High spin baryon in hot strongly coupled plasma

We consider a strings-junction holographic model of probe baryon in the finite-temperature supersymmetric Yang-Mills dual of the AdS-Schwarzschild black hole background. In particular, we investigate the screening length for high spin baryon composed of rotating N_c heavy quarks. To rotate quarks by finite force, we put hard infrared cutoff in the bulk and give quarks finite mass. We find that N_c microscopic strings are embedded reasonably in the bulk geometry when they have finite angular velocity \omega, similar to the meson case. By defining the screening length as the critical separation of quarks, we compute the \omega dependence of the baryon screening length numerically and obtain a reasonable result which shows that baryons with high spin dissociate more easily. Finally, we discuss the relation between J and E^2 for baryons.Comment: 18 pages, 19 figures, version to appear in JHE

Advanced classification of OFDM and MIMO signals with enhanced second order cyclostationarity detection

With the emergence of cognitive radio and the introduction of new modulation techniques such as OFDM and MIMO, the problem of Modulation Classification (MC) becomes more challenging and complicated. In the first part of the thesis, we explore the automatic modulation classification to blindly distinguish OFDM from single carrier signals. We use the fourth order cumulants; an approach which in the past has been also applied to classify single carrier signals. A blind OFDM parameter estimation scheme was then followed, which includes the estimation of number of subcarriers, CP length, timing and frequency offset and the oversampling factor for the OFDM signal. For the second part of the thesis, we improve the statistical signal processing techniques that were used in the first part. Particularly, the second order cyclostationarity based methods have been examined and improved. Based on the fact that most of the cyclostationary communication signals has a real cyclostationary part and a complex non-cyclostaionary part, we suggest an approach that enhance the second order cyclostationarity and hence increase its probability of detection. Using such improved second-order cyclostationarity, we present an improved synchronization method based on second order cyclostationarity. With the proposed approach, it is shown that the timing estimator, is independent of the frequency offset estimator, and therefore performs better than the previously proposed class of blind synchronization methods. To negate the dependence of the blind synchronization scheme on the prior knowledge of the raised cosine pulse shaping filters, we proposed a blind roll-off factor estimator based on the second order cyclostationarity. Last, we address the MIMO classification problem, wherein we estimate the number of transmitting antennas. Here the second order cyclostationarity test has been applied in distinguishing STC from BLAST modulation

On a Localized Riemannian Penrose Inequality

Consider a compact, orientable, three dimensional Riemannian manifold with boundary with nonnegative scalar curvature. Suppose its boundary is the disjoint union of two pieces: the horizon boundary and the outer boundary, where the horizon boundary consists of the unique closed minimal surfaces in the manifold and the outer boundary is metrically a round sphere. We obtain an inequality relating the area of the horizon boundary to the area and the total mean curvature of the outer boundary. Such a manifold may be thought as a region, surrounding the outermost apparent horizons of black holes, in a time-symmetric slice of a space-time in the context of general relativity. The inequality we establish has close ties with the Riemannian Penrose Inequality, proved by Huisken and Ilmanen, and by Bray.Comment: 16 page

Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a longstanding tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao et al. (2018) have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to nonparametrically identify the average treatment effect (ATE) from observational data subject to uncontrolled confounding. In this paper, we establish nonparametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the nonparametric model, and we propose multiply robust and locally efficient estimators when nonparametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the postlicensure surveillance of vaccine safety among children