247 research outputs found
Integration on manifolds by mapped low-discrepancy points and greedy minimal ks -energy points
Constraining the Nordtvedt parameter with the BepiColombo Radioscience experiment
BepiColombo is a joint ESA/JAXA mission to Mercury with challenging
objectives regarding geophysics, geodesy and fundamental physics. The Mercury
Orbiter Radioscience Experiment (MORE) is one of the on-board experiments,
including three different but linked experiments: gravimetry, rotation and
relativity. The aim of the relativity experiment is the measurement of the
post-Newtonian parameters. Thanks to accurate tracking between Earth and
spacecraft, the results are expected to be very precise. However, the outcomes
of the experiment strictly depends on our "knowledge" about solar system:
ephemerides, number of bodies (planets, satellites and asteroids) and their
masses. In this paper we describe a semi-analytic model used to perform a
covariance analysis to quantify the effects, on the relativity experiment, due
to the uncertainties of solar system bodies parameters. In particular, our
attention is focused on the Nordtvedt parameter used to parametrize the
strong equivalence principle violation. After our analysis we estimated
which is about 1~order of magnitude
larger than the "ideal" case where masses of planets and asteroids have no
errors. The current value, obtained from ground based experiments and lunar
laser ranging measurements, is .
Therefore, we conclude that, even in presence of uncertainties on solar system
parameters, the measurement of by MORE can improve the current precision
of about 1~order of magnitude
Photometric determination of the mass accretion rates of pre-main sequence stars. VI. The case of LH 95 in the Large Magellanic Cloud
We report on the accretion properties of low-mass stars in the LH95
association within the Large Magellanic Cloud (LMC). Using non-contemporaneous
wide-band and narrow-band photometry obtained with the HST, we identify 245
low-mass pre-main sequence (PMS) candidates showing H excess emission
above the 4 level. We derive their physical parameters, i.e. effective
temperatures, luminosities, masses (), ages, accretion luminosities,
and mass accretion rates (). We identify two different
stellar populations: younger than ~8Myr with median ~5.4x10/yr (and ~0.15-1.8) and older than
~8Myr with median ~4.8x10/yr (and
~0.6-1.2). We find that the younger PMS candidates are
assembled in groups around Be stars, while older PMS candidates are uniformly
distributed within the region without evidence of clustering. We find that
in LH95 decreases with time more slowly than what is
observed in Galactic star-forming regions (SFRs). This agrees with the recent
interpretation according to which higher metallicity limits the accretion
process both in rate and duration due to higher radiation pressure. The relationship shows different behaviour at different ages,
becoming progressively steeper at older ages, indicating that the effects of
mass and age on cannot be treated independently. With the
aim to identify reliable correlations between mass, age, and , we used for our PMS candidates a multivariate linear regression fit
between these parameters. The comparison between our results with those
obtained in other SFRs of our Galaxy and the MCs confirms the importance of the
metallicity for the study of the evolution in clusters with
different environmental conditions.Comment: Accepted for publication in ApJ; 26 pages, 12 pages, 3 tables;
abstract shortened. Fixed a typo in the name of a co-autho
Quasi-Monte Carlo integration on manifolds with mapped low-discrepancy points and greedy minimal Riesz s-energy points
In this paper we consider two sets of points for Quasi-Monte Carlo integration on two- dimensional manifolds. The first is the set of mapped low-discrepancy sequence by a measure preserving map, from a rectangle U⊂R2 to the manifold. The second is the greedy minimal Riesz s-energy points extracted from a suitable discretization of the manifold. Thanks to the Poppy-seed Bagel Theorem we know that the classes of points with minimal Riesz s-energy, under suitable assumptions, are asymptotically uniformly distributed with respect to the normalized Hausdorff measure. They can then be considered as quadrature points on manifolds via the Quasi-Monte Carlo (QMC) method. On the other hand, we do not know if the greedy minimal Riesz s-energy points are a good choice to integrate functions with the QMC method on manifolds. Through theoretical considerations, by showing some properties of these points and by numerical experiments, we attempt to answer to these questions
A new quasi-monte carlo technique based on nonnegative least squares and approximate Fekete points
The computation of integrals in higher dimensions and on general domains, when no explicit cubature rules are known, can be ”easily” addressed by means of the quasi- Monte Carlo method. The method, simple in its formulation, becomes computationally inefficient when the space dimension is growing and the integration domain is particularly complex. In this paper we present two new approaches to the quasi-Monte Carlo method for cubature based on nonnegative least squares and approximate Fekete points. The main idea is to use less points and especially good points for solving the system of the moments. Good points are here intended as points with good interpolation properties, due to the strict connection between interpolation and cubature. Numerical experiments show that, in average, just a tenth of the points should be used mantaining the same approximation order of the quasi-Monte Carlo method. The method has been satisfactory applied to 2 and 3-dimensional problems on quite complex domains
More properties of -Chebyshev functions and points
Recently, -Chebyshev functions, as well as the corresponding
zeros, have been introduced as a generalization of classical Chebyshev
polynomials of the first kind and related roots. They consist of a family of
orthogonal functions on a subset of , which indeed satisfies a
three-term recurrence formula. In this paper we present further properties,
which are proven to comply with various results about classical orthogonal
polynomials. In addition, we prove a conjecture concerning the Lebesgue
constant's behavior related to the roots of -Chebyshev
functions in the corresponding orthogonality interval
Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization
The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations
Tesi che Giacomo Marchi di Udine si propone di difendere nell' occasione della pubblica sua promozione alla laurea dottorale in ambe le leggi nell' Imp. Reg. Università di Padova nel mese di Agosto del 1840
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