Pairwise Network Information and Nonlinear Correlations

Reconstructing the structural connectivity between interacting units from observed activity is a challenge across many different disciplines. The fundamental first step is to establish whether or to what extent the interactions between the units can be considered pairwise and, thus, can be modeled as an interaction network with simple links corresponding to pairwise interactions. In principle this can be determined by comparing the maximum entropy given the bivariate probability distributions to the true joint entropy. In many practical cases this is not an option since the bivariate distributions needed may not be reliably estimated, or the optimization is too computationally expensive. Here we present an approach that allows one to use mutual informations as a proxy for the bivariate distributions. This has the advantage of being less computationally expensive and easier to estimate. We achieve this by introducing a novel entropy maximization scheme that is based on conditioning on entropies and mutual informations. This renders our approach typically superior to other methods based on linear approximations. The advantages of the proposed method are documented using oscillator networks and a resting-state human brain network as generic relevant examples

The importance of collegiality and reciprocal learning in the professional development of beginning teachers

This paper discusses factors which enhance induction experiences for beginning teachers. It reports the findings from case studies which explore the impact of new entrants to the teaching profession in Scotland. The data suggest that the most supportive induction processes mix both formal and informal elements, but that the informal elements such as collegiality, good communication and a welcoming workplace environment should not be underestimated. The study also highlights the potential benefits of a more collegiate environment for teachers across the career phases. Experienced teachers and new entrants had a range of experience to offer each other, thus creating more cohesive professional working which was supportive of early career teachers while encouraging reflection on practice among the more experienced professionals

Reply to the comment by C. Capan and K. Behnia on "Nernst effect in poor conductors and in the cuprate superconductors" (cond-mat/0501288)

The comment criticisms (cond-mat/0501288) are completely out of line with the context of the commented theory (Phys. Rev. Lett. v.93, 217002 (2004)). The comment neglected essential parts of the theory, which actually addressed all relevant experimental observations. I argue that the coexistence of the large Nernst signal and the insulating-like in-plane resistivity in underdoped cuprates rules out the vortex scenario, but agrees remarkably well with our theory.Comment: 1 page, 1 figur

Genotypic characterisation of Giardia from domestic dogs in the USA

The first large-scale urban survey of Giardia infections in dogs was undertaken in the USA. It involved several locations in the Western United States with Giardia isolates from microscopy-positive samples characterised by multi-locus PCR and sequencing. A high prevalence of Giardia was confirmed in asymptomatic domestic dogs, and for the first time, provides evidence that zoonotic assemblages/subgroups of Giardia occur frequently in domestic dogs living in urban environments, and more frequently than the dog specific assemblages

Sampling Limits for Electron Tomography with Sparsity-exploiting Reconstructions

Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized by compressed sensing, sparsity-exploiting algorithms have been applied to experimental ET data and show promise for improving reconstruction quality or reducing the total beam dose applied to a specimen. Nevertheless, theoretical bounds for these methods have been less explored in the context of ET applications. Here, we perform numerical simulations to investigate performance of l_1-norm and total-variation (TV) minimization under various imaging conditions. From 36,100 different simulated structures, our results show specimens with more complex structures generally require more projections for exact reconstruction. However, once sufficient data is acquired, dividing the beam dose over more projections provides no improvements - analogous to the traditional dose-fraction theorem. Moreover, a limited tilt range of +-75 or less can result in distorting artifacts in sparsity-exploiting reconstructions. The influence of optimization parameters on reconstructions is also discussed