, hospitalization, surgery, thrombophilia and positive family history, cancer, pregnancy, and other hormonal effects. Commonly, clinical signs and symptoms for DVT are unreliable, especially in hospitalized patients, but the clinical assessment of the pretest probability, for example, with the Wells score, is an important component in the diagnostic algorithm, where compression ultrasound also plays a central role. Treatment of DVT aims to acutely prevent PE and short-term and long-term VTE recurrence and to avoid the lon…
Absolute kinematics of radio-source components in the complete S5 polar cap sample: IV. Proper motions of the radio cores over a decade and spectral …
2016
We have carried out a high-precision astrometric analysis of two very-long-baseline-interferometry (VLBI) epochs of observation of the 13 extragalactic radio sources in the complete S5 polar cap sample. The VLBI epochs span a time baseline of ten years and enable us to achieve precisions in the proper motions of the source cores up to a few micro-arcseconds per year. The observations were performed at 14.4 GHz and 43.1 GHz, and enable us to estimate the frequency core-shifts in a subset of sources, for which the spectral-index distributions can be computed. We study the source-position stability by analysing the changes in the relative positions of fiducial source points (the jet cores) ove…
Robust Neutrino Constraints by Combining Low Redshift Observations with the CMB
2009
We illustrate how recently improved low-redshift cosmological measurements can tighten constraints on neutrino properties. In particular we examine the impact of the assumed cosmological model on the constraints. We first consider the new HST H-0 = 74.2 +/- 3.6 measurement by Riess et al. (2009) and the sigma(8)(Omega(m)/0.25)(0.41) = 0.832 +/- 0.033 constraint from Rozo et al. (2009) derived from the SDSS maxBCG Cluster Catalog. In a ACDM model and when combined with WMAP5 constraints, these low-redshift measurements constrain Sigma m(v) < 0.4 eV at the 95% confidence level. This bound does not relax when allowing for the running of the spectral index or for primordial tensor perturbations…
Neutrino masses and their ordering: global data, priors and models
2018
We present a Bayesian analysis of the combination of current neutrino oscillation, neutrinoless double beta decay and CMB observations. Our major goal is to carefully investigate the possibility to single out one neutrino mass ordering, Normal Ordering or Inverted Ordering, with current data. Two possible parametrizations (three neutrino masses versus the lightest neutrino mass plus the two oscillation mass splittings) and priors (linear versus logarithmic) are examined. We find that the preference for NO is only driven by neutrino oscillation data. Moreover, the values of the Bayes factor indicate that the evidence for NO is strong only when the scan is performed over the three neutrino ma…
Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm
2012
We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.
Constraints on the Coupling between Axionlike Dark Matter and Photons Using an Antiproton Superconducting Tuned Detection Circuit in a Cryogenic Penn…
2021
We constrain the coupling between axionlike particles (ALPs) and photons, measured with the superconducting resonant detection circuit of a cryogenic Penning trap. By searching the noise spectrum of our fixed-frequency resonant circuit for peaks caused by dark matter ALPs converting into photons in the strong magnetic field of the Penning-trap magnet, we are able to constrain the coupling of ALPs with masses around $2.7906-2.7914\,\textrm{neV/c}^2$ to $g_{a\gamma}< 1 \times 10^{-11}\,\textrm{GeV}^{-1}$. This is more than one order of magnitude lower than the best laboratory haloscope and approximately 5 times lower than the CERN axion solar telescope (CAST), setting limits in a mass and cou…
Asynchronous L1 control of delayed switched positive systems with mode-dependent average dwell time
2014
Abstract This paper investigates the stability and asynchronous L 1 control problems for a class of switched positive linear systems (SPLSs) with time-varying delays by using the mode-dependent average dwell time (MDADT) approach. By allowing the co-positive type Lyapunov–Krasovskii functional to increase during the running time of active subsystems, a new stability criterion for the underlying system with MDADT is first derived. Then, the obtained results are extended to study the issue of asynchronous L 1 control, where “asynchronous” means that the switching of the controllers has a lag with respect to that of system modes. Sufficient conditions are provided to guarantee that the resulti…
Retrieval of atmospheric CH4profiles from Fourier transform infrared data using dimension reduction and MCMC
2016
We introduce an inversion method that uses dimension reduction for the retrieval of atmospheric methane (CH4) profiles. Uncertainty analysis is performed using the Markov chain Monte Carlo (MCMC) statistical estimation. These techniques are used to retrieve CH4 profiles from the ground-based spectral measurements by the Fourier Transform Spectrometer (FTS) instrument at Sodankyla (67.4 degrees N, 26.6 degrees E), Northern Finland. The Sodankyla FTS is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the data provides approximately 3 degrees of freedom about the vertical struc…
Improving spatial temperature estimates by resort to time autoregressive processes
2012
Temperature estimation methods usually involve regression followed by kriging of residuals (residual kriging). Despite the performance of such models, there is invariably a residual which is not necessarily unpredictable because it may still be correlated in time. We set out to analyse such residuals through resort to autoregressive processes. It is shown that the optimal period varies depending on whether it is identified by functions of the form resd = f(resd−1, resd−2, ..., resd−p) or by partial correlations. Autoregressive processes significantly improve estimates, which are evaluated by cross-validations. Finally, the two following points are discussed: (1) the assumptions of the autor…