On Berry--Esseen bounds for non-instantaneous filters of linear processes

Let $X_n=\sum_{i=1}^{\infty}a_i\epsilon_{n-i}$, where the $\epsilon_i$ are i.i.d. with mean 0 and at least finite second moment, and the $a_i$ are assumed to satisfy $|a_i|=O(i^{-\beta})$ with $\beta >1/2$. When $1/2<\beta<1$, $X_n$ is usually called a long-range dependent or long-memory process. For a certain class of Borel functions $K(x_1,...,x_{d+1})$, $d\ge0$, from ${\mathcal{R}}^{d+1}$ to $\mathcal{R}$, which includes indicator functions and polynomials, the stationary sequence $K(X_n,X_{n+1},...,X_{n+d})$ is considered. By developing a finite orthogonal expansion of $K(X_n,...,X_{n+d})$, the Berry--Esseen type bounds for the normalized sum $Q_N/\sqrt{N},Q_N=\sum_{n=1}^N(K(X_ n,...,X_{n+d})-\mathrm{E}K(X_n,...,X_{n+d}))$ are obtained when $Q_N/\sqrt{N}$ obeys the central limit theorem with positive limiting variance.Comment: Published in at http://dx.doi.org/10.3150/07-BEJ112 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Nanomechanical Properties and Buckling Instability of Plasma Induced Damaged Layer on Polystyrene

In this thesis we report on an investigation of an elastic buckling instability as a driving force for the roughening of polystyrene, a model resist, during Ar+ plasma etching. Polystyrene films etched by pure Ar+ plasma with different ion energies were characterized using both atomic force microscopy topography and force curve measurements. By using height-height correlation function in analyzing the AFM measured topography images, we find that surface corrugation of etched polystyrene film surfaces all display a dominant wrinkle wavelength (ë), which is a function of ion energy. Next, we characterized the mechanical properties of these samples using AFM force curve measurements in an controlled ambient environment. We analyzed the measured force curves using a systematic algorithm based on statistical fitting procedures, and taking into account the adhesive interaction, in order to determine the effective elastic modulus of the films. We find that the effective elastic modulus (EBL) of the etched samples increases monotonically with increasing ion energy, but the changes are rather subtle as compared to the elastic modulus (EPS) of the unetched one. In order to test the validity of a buckling instability as the mechanism for surface roughening in our polystyrene-Ar plasma system, the elastic modulus of individual layer (i.e. ion-damaged layer plus unmodified foundation) needs to be determined. We present a determination of the damaged layer elastic modulus (EDL) from the effective elastic modulus of the damaged layer/polystyrene bilayer structure (EBL), based upon a finite element method simulation taking into account the thickness and elastic modulus of the damaged layers. We extract the damaged layer elastic modulus versus etching ion energy initially within the approximation of a spherical tip in contact with a flat sample surface. We next extend our model, by considering a periodic corrugated film surface, with its amplitude and wavelength determined by AFM, to take into account the effect of roughness induced by plasma exposure. The damaged layer elastic modulus extracted from these two approximations gives of quantitative agreement, and thus evidence for the correlation between buckling instability and plasma-induced roughening

On the second order of Zeta functional equations for Riemann Type

This paper discuss a new class of functional equations by using both Poisson summation formula and Jacobi type theta a function. The class of Riemann type functional equations are derived from self-reciprocal probability density functions. Finally, the second order Zeta functional equations for Riemann type is also investigated.Comment: 13 page

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

Influence of electrode thermal conductivity on resistive switching behavior during reset process

Resistive random access memory (RRAM) is the most promising candidate for non-volatile memory (NVM) due to its extremely low operation voltage, extremely fast write/erase speed, and excellent scaling capability. However, an obstacle hindering mass production of RRAM is the non-uniform physical mechanism in its resistance switching process. This study examines the influence of different electrode thermal conductivity on switching behavior during the reset process. Electrical analysis methods and an analysis of current conduction mechanism indicate that better thermal conductivity in the electrode will require larger input power in order to induce more active oxygen ions to take part in the reset process. More active oxygen ions cause a more complete reaction during the reset process, and cause the effective switching gap (dsw) to become thicker. The effect of the electrode thermal conductivity and input power are explained by our model and clarified by electrical analysis methods. Please click Additional Files below to see the full abstract