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A compact fiber-optic inclinometer based on a fiber-taper Michelson interferometric sensor is constructed and demonstrated. The sensor consist of a single symmetrically taper waist of 80 \mum distanced 30 mm from the single-mode fiber end-tip right-angled cleaved. The amplitude of the bending angle of the fiber taper interferometer is obtained by passive interferometric interrogation based on the generation of two quadrature phase-shifted signals from two fiber Bragg gratings with different resonant wavelengths. Optical phase-to-bending sensitivity of \sim1.13 rad/degree and a bend angle resolution of \sim0.014 degree/\surd Hz were achieved. [ABSTRACT FROM PUBLISHER]

Most real-world networks considered in the literature have a modular structure. Analysis of these real-world networks often are performed under the assumption that there is only one type of node. However, social and biochemical systems are often bipartite networks, meaning that there are two exclusive sets of nodes, and that edges run exclusively between nodes belonging to different sets. Here we address the issue of module detection in bipartite networks by comparing the performance of two classes of group identification methods – modularity maximization and clique percolation – on an ensemble of modular random bipartite networks. We find that the modularity maximization methods are able to reliably detect the modular bipartite structure, and that, under some conditions, the simulated annealing method outperforms the spectral decomposition method. We also find that the clique percolation methods are not capable of reliably detecting the modular bipartite structure of the bipartite model networks considered. [ABSTRACT FROM AUTHOR]

We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task – a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations’ ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs. [ABSTRACT FROM AUTHOR]

Most networks found in social and biochemical systems have modular structures. An important question prompted by the modularity of these networks is whether nodes can be said to belong to a single group. If they cannot, we would need to consider the role of “overlapping communities.” Despite some efforts in this direction, the problem of detecting overlapping groups remains unsolved because there is neither a formal definition of overlapping community, nor an ensemble of networks with which to test the performance of group detection algorithms when nodes can belong to more than one group. Here, we introduce an ensemble of networks with overlapping groups. We then apply three group identification methods – modularity maximization, k-clique percolation, and modularity-landscape surveying – to these networks. We find that the modularity-landscape surveying method is the only one able to detect heterogeneities in node memberships, and that those heterogeneities are only detectable when the overlap is small. Surprisingly, we find that the k-clique percolation method is unable to detect node membership for the overlapping case. [ABSTRACT FROM AUTHOR]

In this opening introductory paper, we discuss the possibility that scaleinvariant correlations may be a feature of biological and possibly even social systems. We illustrate this possibility by reviewing recent work at Boston University. Specifically, we focus first on the apparent scale-invariant correlations in non-coding deoxyribonucleic acid (DNA) and show that this feature can be used to distinguish coding and non-coding DNA. We argue that the inflating a degassed lung is characterized by a cascade of avalanches, as the airways successively open, and that distribution functions characterizing this cascade are scale invariant. Moving from the lung to the heart, we find that the sequence of interbeat intervals is characterized by scale-invariant correlations in health, but not in disease. Moving from individual organs to entire organisms, we discuss recent experimental evidence that the foraging behaviour of the wandering albatross is governed by a scale-invariant Levy distribution. Finally, we enquire whether scale invariance describes not only animal behaviour but also human behaviour. To this end, we analyse data on urban growth patterns, on finance and on economics. For all cases, we find empirical evidence of scaling behaviour. We conclude by asking why such complex systems might display scale invariance. [ABSTRACT FROM AUTHOR]