1,257 research outputs found
Non-Gaussian fluctuations of mesoscopic persistent currents
The persistent current in an ensemble of normal-metal rings shows Gaussian
distributed sample-to-sample fluctuations with non-Gaussian corrections, which
are precursors of the transition into the Anderson localized regime. We here
report a calculation of the leading non-Gaussian correction to the current
autocorrelation function, which is of third order in the current. Although the
third-order correlation function is small, inversely proportional to the
dimensionless conductance of the ring, the mere fact that it is nonzero is
remarkable, since it is an odd moment of the current distribution.Comment: 4+ pages, 2 figure
Nuclear spin pumping and electron spin susceptibilities
In this work we present a new formalism to evaluate the nuclear spin dynamics
driven by hyperfine interaction with non-equilibrium electron spins. To
describe the dynamics up to second order in the hyperfine coupling, it suffices
to evaluate the susceptibility and fluctuations of the electron spin. Our
approach does not rely on a separation of electronic energy scales or the
specific choice of electronic basis states, thereby overcoming practical
problems which may arise in certain limits when using a more traditional
formalism based on rate equations.Comment: 9 pages, 2 figure
Social encounter networks : collective properties and disease transmission
A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of −2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions
Community analysis in social networks
We present an empirical study of different social networks obtained from
digital repositories. Our analysis reveals the community structure and provides
a useful visualising technique. We investigate the scaling properties of the
community size distribution, and that find all the networks exhibit power law
scaling in the community size distributions with exponent either -0.5 or -1.
Finally we find that the networks' community structure is topologically
self-similar using the Horton-Strahler index.Comment: Submitted to European Physics Journal
Impact of constrained rewiring on network structure and node dynamics
In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic.We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network’s initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence
Cross-border Dividends from the Perspective of Switzerland as the Source State - Selected Issues under Article 15 of the Swiss-EU Savings Agreement
Benchmark graphs for testing community detection algorithms
Community structure is one of the most important features of real networks
and reveals the internal organization of the nodes. Many algorithms have been
proposed but the crucial issue of testing, i.e. the question of how good an
algorithm is, with respect to others, is still open. Standard tests include the
analysis of simple artificial graphs with a built-in community structure, that
the algorithm has to recover. However, the special graphs adopted in actual
tests have a structure that does not reflect the real properties of nodes and
communities found in real networks. Here we introduce a new class of benchmark
graphs, that account for the heterogeneity in the distributions of node degrees
and of community sizes. We use this new benchmark to test two popular methods
of community detection, modularity optimization and Potts model clustering. The
results show that the new benchmark poses a much more severe test to algorithms
than standard benchmarks, revealing limits that may not be apparent at a first
analysis.Comment: 6 pages, 8 figures. Extended version published on Physical Review E.
The code to build the new benchmark graphs can be downloaded from
http://santo.fortunato.googlepages.com/inthepress
Social encounter networks : characterizing Great Britain
A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact
Challenges and Opportunities in Central Nervous System Drug Discovery
The development of new drugs for disorders of the central nervous system (CNS) presents unique challenges when compared with other disease areas. These include an incomplete understanding of the biology of multifaceted CNS conditions such as Alzheimer's disease, the presence of a blood–brain barrier that restricts the flow of molecules to the brain, and a lack of clinically relevant animal models in which to test new drugs. In this review, we aim to discuss some of these issues at each stage of the drug discovery process, provide examples of recent work addressing them, and consider the options available to researchers in the future
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
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