Spectral properties for the Laplacian of a generalized Wigner matrix

In this paper, we consider the spectrum of a Laplacian matrix, also known as Markov matrices where the entries of the matrix are independent but have a variance profile. Motivated by recent works on generalized Wigner matrices we assume that the variance profile gives rise to a sequence of graphons. Under the assumption that these graphons converge, we show that the limiting spectral distribution converges. We give an expression for the moments of the limiting measure in terms of graph homomorphisms. In some special cases, we identify the limit explicitly. We also study the spectral norm and derive the order of the maximum eigenvalue. We show that our results cover Laplacians of various random graphs including inhomogeneous Erdős–Rényi random graphs, sparse W-random graphs, stochastic block matrices and constrained random graphs.

Some patterned matrices with independent entries

Patterned random matrices such as the reverse circulant, the symmetric circulant, the Toeplitz and the Hankel matrices and their almost sure limiting spectral distribution (LSD), have attracted much attention. Under the assumption that the entries are taken from an i.i.d. sequence with finite variance, the LSD are tied together by a common thread — the [Formula: see text]th moment of the limit equals a weighted sum over different types of pair-partitions of the set [Formula: see text] and are universal. Some results are also known for the sparse case. In this paper, we generalize these results by relaxing significantly the i.i.d. assumption. For our models, the limits are defined via a larger class of partitions and are also not universal. Several existing and new results for patterned matrices, their band and sparse versions, as well as for matrices with continuous and discrete variance profile follow as special cases.

Characteristics of convective boundary layer and associated entrainment zone as observed by a ground-based polarization lidar

A tilted polarization lidar (TPL) with a pointing angle of 30° off zenith has been developed for continuous monitoring of the atmosphere with 10-s time and 6.5-m height resolution. From lidar-derived aerosol backscatter, instantaneous ABL depths are retrieved by logarithm gradient method (LGM) and Harr wavelet transform method (HWT), while hourly-mean ABL depths by variance method. A new FWHM method utilizing the full width at half maximum (FWHM) of the variance profile of aerosol backscatter ratio (ABR) fluctuations is proposed to determine the entrainment zone thickness (EZT). Both typical winter and summer clear-day observational cases are presented. It is concluded the convective boundary layer (CBL) evolution can be described by four stages. At the formation stage, the hourly-mean CBL depth grew slowly with a positive growth rate of 0.3 km/h. At the quasi-stationary stage, the hourly-mean CBL depth varied little and the corresponding growth rate changed sign with absolute value of 150 m, while the latter had respective percentages of 2.0 % and 31 % of EZT falling into the same corresponding subranges. Common statistical characteristics also existed for both cases. The growth stage always had the largest mean and stddev of EZT and the quasi-stationary stage usually the smallest stddev of EZT. For all four stages, most EZT values fell into the 50–150 m subrange; the overall percentages of EZT falling into the 50–150 m subrange between 0900 and 1900 LT were 84 % and 67 % for the winter and summer cases, respectively.

Freeness over the diagonal and outliers detection in deformed random matrices with a variance profile

We study the eigenvalue distribution of a Gaussian unitary ensemble (GUE) matrix with a variance profile that is perturbed by an additive random matrix that may possess spikes. Our approach is guided by Voiculescu’s notion of freeness with amalgamation over the diagonal and by the notion of deterministic equivalent. This allows to derive a fixed point equation to approximate the spectral distribution of certain deformed GUE matrices with a variance profile and to characterize the location of potential outliers in such models in a non-asymptotic setting. We also consider the singular values distribution of a rectangular Gaussian random matrix with a variance profile in a similar setting of additive perturbation. We discuss the application of this approach to the study of low-rank matrix denoising models in the presence of heteroscedastic noise, that is when the amount of variance in the observed data matrix may change from entry to entry. Numerical experiments are used to illustrate our results. Deformed random matrix, Variance profile, Outlier detection, Free probability, Freeness with amalgamation, Operator-valued Stieltjes transform, Gaussian spiked model, Low-rank model. 2000 Math Subject Classification: 62G05, 62H12.

Linear eigenvalue statistics of random matrices with a variance profile

We give an upper bound on the total variation distance between the linear eigenvalue statistic, properly scaled and centered, of a random matrix with a variance profile and the standard Gaussian random variable. The second-order Poincaré inequality-type result introduced in [S. Chatterjee, Fluctuations of eigenvalues and second order poincaré inequalities, Prob. Theory Rel. Fields 143(1) (2009) 1–40.] is used to establish the bound. Using this bound, we prove central limit theorem for linear eigenvalue statistics of random matrices with different kind of variance profiles. We re-establish some existing results on fluctuations of linear eigenvalue statistics of some well-known random matrix ensembles by choosing appropriate variance profiles.

Eigenvectors of a matrix under random perturbation

In this text, based on elementary computations, we provide a perturbative expansion of the coordinates of the eigenvectors of a Hermitian matrix of large size perturbed by a random matrix with small operator norm whose entries in the eigenvector basis of the first one are independent, centered, with a variance profile. This is done through a perturbative expansion of spectral measures associated to the state defined by a given vector.

Reynolds stress scaling in pipe flow turbulence—first results from CICLoPE

This paper reports the first turbulence measurements performed in the Long Pipe Facility at the Center for International Cooperation in Long Pipe Experiments (CICLoPE). In particular, the Reynolds stress components obtained from a number of straight and boundary-layer-type single-wire and X-wire probes up to a friction Reynolds number of 3.8×10 4 are reported. In agreement with turbulent boundary-layer experiments as well as with results from the Superpipe, the present measurements show a clear logarithmic region in the streamwise variance profile, with a Townsend–Perry constant of A 2 ≈1.26. The wall-normal variance profile exhibits a Reynolds-number-independent plateau, while the spanwise component was found to obey a logarithmic scaling over a much wider wall-normal distance than the other two components, with a slope that is nearly half of that of the Townsend–Perry constant, i.e. A 2, w ≈ A 2 /2. The present results therefore provide strong support for the scaling of the Reynolds stress tensor based on the attached-eddy hypothesis. Intriguingly, the wall-normal and spanwise components exhibit higher amplitudes than in previous studies, and therefore call for follow-up studies in CICLoPE, as well as other large-scale facilities. This article is part of the themed issue ‘Toward the development of high-fidelity models of wall turbulence at large Reynolds number’.