Localized States and Resultant Band Bending in Graphene Antidot Superlattices

We fabricated dye sensitized graphene antidot superlattices with the purpose of elucidating the role of the localized edge state density. The fluorescence from deposited dye molecules was found to strongly quench as a function of increasing antidot filling fraction, whereas it was enhanced in unpatterned but electrically back-gated samples. This contrasting behavior is strongly indicative of a built-in lateral electric field that accounts for fluorescence quenching as well as p-type doping. These findings are of great interest for light-harvesting applications that require field separation of electron-hole pairs.Comment: NanoLetters, 201

Communication complexity of approximate maximum matching in the message-passing model

We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model. The maximum matching problem is one of the most fundamental graph combinatorial problems, with a variety of applications. The input to the problem is a graph G that has n vertices and the set of edges partitioned over k sites, and an approximation ratio parameter α. The output is required to be a matching in G that has to be reported by one of the sites, whose size is at least factor α of the size of a maximum matching in G. We show that the communication complexity of this problem is Ω(α2kn)information bits. This bound is shown to be tight up to a log n factor, by constructing an algorithm, establishing its correctness, and an upper bound on the communication cost. The lower bound also applies to other graph combinatorial problems in the message-passing communication model, including max-flow and graph sparsification

Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration

Age related macular degeneration is a major cause of blindness and visual impairment in older adults. Its exudative form, where fluids leak into the macula, is especially damaging. The standard treatment involves injections of anti-VEGF (vascular endothelial growth factor) agents into the eye, which prevent further vascular growth and leakage, and can restore vision. These intravitreal injections have a risk of devastating complications including blindness from infection and are expensive. Optimizing the interval between injections in a patient specific manner is of great interest, as the retinal response is partially patient specific. In this paper we propose a machine learning approach to predict the retinal response at the end of a standardized 12-week induction phase of the treatment. From a longitudinal series of optical coherence tomography (OCT) images, a number of quantitative measurements are extracted, describing the underlying retinal structure and pathology and its response to initial treatment. After initial feature selection, the selected set of features is used to predict the treatment response status at the end of the induction phase using the support vector machine classifier. On a population of 30 patients, leave-one-out cross-validation showed the classification success rate of 87% of predicting whether the subject will show a response to the treatment at the next visit. The proposed methodology is a promising step towards the much needed image-guided prediction of patient-specific treatment response