Empirical Determinations of Key Physical Parameters Related to Classical Double Radio Sources

Multi-frequency radio observations of the radio bridge of powerful classical double radio sources can be used to determine: the beam power of the jets emanating from the AGN; the total time the source will actively produce jets that power large-scale radio emission; the thermal pressure of the medium in the vicinity of the radio source; and the total mass, including dark matter, of the galaxy or cluster of galaxies traced by the ambient gas that surrounds the radio source. The theoretical constructs that allow a determination of each of these quantities using radio observations are presented and discussed. Empirical determinations of each of these quantities are obtained and analyzed. A sample of 14 radio galaxies and 8 radio loud quasars with redshifts between zero and two for which there is enough radio information to be able to determine the physical parameters listed above was studied in detail. (abridged)Comment: Submitted to ApJ, LaTex, 26 total pages of text which includes captions & two tables, plus 13 EPS figures & 1 tabl

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Thrust distribution in Higgs decays at the next-to-leading order and beyond

We present predictions for the thrust distribution in hadronic decays of the Higgs boson at the next-to-leading order and the approximate next-to-next-to-leading order. The approximate NNLO corrections are derived from a factorization formula in the soft/collinear phase-space regions. We find large corrections, especially for the gluon channel. The scale variations at the lowest orders tend to underestimate the genuine higher order contributions. The results of this paper is therefore necessary to control the perturbative uncertainties of the theoretical predictions. We also discuss on possible improvements to our results, such as a soft-gluon resummation for the 2-jets limit, and an exact next-to-next-to-leading order calculation for the multi-jets region