Looking at the right place at the right time is a critical component of driving skill. Therefore, gaze guidance has the potential to become a valuable driving assistance system. In previous work, we have already shown that complex gaze-contingent stimuli can guide attention and reduce the number of accidents in a simple driving simulator. We here set out to investigate whether cues that are simple enough to be implemented in a real car can also capture gaze during a more realistic driving task in a high-fidelity driving simulator. This immediately raises another question, namely how such cues would interfere with the driving task itself.
We used a state-of-the-art, wide-field-of-view driving simulator with an integrated eye tracker. Gaze-contingent warnings were implemented using two arrays of light-emitting diodes horizontally fitted below and above the simulated windshield. Twelve volunteers drove along predetermined routes in the simulated environment populated with autonomous traffic. Warnings were triggered during the approach to half of the intersections, cueing either towards the right or to the left. The remaining intersections were not cued, and served as controls. A preliminary analysis shows that gaze-contingent cues led to a significant shift in gaze position towards the highlighted direction.
People in violent neighbourhoods attribute violence in public spaces to, especially, poverty and unemployment, but agree that social disintegration, disrespect, drinking and drugs and the weaknesses of the criminal justice system also contribute substantially. However, data from a panel of young men in Cape Town provide little support for the hypothesis that unemployment and poverty are direct causes of violence against strangers. Growing up in a home where someone drank heavily or took drugs is, however, a strong predictor of violence against strangers in early adulthood. A history of drinking (or taking drugs) correlates with perpetration of violence, and might also serve as a mechanism through which conditions during childhood have indirect effects. Living in a bad neighbourhood and immediate poverty are associated with violence against strangers, but being unemployed is not. Overall, heavy drinking – whether by adults in the childhood home or by young men themselves – seems to be a more important predictor of violence than economic circumstances in childhood or the recent past. Heavy drinking seems to play an important part in explaining why some young men have been more violent than others in circumstances that seem to have been generally conducive to rising violence, for reasons that remain unclear. It seems likely that few young people in South Africa in the early 2000s come from backgrounds that strongly predispose them against the use of violence.
The spectral theory of graphs provides a bridge between classical signal processing and the nascent field of graph signal processing. In this paper, a spectral graph analogy to Heisenberg's celebrated uncertainty principle is developed. Just as the classical result provides a tradeoff between signal localization in time and frequency, this result provides a fundamental tradeoff between a signal's localization on a graph and in its spectral domain. Using the eigenvectors of the graph Laplacian as a surrogate Fourier basis, quantitative definitions of graph and spectral ``spreads'' are given, and a complete characterization of the feasibility region of these two quantities is developed. In particular, the lower boundary of the region, referred to as the uncertainty curve, is shown to be achieved by eigenvectors associated with the smallest eigenvalues of an affine family of matrices. The convexity of the uncertainty curve allows it to be found to within $\varepsilon$ by a fast approximation algorithm requiring $\mathcal{O}(\varepsilon^{-1/2})$ typically sparse eigenvalue evaluations. Closed-form expressions for the uncertainty curves for some special classes of graphs are derived, and an accurate analytical approximation for the expected uncertainty curve of Erdos-Renyi random graphs is developed. These theoretical results are validated by numerical experiments, which also reveal an intriguing connection between diffusion processes on graphs and the uncertainty bounds.
