Harnack Inequalities for Stochastic Equations Driven by L\'evy Noise

By using coupling argument and regularization approximations of the underlying subordinator, dimension-free Harnack inequalities are established for a class of stochastic equations driven by a L\'evy noise containing a subordinate Brownian motion. The Harnack inequalities are new even for linear equations driven by L\'evy noise, and the gradient estimate implied by our log-Harnack inequality considerably generalizes some recent results on gradient estimates and coupling properties derived for L\'evy processes or linear equations driven by L\'evy noise. The main results are also extended to semi-linear stochastic equations in Hilbert spaces.Comment: 15 page

Perturbations of Functional Inequalities for L\'evy Type Dirichlet Forms

Perturbations of super Poincar\'e and weak Poincar\'e inequalities for L\'evy type Dirichlet forms are studied. When the range of jumps is finite our results are natural extensions to the corresponding ones derived earlier for diffusion processes; and we show that the study for the situation with infinite range of jumps is essentially different. Some examples are presented to illustrate the optimality of our results

The focusing of electron flow in a bipolar Graphene ribbon with different chiralities

The focusing of electron flow in a symmetric p-n junction (PNJ) of graphene ribbon with different chiralities is studied. Considering the PNJ with the sharp interface, in a armchair ribbon, the electron flow emitting from $(-L,0)$ in n-region can always be focused perfectly at $(L,0)$ in p-region in the whole Dirac fermion regime, i.e. in whole regime $E_0<t$ where $E_0$ is the distance between Dirac-point energy and Fermi energy and $t$ is the nearest hopping energy. For the bipolar ribbon with zigzag edge, however, the incoming electron flow in n-region is perfectly converged in p-region only in a very low energy regime with $E_0<0.05t$. Moreover, for a smooth PNJ, electrons are backscattered near PNJ, which weakens the focusing effect. But the focusing pattern still remains the same as that of the sharp PNJ. In addition, quantum oscillation in charge density occurs due to the interference between forward and backward scattering. Finally, in the presence of weak perpendicular magnetic field, charge carriers are deflected in opposite directions in the p-region and n-region. As a result, the focusing effect is smeared. The lower energy $E_0$, the easier the focusing effect is destroyed. For the high energy $E_0$ (e.g. $E_0=0.9t$), however, the focusing effect can still survive in a moderate magnetic field on order of one Tesla.Comment: 29 pages, 16 figure

Symmetry and transport property of spin current induced spin-Hall effect

We study the spin current induced spin-Hall effect that a longitudinal spin dependent chemical potential $qV_{s=x,y,z}$ induces a transverse spin conductances $G^{ss'}$. A four terminal system with Rashba and Dresselhaus spin-orbit interaction (SOI) in the scattering region is considered. By using Landauer-B$\ddot u$ttiker formula with the aid of the Green function, various spin current induced spin-Hall conductances $G^{ss'}$ are calculated. With the charge chemical potential $qV_c$ or spin chemical potential $qV_{s=x,y,z}$, there are 16 elements for the transverse conductances $G^{\mu \nu}_p=J_{p,\mu}/V_{\nu}$ where $\mu,\nu=x,y,z,c$. Due to the symmetry of our system these elements are not independent. For the system with $C_2$ symmetry half of elements are zero, when the center region only exists the Rashba SOI or Dresselhaus SOI. The numerical results show that of all the conductance elements, the spin current induced spin-Hall conductances $G^{ss'}$ are usually much greater (about one or two orders of magnitude) than the spin Hall conductances $G^{sc}$ and the reciprocal spin Hall conductances $G^{cs}$. So the spin current induced spin-Hall effect is dominating in the present device.Comment: 7 pages, 6 figure

Fast and Provable Algorithms for Spectrally Sparse Signal Reconstruction via Low-Rank Hankel Matrix Completion

A spectrally sparse signal of order $r$ is a mixture of $r$ damped or undamped complex sinusoids. This paper investigates the problem of reconstructing spectrally sparse signals from a random subset of $n$ regular time domain samples, which can be reformulated as a low rank Hankel matrix completion problem. We introduce an iterative hard thresholding (IHT) algorithm and a fast iterative hard thresholding (FIHT) algorithm for efficient reconstruction of spectrally sparse signals via low rank Hankel matrix completion. Theoretical recovery guarantees have been established for FIHT, showing that $O(r^2\log^2(n))$ number of samples are sufficient for exact recovery with high probability. Empirical performance comparisons establish significant computational advantages for IHT and FIHT. In particular, numerical simulations on $3$D arrays demonstrate the capability of FIHT on handling large and high-dimensional real data

DJpsiFDC: an event generator for the process gg→J/ψJ/ψgg\to J/\psi J/\psi at LHC

DJpsiFDC is an event generator package for the process $gg\to J/\psi J/\psi$. It generates events for primary leading-order $2\to 2$ processes. The package could generate a LHE document and this document could easily be embedded into detector simulation software frameworks. The package is produced in Fortran codes.Comment: 10 pages, 3 figure

Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling

We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.Comment: 5 pages, 2 figure