Dynamical Stability of Six-Dimensional Warped Brane-Worlds

We study a generalization of the Randall-Sundrum mechanism for generating the weak/Planck hierarchy, which uses two rather than one warped extra dimension, and which requires no negative tension branes. A 4-brane with one exponentially large compact dimension plays the role of the Planck brane. We investigate the dynamical stability with respect to graviton, graviphoton and radion modes. The radion is shown to have a tachyonic instability for certain models of the 4-brane stress-energy, while it is stable in others, and massless in a special case. If stable, its mass is in the milli-eV range, for parameters of the model which solve the hierarchy problem. The radion is shown to couple to matter with gravitational strength, so that it is potentially detectable by submillimeter-range gravity experiments. The radion mass can be increased using a bulk scalar field in the manner of Goldberger and Wise, but only to order MeV, due to the effect of the large extra dimension. The model predicts a natural scale of 10^{13} GeV on the 4-brane, making it a natural setting for inflation from the ultraviolet brane.Comment: 28 pages, 7 figure

Driver workload detection in on-road driving environment using machine learning

Drivers' high workload caused by distractions has become one of the major concerns for road safety. This paper presents a datadriven method using machine learning algorithms to detect high workload caused by surrogate in-vehicle (IV) secondary tasks performed in an on-road experiment with real traffic. The data were collected using an instrumented vehicle while drivers performed two types of secondary tasks: visual-manual and auditory-vocal tasks. Two types of machine learning methods, support vector machine (SVM) and extreme learning machine (ELM), were applied to detect drivers' workload via drivers' visual behaviour (i.e. eye movements) data alone, as well as visual plus driving performance data. The results suggested that both methods can detect drivers' workload at high accuracy, with ELM outperformed SVM in most cases. We found that for visual intensive workload, using drivers' visual data alone achieveed an accuracy close to using the combination information from both visual and driving performance data. This study proves that machine learning methods can be used for real driving applications