Modeling electrolytically top gated graphene

We investigate doping of a single-layer graphene in the presence of electrolytic top gating. The interfacial phenomena is modeled using a modified Poisson-Boltzmann equation for an aqueous solution of simple salt. We demonstrate both the sensitivity of graphene's doping levels to the salt concentration and the importance of quantum capacitance that arises due to the smallness of the Debye screening length in the electrolyte.Comment: 7 pages, including 4 figures, submitted to Nanoscale Research Letters for a special issue related to the NGC 2009 conference (http://asdn.net/ngc2009/index.shtml

Longitudinal Imaging of the Ageing Mouse

Several non-invasive imaging techniques are used to investigate the effect of pathologies and treatments over time in mouse models. Each preclinical in vivo technique provides longitudinal and quantitative measurements of changes in tissues and organs, which are fundamental for the evaluation of alterations in phenotype due to pathologies, interventions and treatments. However, it is still unclear how these imaging modalities can be used to study ageing with mice models. Almost all age related pathologies in mice such as osteoporosis, arthritis, diabetes, cancer, thrombi, dementia, to name a few, can be imaged in vivo by at least one longitudinal imaging modality. These measurements are the basis for quantification of treatment effects in the development phase of a novel treatment prior to its clinical testing. Furthermore, the non-invasive nature of such investigations allows the assessment of different tissue and organ phenotypes in the same animal and over time, providing the opportunity to study the dysfunction of multiple tissues associated with the ageing process. This review paper aims to provide an overview of the applications of the most commonly used in vivo imaging modalities used in mouse studies: micro-computed-tomography, preclinical magnetic-resonance-imaging, preclinical positron-emission-tomography, preclinical single photon emission computed tomography, ultrasound, intravital microscopy, and whole body optical imaging

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.Comment: 11 pages, 2 figures, 3 table

Online learning meets optimization in the dual

Abstract. We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universal online bounds as an optimization problem. Using the weak duality theorem we reduce the process of online learning to the task of incrementally increasing the dual objective function. The amount by which the dual increases serves as a new and natural notion of progress. We are thus able to tie the primal objective value and the number of prediction mistakes using and the increase in the dual. The end result is a general framework for designing and analyzing old and new online learning algorithms in the mistake bound model.

De copulis non est disputandum

Copulae, Multivariate dependence, Value-at-Risk,