Disk Accretion onto Magnetized Neutron Stars: The Inner Disk Radius and Fastness Parameter

It is well known that the accretion disk around a magnetized compact star can penetrate inside the magnetospheric boundary, so the magnetospheric radius \ro does not represent the true inner edge \rin of the disk; but controversies exist in the literature concerning the relation between \ro and \rin. In the model of Ghosh & Lamb, the width of the boundary layer is given by \delta=\ro-\rin\ll\ro, or \rin\simeq\ro, while Li & Wickramasinghe recently argued that \rin could be significantly smaller than \ro in the case of a slow rotator. Here we show that if the star is able to absorb the angular momentum of disk plasma at \ro, appropriate for binary X-ray pulsars, the inner disk radius can be constrained by 0.8\lsim \rin/\ro\lsim 1, and the star reaches spin equilibrium with a relatively large value of the fastness parameter ($\sim 0.7-0.95$). For accreting neutron stars in low-mass X-ray binaries (LMXBs), \ro is generally close to the stellar radius \rs so that the toroidal field cannot transfer the spin-up torque efficiently to the star. In this case the critical fastness parameter becomes smaller, but \rin is still near \ro.Comment: 7 pages, 2 figures, to appear in Ap

Single-Shot Visualization Of Evolving Laser- Or Beam-Driven Plasma Wakefield Accelerators

We introduce Frequency-Domain Tomography (FDT) for visualizing sub-ps evolution of light-speed refractive index structures in a single shot. As a prototype demonstration, we produce single-shot tomographic movies of self-focusing, filamenting laser pulses propagating in a transparent Kerr medium. We then discuss how to adapt FDT to visualize evolving laser-or beam-driven plasma wakefields of current interest to the advanced accelerator community. For short (L similar to 1 cm), dense (n(e) similar to 10(19) cm(-3)) plasmas, the key challenge is broadening probe bandwidth sufficiently to resolve plasma-wavelength-size structures. For long (L similar to 10 to 100 cm), tenuous (n(e) similar to 10(17) cm(-3)) plasmas, probe diffraction from the evolving wake becomes the key challenge. We propose and analyze solutions to these challenges.Physic

Semiparametric estimation for a class of time-inhomogenous diffusion processes

Copyright @ 2009 Institute of Statistical Science, Academia SinicaWe develop two likelihood-based approaches to semiparametrically estimate a class of time-inhomogeneous diffusion processes: log penalized splines (P-splines) and the local log-linear method. Positive volatility is naturally embedded and this positivity is not guaranteed in most existing diffusion models. We investigate different smoothing parameter selections. Separate bandwidths are used for drift and volatility estimation. In the log P-splines approach, different smoothness for different time varying coefficients is feasible by assigning different penalty parameters. We also provide theorems for both approaches and report statistical inference results. Finally, we present a case study using the weekly three-month Treasury bill data from 1954 to 2004. We find that the log P-splines approach seems to capture the volatility dip in mid-1960s the best. We also present an application to calculate a financial market risk measure called Value at Risk (VaR) using statistical estimates from log P-splines

Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

Copyright [2006] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Tunable Circularly Polarized Terahertz Radiation from Magnetized Gas Plasma

It is shown, by simulation and theory, that circularly or elliptically polarized terahertz radiation can be generated when a static magnetic (B) field is imposed on a gas target along the propagation direction of a two-color laser driver. The radiation frequency is determined by $\sqrt{\omega_p^2+{\omega_c^2}/{4}} + {\omega_c}/{2}$, where $\omega_p$ is the plasma frequency and $\omega_c$ is the electron cyclotron frequency. With the increase of the B field, the radiation changes from a single-cycle broadband waveform to a continuous narrow-band emission. In high-B-field cases, the radiation strength is proportional to $\omega_p^2/\omega_c$. The B field provides a tunability in the radiation frequency, spectrum width, and field strength.Comment: 6 pages, 5 figure

Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

The official published version can found at the link below.Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean-square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three-gene network to illustrate the applicability and usefulness of the design.This work was funded by Royal Society of the U.K.; Foundation for the Author of National Excellent Doctoral Dissertation of China. Grant Number: 2007E4; Heilongjiang Outstanding Youth Science Fund of China. Grant Number: JC200809; Fok Ying Tung Education Foundation. Grant Number: 111064; International Science and Technology Cooperation Project of China. Grant Number: 2009DFA32050; University of Science and Technology of China Graduate Innovative Foundation

Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach

This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016