Stein meets Malliavin in normal approximation

Stein's method is a method of probability approximation which hinges on the solution of a functional equation. For normal approximation the functional equation is a first order differential equation. Malliavin calculus is an infinite-dimensional differential calculus whose operators act on functionals of general Gaussian processes. Nourdin and Peccati (2009) established a fundamental connection between Stein's method for normal approximation and Malliavin calculus through integration by parts. This connection is exploited to obtain error bounds in total variation in central limit theorems for functionals of general Gaussian processes. Of particular interest is the fourth moment theorem which provides error bounds of the order $\sqrt{\mathbb{E}(F_n^4)-3}$ in the central limit theorem for elements $\{F_n\}_{n\ge 1}$ of Wiener chaos of any fixed order such that $\mathbb{E}(F_n^2) = 1$. This paper is an exposition of the work of Nourdin and Peccati with a brief introduction to Stein's method and Malliavin calculus. It is based on a lecture delivered at the Annual Meeting of the Vietnam Institute for Advanced Study in Mathematics in July 2014.Comment: arXiv admin note: text overlap with arXiv:1404.478

Factorizations of Matrices Over Projective-free Rings

An element of a ring $R$ is called strongly $J^{\#}$-clean provided that it can be written as the sum of an idempotent and an element in $J^{\#}(R)$ that commute. We characterize, in this article, the strongly $J^{\#}$-cleanness of matrices over projective-free rings. These extend many known results on strongly clean matrices over commutative local rings

Compression of Deep Neural Networks on the Fly

Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods

Stein's method, Malliavin calculus, Dirichlet forms and the fourth moment theorem

The fourth moment theorem provides error bounds of the order $\sqrt{{\mathbb E}(F^4) - 3}$ in the central limit theorem for elements $F$ of Wiener chaos of any order such that ${\mathbb E}(F^2) = 1$. It was proved by Nourdin and Peccati (2009) using Stein's method and the Malliavin calculus. It was also proved by Azmoodeh, Campese and Poly (2014) using Stein's method and Dirichlet forms. This paper is an exposition on the connections between Stein's method and the Malliavin calculus and between Stein's method and Dirichlet forms, and on how these connections are exploited in proving the fourth moment theorem

The rate of period change in DAV stars

Grids of DAV star models are evolved by \texttt{WDEC}, taking the element diffusion effect into account. The grid parameters are hydrogen mass log($M_{H}/M_{*}$), helium mass log($M_{He}/M_{*}$), stellar mass $M_{\rm *}$, and effective temperature $T_{\rm eff}$ for DAV stars. The core compositions are from white dwarf models evolved by \texttt{MESA}. Therefore, those DAV star models evolved by \texttt{WDEC} have historically viable core compositions. Based on those DAV star models, we studied the rate of period change ($\dot{P}(k)$) for different values of H, He, $M_{\rm *}$, and $T_{\rm eff}$. The results are consistent with previous work. Two DAV stars G117-B15A and R548 have been observed around forty years. The rates of period change of two large-amplitude modes were obtained through O-C method. We did asteroseismological study on the two DAV stars and then obtained a best-fitting model for each star. Based on the two best-fitting models, the mode identifications ($l$, $k$) of the observed modes for G117-B15A and R548 are consistent with previous work. Both the observed modes and the observed $\dot{P}$s can be fitted by calculated ones. The results indicate that our method of evolving DAV star models is feasible.Comment: 20pages, 12 figures, 6 tables, accepted by RAA on 3/18, 201

Stein's method, Palm theory and Poisson process approximation

The framework of Stein's method for Poisson process approximation is presented from the point of view of Palm theory, which is used to construct Stein identities and define local dependence. A general result (Theorem \refimportantproposition) in Poisson process approximation is proved by taking the local approach. It is obtained without reference to any particular metric, thereby allowing wider applicability. A Wasserstein pseudometric is introduced for measuring the accuracy of point process approximation. The pseudometric provides a generalization of many metrics used so far, including the total variation distance for random variables and the Wasserstein metric for processes as in Barbour and Brown [Stochastic Process. Appl. 43 (1992) 9-31]. Also, through the pseudometric, approximation for certain point processes on a given carrier space is carried out by lifting it to one on a larger space, extending an idea of Arratia, Goldstein and Gordon [Statist. Sci. 5 (1990) 403-434]. The error bound in the general result is similar in form to that for Poisson approximation. As it yields the Stein factor 1/\lambda as in Poisson approximation, it provides good approximation, particularly in cases where \lambda is large. The general result is applied to a number of problems including Poisson process modeling of rare words in a DNA sequence.Comment: Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Probability (http://www.imstat.org/aop/) at http://dx.doi.org/10.1214/00911790400000002

Advances on creep–fatigue damage assessment in notched components

In this paper, the extended Direct Steady Cyclic Analysis method (eDSCA) within the Linear Matching Method Framework (LMMF) is combined with the Stress Modified Ductility Exhaustion method and the modified Cavity Growth Factor (CGF) for the first time. This new procedure is used to systematically investigate the effect of several load parameters including load level, load type and creep dwell duration on the creep–fatigue crack initiation process in a notched specimen. The results obtained are verified through a direct comparison with experimental results available in the literature demonstrating great accuracy in predicting the crack initiation life and the driving mechanisms. Furthermore, this extensive numerical study highlighted the possible detrimental effect of the creep–ratchetting mechanism on the crack growth process. This work has a significant impact on structural integrity assessments of complex industrial components and for the better understanding of creep–fatigue lab scale tests

Application of the linear matching method to creep-fatigue failure analysis of cruciform weldment manufactured of the austenitic steel AISI type 316N(L)

This paper demonstrates the recent extension of the Linear Matching Method (LMM) to include cyclic creep assessment [1] in application to a creep-fatigue analysis of a cruciform weldment made of the stainless steel AISI type 316N(L). The obtained results are compared with the results of experimental studies implemented by Bretherton et al. [2] with the overall objective to identify fatigue strength reduction factors (FSRF) of austenitic weldments for further design application. These studies included a series of strain-controlled tests at 550°C with different combinations of reversed bending moment and dwell time Δt. Five levels of reversed bending moment histories corresponding to defined values of total strain range Δεtot in remote parent material (1%, 0.6%, 0.4%, 0.3%, 0.25%) were used in combination with three variants of creep-fatigue conditions: pure fatigue, 1 hour and 5 hours of dwell period Δt of hold in tension. An overview of previous works devoted to analysis and simulation of these experiments [2] and highlight of the LMM development progress could be found in [3]

Systematic study of the symmetry energy coefficient in finite nuclei

The symmetry energy coefficients in finite nuclei have been studied systematically with a covariant density functional theory (DFT) and compared with the values calculated using several available mass tables. Due to the contamination of shell effect, the nuclear symmetry energy coefficients extracted from the binding energies have large fluctuations around the nuclei with double magic numbers. The size of this contamination is shown to be smaller for the nuclei with larger isospin value. After subtracting the shell effect with the Strutinsky method, the obtained nuclear symmetry energy coefficients with different isospin values are shown to decrease smoothly with the mass number $A$ and are subsequently fitted to the relation $\dfrac{4a_{\rm sym}}{A}=\dfrac{b_v}{A}-\dfrac{b_s}{A^{4/3}}$. The resultant volume $b_v$ and surface $b_s$ coefficients from axially deformed covariant DFT calculations are $121.73$ and $197.98$ MeV respectively. The ratio $b_s/b_v=1.63$ is in good agreement with the value derived from the previous calculations with the non-relativistic Skyrme energy functionals. The coefficients $b_v$ and $b_s$ corresponding to several available mass tables are also extracted. It is shown that there is a strong linear correlation between the volume $b_v$ and surface $b_s$ coefficients and the ratios $b_s/b_v$ are in between $1.6-2.0$ for all the cases.Comment: 16 pages, 6 figure