Field-induced breakdown of the quantum Hall effect

A numerical analysis is made of the breakdown of the quantum Hall effect caused by the Hall electric field in competition with disorder. It turns out that in the regime of dense impurities, in particular, the number of localized states decreases exponentially with the Hall field, with its dependence on the magnetic and electric field summarized in a simple scaling law. The physical picture underlying the scaling law is clarified. This intra-subband process, the competition of the Hall field with disorder, leads to critical breakdown fields of magnitude of a few hundred V/cm, consistent with observations, and accounts for their magnetic-field dependence \propto B^{3/2} observed experimentally. Some testable consequences of the scaling law are discussed.Comment: 7 pages, Revtex, 3 figures, to appear in Phys. Rev.

Vision and Reading Difficulties Part 2: Optometric correlates of reading difficulties

In this second article of the series on vision and reading difficulties, the optometric factors (for example refractive error and orthoptic function) that may be associated with reading problems are discussed in detail. The first article of this series introduced the correlates of, and interventions for, reading difficulties that have been supported by evidence-based research. This present article describes the optometric correlates more specifically, providing details of the aspects of visual function that ought to be considered for further investigation

Variational Image Segmentation Model Coupled with Image Restoration Achievements

Image segmentation and image restoration are two important topics in image processing with great achievements. In this paper, we propose a new multiphase segmentation model by combining image restoration and image segmentation models. Utilizing image restoration aspects, the proposed segmentation model can effectively and robustly tackle high noisy images, blurry images, images with missing pixels, and vector-valued images. In particular, one of the most important segmentation models, the piecewise constant Mumford-Shah model, can be extended easily in this way to segment gray and vector-valued images corrupted for example by noise, blur or missing pixels after coupling a new data fidelity term which comes from image restoration topics. It can be solved efficiently using the alternating minimization algorithm, and we prove the convergence of this algorithm with three variables under mild condition. Experiments on many synthetic and real-world images demonstrate that our method gives better segmentation results in comparison to others state-of-the-art segmentation models especially for blurry images and images with missing pixels values.Comment: 23 page

Tracking the affective state of unseen persons.

Emotion recognition is an essential human ability critical for social functioning. It is widely assumed that identifying facial expression is the key to this, and models of emotion recognition have mainly focused on facial and bodily features in static, unnatural conditions. We developed a method called affective tracking to reveal and quantify the enormous contribution of visual context to affect (valence and arousal) perception. When characters' faces and bodies were masked in silent videos, viewers inferred the affect of the invisible characters successfully and in high agreement based solely on visual context. We further show that the context is not only sufficient but also necessary to accurately perceive human affect over time, as it provides a substantial and unique contribution beyond the information available from face and body. Our method (which we have made publicly available) reveals that emotion recognition is, at its heart, an issue of context as much as it is about faces