Convolution Powers Research Articles

Analytic results on the polymerisation random graph model

The step-growth polymerisation of a mixture of arbitrary-functional monomers is viewed as a time-continuos random graph process with degree bounds that are not necessarily the same for different vertices. The sequence of degree bounds acts as the only input parameter of the model. This parameter entirely defines the timing of the phase transition. Moreover, the size distribution of connected components features a rich temporal dynamics that includes: switching between exponential and algebraic asymptotes and acquiring oscillations. The results regarding the phase transition and the expected size of a connected component are obtained in a closed form. An exact expression for the size distribution is resolved up to the convolution power and is computable in subquadratic time. The theoretical results are illustrated on a few special cases, including a comparison with Monte Carlo simulations.

Convolution Powers of Salem Measures With Applications

AbstractWe study the regularity of convolution powers for measures supported on Salemsets, and prove related results on Fourier restriction and Fourier multipliers. In particular we show that for α of the form d/n, n = 2, 3, … there exist α-Salem measures for which the L2Fourier restriction theorem holds in the range. The results rely on ideas of Körner. We extend some of his constructions to obtain upper regular α-Salem measures, with sharp regularity results forn-fold convolutions for all n ∈ ℕ.

A note on estimating cumulative distribution functions by the use of convolution power kernels

Our paper investigates the nonparametric estimation of cumulative distribution functions of nonnegative valued random variables using convolution power kernels. Our proposed consistent estimator avoids boundary effects near the origin. We present its asymptotic properties and give a short simulation study.

Analysis for Functional MRI Data Based on Improved Convolution Power Spectrum

Analysis for Functional MRI Data Based on Improved Convolution Power Spectrum

Convolution in perfect Lie groups

AbstractLetGbe a connected perfect real Lie group. We show that there exists α < dimGandp∈$\mathbb{N}$* such that if μ is a compactly supported α-Frostman Borel measure onG, then thepth convolution power μ*pis absolutely continuous with respect to the Haar measure onG, with arbitrarily smooth density. As an application, we obtain that ifA⊂Gis a Borel set with Hausdorff dimension at least α, then thep-fold product setApcontains a non-empty open set.

Fundamental solution of the Laplacian on flat tori and boundary value problems for the planar Poisson equation in rectangles

The fundamental solution of the Laplacian on flat tori is obtained using Eisenstein’s approach to elliptic functions via infinite series over lattices in the complex plane. Most boundary value problems stated for the planar Poisson equation in a rectangle for which series-only representations of solution were known, may thus be solved explicitly in closed-form using the method of images. Moreover, the fundamental solution of n-Laplacian on flat tori may also be simply derived by a convolution power. PACS Codes: 02.30.Em, 02.30.Jr.

A Fourier restriction theorem based on convolution powers

We prove a Fourier restriction estimate under the assumption that certain convolution power of the measure admits an $r$-integrable density.

On a Plug-In Wavelet Estimator for Convolutions of Densities

The nonparametric estimation of the m-fold convolution power of an unknown function f is considered. We introduce an estimator based on a plug-in approach and a wavelet hard thresholding estimator. We explore its theoretical asymptotic performances via the mean integrated squared error, assuming that f has a certain degree of smoothness. Applications and numerical examples are given for the standard density estimation problem and the deconvolution density estimation problem.

On operator-valued free convolution powers

We give an explicit realization of the $\eta$-convolution power of an $A$-valued distribution, as defined earlier by Anshelevich, Belinschi, Fevrier and Nica. If $\eta:A\to A$ is completely positive and $\eta\geq\operatorname{id}$, we give a short proof of positivity of the $\eta$-convolution power of a positive distribution. Conversely, if $\eta\not\geq\operatorname{id}$, and $s$ is large enough, we construct an $s$-tuple whose $A$-valued distribution is positive, but has non-positive $\eta$-convolution power.

Convolution Powers of the Identity

We study convolution powers $\mathtt{id}^{\ast n}$ of the identity of graded connected Hopf algebras $H$. (The antipode corresponds to $n=-1$.) The chief result is a complete description of the characteristic polynomial - both eigenvalues and multiplicity - for the action of the operator $\mathtt{id}^{\ast n}$ on each homogeneous component $H_m$. The multiplicities are independent of $n$. This follows from considering the action of the (higher) Eulerian idempotents on a certain Lie algebra $\mathfrak{g}$ associated to $H$. In case $H$ is cofree, we give an alternative (explicit and combinatorial) description in terms of palindromic words in free generators of $\mathfrak{g}$. We obtain identities involving partitions and compositions by specializing $H$ to some familiar combinatorial Hopf algebras. Nous étudions les puissances de convolution $\mathtt{id}^{\ast n}$ de l’identité d’une algèbre de Hopf graduée et connexe $H$ quelconque. (L’antipode correspond à $n=-1$.) Le résultat principal est une description complète du polynôme caractéristique (des valeurs propres et de leurs multiplicités) de l’opérateur $\mathtt{id}^{\ast n}$ agissant sur chaque composante homogène $H_m$. Les multiplicités sont indépendants de $n$. Ceci résulte de l’examen de l’action des idempotents eulériens (supérieures) sur une algèbre de Lie $\mathfrak{g}$ associée à $H$. Dans le cas où $H$ est colibre, nous donnons une description alternative (explicite et combinatoire) en termes de mots palindromes dans les générateurs libres de $\mathfrak{g}$. Nous obtenons des identités impliquant des partitions et compositions en choisissant comme $H$ certaines algèbres de Hopf combinatoires connues.

Multi-Fractal Analysis of Convolution Powers of Measures

We investigate the multi-fractal analysis of (large) convolution powers of probability measures on \(\mathbb{R}\). If the measure \(\mu \) satisfies \((N)\) supp\(\mu =[0,N]\) for some \(N\), then under weak assumptions there is an isolated point in the multi-fractal spectrum of \(\mu ^{n}\) for sufficiently large \(n\). A formula is found for the limiting behaviour (as \(n\rightarrow \infty \)) of the \(L^{q}\)-spectrum of \(\mu ^{n}\) and this is related to the limit of the energy dimension of \(\mu ^{n}\) when \(q\geq 1\).

Convolution powers in the operator-valued framework

We consider the framework of an operator-valued noncommutative probability space over a unital C*-algebra B. We show how for a B-valued distribution \mu one can define convolution powers with respect to free additive convolution and with respect to Boolean convolution, where the exponent considered in the power is a suitably chosen linear map \eta from B to B, instead of being a non-negative real number. More precisely, the Boolean convolution power is defined whenever \eta is completely positive, while the free additive convolution power is defined whenever \eta - 1 is completely positive (where 1 stands for the identity map on B). In connection to these convolution powers we define an evolution semigroup related to the Boolean Bercovici-Pata bijection. We prove several properties of this semigroup, including its connection to the B-valued free Brownian motion. We also obtain two results on the operator-valued analytic function theory related to the free additive convolution powers with exponent \eta. One of the results concerns analytic subordination for B-valued Cauchy-Stieltjes transforms. The other gives a B-valued version of the inviscid Burgers equation, which is satisfied by the Cauchy-Stieltjes transform of a B-valued free Brownian motion.

Convolution Power Spectrum Analysis for fMRI Data Based on Prior Image Signal

Functional MRI (fMRI) data-processing methods based on changes in the time domain involve, among other things, correlation analysis and use of the general linear model with statistical parametric mapping (SPM). Unlike conventional fMRI data analysis methods, which aim to model the blood-oxygen-level-dependent (BOLD) response of voxels as a function of time, the theory of power spectrum (PS) analysis focuses completely on understanding the dynamic energy change of interacting systems. We propose a new convolution PS (CPS) analysis of fMRI data, based on the theory of matched filtering, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the PS density analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical PS SPM and the support vector machine methods. Our results demonstrate that the CPS method is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.

Random walks on quasisymmetric functions

Conditions are provided under which an endomorphism on quasisymmetric functions gives rise to a left random walk on the descent algebra which is also a lumping of a left random walk on permutations. Spectral results are also obtained. Several important random walks are now realized this way: Stanley's QS-distribution results from endomorphisms given by evaluation maps, a-shuffles result from the ath convolution power of the universal character, and the Tchebyshev operator of the second kind introduced recently by Ehrenborg and Readdy yields traditional riffle shuffles. A conjecture of Ehrenborg regarding the spectra for a family of random walks on ab-words is proven. A theorem of Stembridge from the theory of enriched P-partitions is also recovered as a special case.

On some results of Cufaro Petroni about Student t-processes

This paper deals with Student t-processes as studied in Cufaro Petroni (2007 J. Phys. A. Math. Theor. 40 2227–50). We prove and extend some conjectures expressed by Cufaro Petroni about the asymptotical behavior of a Student t-process and the expansion of its density. First, the explicit asymptotic behavior of any real positive convolution power of a Student t-density with any real positive degrees of freedom is given in the multivariate case; then the integer convolution power of a Student t-distribution with odd degrees of freedom is shown to be a convex combination of Student t-densities with odd degrees of freedom. At last, we show that this result does not extend to the case of non-integer convolution powers.

A NOTE ON THE CLOSURE OF CONVOLUTION POWER MIXTURES (RANDOM SUMS) OF EXPONENTIAL DISTRIBUTIONS

AbstractWe make a correction to an important result by Cline [D. B. H. Cline, ‘Convolutions of distributions with exponential tails’, J. Austral. Math. Soc. (Series A)43 (1987), 347–365; D. B. H. Cline, ‘Convolutions of distributions with exponential tails: corrigendum’, J. Austral. Math. Soc. (Series A)48 (1990), 152–153] on the closure of the exponential class $\mathcal {L}(\alpha )$ under convolution power mixtures (random summation).

Dissipation of Convolution Powers in a Metric Group

In contrast to what is known about probability measures on locally compact groups, a metric group G can support a probability measure μ which is not carried on a compact subgroup but for which there exists a compact subset C⊆G such that the sequence μn(C) fails to converge to zero as n tends to ∞. A noncompact metric group can also support a probability measure μ such that supp μ=G and the concentration functions of μ do not converge to zero. We derive a number of conditions which guarantee that the concentration functions in a metric group G converge to zero, and obtain a sufficient and necessary condition in order that a probability measure μ on G satisfy lim n→∞μn(C)=0 for every compact subset C⊆G.

Solutions to arithmetic convolution equations

In the C-algebra A of arithmetic functions g: N → C, endowed with the usual pointwise linear operations and the Dirichlet convolution, let g *k denote the convolution power g*···*g with k factors g ∈ A. We investigate the solvability of polynomial equations of the form a d *g *d + a d-1 * g *(d-1) + ··· + a 1 * g + a 0 = 0 with fixed coefficients a d , a d-1 ,···, a 1 ,a 0 ∈ A. In some cases the solutions have specific properties and can be determined explicitly. We show that the property of the coefficients to belong to convergent Dirichlet series transfers to those solutions g ∈ A, whose values g(1) are simple zeros of the polynomial a d (1)z d + a d-1 (1)z d-1 + ···+ a 1 (1)z + a 0 (1). We extend this to systems of convolution equations, which need not be of polynomial-type.

Multivariate subexponential distributions and random sums of random vectors

Let F(x) denote a distribution function in Rd and let F*n(x) denote the nth convolution power of F(x). In this paper we discuss the asymptotic behaviour of 1 - F*n(x) as x tends to ∞ in a certain prescribed way. It turns out that in many cases 1 - F*n(x) ∼ n(1 - F(x)). To obtain results of this type, we introduce and use a form of subexponential behaviour, thereby extending the notion of multivariate regular variation. We also discuss subordination, in which situation the index n is replaced by a random index N.

Some Conditions for Decay of Convolution Powers and Heat Kernels on Groups

AbstractLet K be a function on a unimodular locally compact group G, and denote by Kn = K * K * · · · * K the n-th convolution power of K. Assuming that K satisfies certain operator estimates in L2(G), we give estimates of the norms and for large n. In contrast to previous methods for estimating , we do not need to assume that the function K is a probability density or nonnegative. Our results also adapt for continuous time semigroups on G. Various applications are given, for example, to estimates of the behaviour of heat kernels on Lie groups.