Electron-positron energy deposition rate from neutrino pair annihilation on the rotation axis of neutron and quark stars

We investigate the deposition of energy due to the annihilations of neutrinos and antineutrinos on the rotation axis of rotating neutron and quark stars, respectively. The source of the neutrinos is assumed to be a neutrino-cooled accretion disk around the compact object. Under the assumption of the separability of the neutrino null geodesic equation of motion we obtain the general relativistic expression of the energy deposition rate for arbitrary stationary and axisymmetric space-times. The neutrino trajectories are obtained by using a ray tracing algorithm, based on numerically solving the Hamilton-Jacobi equation for neutrinos by reversing the proper time evolution. We obtain the energy deposition rates for several classes of rotating neutron stars, described by different equations of state of the neutron matter, and for quark stars, described by the MIT bag model equation of state and in the CFL (Color-Flavor-Locked) phase, respectively. The electron-positron energy deposition rate on the rotation axis of rotating neutron and quark stars is studied for two accretion disk models (isothermal disk and accretion disk in thermodynamical equilibrium). Rotation and general relativistic effects modify the total annihilation rate of the neutrino-antineutrino pairs on the rotation axis of compact stellar, as measured by an observer at infinity. The differences in the equations of state for neutron and quark matter also have important effects on the spatial distribution of the energy deposition rate by neutrino-antineutrino annihilation.Comment: 38 pages, 9 figures, accepted for publication in MNRA

Spatiotemporal Patterns and Predictability of Cyberattacks

Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ479 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Compressing Inertial Motion Data in Wireless Sensing Systems – An Initial Experiment

The use of wireless inertial motion sensors, such as accelerometers, for supporting medical care and sport’s training, has been under investigation in recent years. As the number of sensors (or their sampling rates) increases, compressing data at source(s) (i.e. at the sensors), i.e. reducing the quantity of data that needs to be transmitted between the on-body sensors and the remote repository, would be essential especially in a bandwidth-limited wireless environment. This paper presents a set of compression experiment results on a set of inertial motion data collected during running exercises. As a starting point, we selected a set of common compression algorithms to experiment with. Our results show that, conventional lossy compression algorithms would achieve a desirable compression ratio with an acceptable time delay. The results also show that the quality of the decompressed data is within acceptable range