Spatially embedded random networks

Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples

A Network of Community Partners Representing Multiple Communities: Developing a Tool for Matching Community- Engaged Scholars with Community Partners

The Community Partnership for Ethical Research (CPER) was a multi-faceted research project designed to test a model of community engagement using a network of community partners called Community Advocates for Research (CARs). The goals of the project included developing systems to sustain and expand the CARs network. This article presents one facet of this project—a method of effectively and efficiently managing data about the CARs. User-friendly surveys and a database were designed for the management of these data. The web-based survey allows data capture in the community. Moreover, the web-based database tools facilitate centralized data collection and management that will contribute to the sustainability of the network of CARs beyond the initial grant that provided the funding for its development. This article describes the surveys and database and their utility for other institutions desiring to establish similar networks of community partners

The Geography of Scientific Productivity: Scaling in U.S. Computer Science

Here we extract the geographical addresses of authors in the Citeseer database of computer science papers. We show that the productivity of research centres in the United States follows a power-law regime, apart from the most productive centres for which we do not have enough data to reach definite conclusions. To investigate the spatial distribution of computer science research centres in the United States, we compute the two-point correlation function of the spatial point process and show that the observed power-laws do not disappear even when we change the physical representation from geographical space to cartogram space. Our work suggests that the effect of physical location poses a challenge to ongoing efforts to develop realistic models of scientific productivity. We propose that the introduction of a fine scale geography may lead to more sophisticated indicators of scientific output.Comment: 6 pages, 3 figures; minor change

Limits of Abductivism About Logic

I argue against abductivism about logic, which is the view that rational theory choice in logic happens by abduction. Abduction cannot serve as a neutral arbiter in many foundational disputes in logic because, in order to use abduction, one must first identify the relevant data. Which data one deems relevant depends on what I call one's conception of logic. One's conception of logic is, however, not independent of one's views regarding many of the foundational disputes that one may hope to solve by abduction

Enhanced statistical stability in coherent interferometric imaging

http://iopscience.iop.org/0266-5611/International audienc

Simulation of truncated normal variables

We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure.Comment: This 1992 paper appeared in 1995 in Statistics and Computing and the gist of it is contained in Monte Carlo Statistical Methods (2004), but I receive weekly requests for reprints so here it is

Deletion of alpha-synuclein decreases impulsivity in mice

The presynaptic protein alpha-synuclein, associated with Parkinson's Disease (PD), plays a role in dopaminergic neurotransmission and is implicated in impulse control disorders (ICDs) such as drug addiction. In this study we investigated a potential causal relationship between alpha-synuclein and impulsivity, by evaluating differences in motor impulsivity in the 5-choice serial reaction time task (5-CSRTT) in strains of mice that differ in the expression of the alpha-synuclein gene. C57BL/6JOlaHsd mice differ from their C57BL/6J ancestors in possessing a chromosomal deletion resulting in the loss of two genes, snca, encoding alpha-synuclein, and mmrn1, encoding multimerin-1. C57BL/6J mice displayed higher impulsivity (more premature responding) than C57BL/6JOlaHsd mice when the pre-stimulus waiting interval was increased in the 5-CSRTT. In order to ensure that the reduced impulsivity was indeed related to snca, and not adjacent gene deletion, wild type (WT) and mice with targeted deletion of alpha-synuclein (KO) were tested in the 5-CSRTT. Similarly, WT mice were more impulsive than mice with targeted deletion of alpha-synuclein. Interrogation of our ongoing analysis of impulsivity in BXD recombinant inbred mouse lines revealed an association of impulsive responding with levels of alpha-synuclein expression in hippocampus. Expression of beta- and gamma-synuclein, members of the synuclein family that may substitute for alpha-synuclein following its deletion, revealed no differential compensations among the mouse strains. These findings suggest that alpha-synuclein may contribute to impulsivity and potentially, to ICDs which arise in some PD patients treated with dopaminergic medication

Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.Comment: 21 pages, 10 figure

Extracting galactic binary signals from the first round of Mock LISA Data Challenges

We report on the performance of an end-to-end Bayesian analysis pipeline for detecting and characterizing galactic binary signals in simulated LISA data. Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM) algorithm, which has been optimized to search for tens of thousands of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior distribution functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the Round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. The only misses were later traced to a coding error that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a Genetic Algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals.Comment: 13 pages, 4 figures, submitted to Proceedings of GWDAW-11 (Berlin, Dec. '06

Anaerobic digestion of whole-crop winter wheat silage for renewable energy production

With biogas production expanding across Europe in response to renewable energy incentives, a wider variety of crops need to be considered as feedstock. Maize, the most commonly used crop at present, is not ideal in cooler, wetter regions, where higher energy yields per hectare might be achieved with other cereals. Winter wheat is a possible candidate because, under these conditions, it has a good biomass yield, can be ensiled, and can be used as a whole crop material. The results showed that, when harvested at the medium milk stage, the specific methane yield was 0.32 m3 CH4 kg–1 volatile solids added, equal to 73% of the measured calorific value. Using crop yield values for the north of England, a net energy yield of 146–155 GJ ha–1 year–1 could be achieved after taking into account both direct and indirect energy consumption in cultivation, processing through anaerobic digestion, and spreading digestate back to the land. The process showed some limitations, however: the relatively low density of the substrate made it difficult to mix the digester, and there was a buildup of soluble chemical oxygen demand, which represented a loss in methane potential and may also have led to biofoaming. The high nitrogen content of the wheat initially caused problems, but these could be overcome by acclimatization. A combination of these factors is likely to limit the loading that can be applied to the digester when using winter wheat as a substrat