Relativistic Winds from Compact Gamma-ray Sources: I. Radiative Acceleration in the Klein-Nishina Regime

We consider the radiative acceleration to relativistic bulk velocities of a cold, optically thin plasma which is exposed to an external source of gamma-rays. The flow is driven by radiative momentum input to the gas, the accelerating force being due to Compton scattering in the relativistic Klein-Nishina limit. The bulk Lorentz factor of the plasma, Gamma, derived as a function of distance from the radiating source, is compared with the corresponding result in the Thomson limit. Depending on the geometry and spectrum of the radiation field, we find that particles are accelerated to the asymptotic Lorentz factor at infinity much more rapidly in the relativistic regime; and the radiation drag is reduced as blueshifted, aberrated photons experience a decreased relativistic cross section and scatter preferentially in the forward direction. The random energy imparted to the plasma by gamma-rays can be converted into bulk motion if the hot particles execute many Larmor orbits before cooling. This `Compton afterburn' may be a supplementary source of momentum if energetic leptons are injected by pair creation, but can be neglected in the case of pure Klein-Nishina scattering. Compton drag by side-scattered radiation is shown to be more important in limiting the bulk Lorentz factor than the finite inertia of the accelerating medium. The processes discussed here may be relevant to a variety of astrophysical situations where luminous compact sources of hard X- and gamma-ray photons are observed, including active galactic nuclei, galactic black hole candidates, and gamma-ray bursts.Comment: LateX, 20 pages, 5 figures, revised version accepted for publication in the Ap

Categorification of the Kauffman bracket skein module of I-bundles over surfaces

Khovanov defined graded homology groups for links L in R^3 and showed that their polynomial Euler characteristic is the Jones polynomial of L. Khovanov's construction does not extend in a straightforward way to links in I-bundles M over surfaces F not D^2 (except for the homology with Z/2 coefficients only). Hence, the goal of this paper is to provide a nontrivial generalization of his method leading to homology invariants of links in M with arbitrary rings of coefficients. After proving the invariance of our homology groups under Reidemeister moves, we show that the polynomial Euler characteristics of our homology groups of L determine the coefficients of L in the standard basis of the skein module of M. Therefore, our homology groups provide a `categorification' of the Kauffman bracket skein module of M. Additionally, we prove a generalization of Viro's exact sequence for our homology groups. Finally, we show a duality theorem relating cohomology groups of any link L to the homology groups of the mirror image of L.Comment: Version 2 was obtained by merging math.QA/0403527 (now removed) with Version 1. This version is published by Algebraic and Geometric Topology at http://www.maths.warwick.ac.uk/agt/AGTVol4/agt-4-52.abs.htm

Zinc ferrite nanoparticles as perspective functional materials for applications in casting technologies

In this article it discuss on possible application of magnetic oxide nanoparticles, namely non-stoichiometric zinc ferrite nanoparticles as a functionalizing agent in foundry processes. Thermal analysis showed a weight loss of the sample at 1 273 K in an amount of 7,7 %, which is a result of the following processes taking place in different temperature ranges. Upon its thermal treatment Zn0,4Fe2,6O4 decomposes to zinc oxide and iron (III) oxide (first stage) and next to iron (II,III) oxide and oxygen (second stage). The degree of decomposition was expressed as Fe2+ / Fetotal. Mössbauer spectroscopy showed that the over 30 % of Fe3+ present in starting material was reduced to Fe2+

Content-Based Video Description for Automatic Video Genre Categorization

International audienceIn this paper, we propose an audio-visual approach to video genre categorization. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal structural level, we asses action contents with respect to human perception. Further, color perception is quantified with statistics of color distribution, elementary hues, color properties and relationship of color. The last category of descriptors determines statistics of contour geometry. An extensive evaluation of this multi-modal approach based on more than 91 hours of video footage is presented. We obtain average precision and recall ratios within [87% − 100%] and [77% − 100%], respectively,nwhile average correct classification is up to 97%. Additionally, movies displayed according to feature-based coordinates in a virtual 3D browsing environment tend to regroup with respect to genre, which has potential application with real content-based browsing systems

Probabilistic properties of detrended fluctuation analysis for Gaussian processes

Detrended fluctuation analysis (DFA) is one of the most widely used tools for the detection of long-range dependence in time series. Although DFA has found many interesting applications and has been shown to be one of the best performing detrending methods, its probabilistic foundations are still unclear. In this paper, we study probabilistic properties of DFA for Gaussian processes. Our main attention is paid to the distribution of the squared error sum of the detrended process. We use a probabilistic approach to derive general formulas for the expected value and the variance of the squared fluctuation function of DFA for Gaussian processes. We also get analytical results for the expected value of the squared fluctuation function for particular examples of Gaussian processes, such as Gaussian white noise, fractional Gaussian noise, ordinary Brownian motion, and fractional Brownian motion. Our analytical formulas are supported by numerical simulations. The results obtained can serve as a starting point for analyzing the statistical properties of DFA-based estimators for the fluctuation function and long-memory parameter