Reconfigurable photonic metamaterials drive by Coulomb, Lorentz and optical forces

Metamaterials offer a huge range of enhanced and novel functionalities that natural materials cannot provide. They promise applications in superresolution imaging, optical data storage, optical filters, polarization control, cloaking, fraud prevention and many more. However, their unique optical properties are often narrowband and usually fixed. Here we demonstrate how the mechanical rearrangement of metamaterial structures at the nanoscale provides a powerful platform for controlling metamaterial properties dynamically. Using thermal, electrical, magnetic and optical control signals we demonstrate large-range tuning, high-contrast switching and modulation of metamaterial optical properties at megahertz frequencies and beyond. Beyond the obvious benefit of adding tunability to known metamaterial functionalities, this unlocks many new opportunities in areas such as light modulation and highly nonlinear & bistable optical device

Dynamic Models of Segregation in Small-World Networks

Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by one- or two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We generalize Schelling's model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling's original results seem to be robust to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Small worlds, Scale-free networks, Best-response dynamics

Segregation in Networks.

Schelling (1969, 1971a,b, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact ’locally’ in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling’s original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.

Dynamic Models of Segregation in Small-World Networks

Schelling (1969, 1971a,b, 1978) considered a simple proximity model of segregation where individual agents only care about the types of people living in their own local geographical neighborhood, the spatial structure being represented by one- or two-dimensional lattices. In this paper, we argue that segregation might occur not only in the geographical space, but also in social environments. Furthermore, recent empirical studies have documented that social interaction structures are well-described by small-world networks. We gen- eralize Schelling's model by allowing agents to interact in small-world networks instead of regular lattices. We study two alternative dynamic models where agents can decide to move either arbitrarily far away (global model) or are bound to choose an alternative location in their social neighborhood (local model). Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. Thus, Schelling's original results seem to be robust to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Small worlds, Scale-free networks, Best-response dynamics

Segregation in Networks

Schelling (1969, 1971, 1971, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact 'locally' in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling's original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.

Lorentz force metamaterial with giant optical magnetoelectric response

We demonstrate the first reconfigurable photonic metamaterial controlled by electrical currents and magnetic fields, providing first practically useful solutions for sub-megahertz and high contrast modulation of metamaterial optical properties

Clonality and Genetic Diversity Revealed by AFLPs in Schisandra glabra (Brickell) Rheder (Schisandraceae), a Rare Basal Angiosperm

Rare species with fragmented distributions often exhibit reduced levels of genetic diversity within populations. However, life history traits such as long lived perennial habit and outcrossing mating system, are associated with high levels of within species genetic variation being partitioned within populations. Schisandra glabra (Schisandraceae) is a rare basal angiosperm with a fragmented distribution across the southeastern US and in a disjunct population in cloudforest of Mexico. The species’ clonal reproduction by rhizomes, confounds the delineation of genetically distinct individuals in the field. The patterns of genetic diversity and clonality in 10 populations of S. glabra were investigated using AFLP markers. I found a surprising number of distinct genetic individuals in the two populations sampled on 3m grids, with 31 unique genotypes out of 42 samples at Wolfpen Creek, KY, and unique genotypes in all 48 samples from Panther Creek, GA. AMOVA of 237 individuals from 10 populations revealed that the largest portion of the genetic variation is found within populations (58.0%; P\u3c0.0001), and 27.7% (P\u3c0.0001) of the genetic variation is partitioned between the US and Mexico S. glabra populations. Population structure was also detected between the US and Mexico populations, but no structure was detected between the majority of the US populations. The genetic differentiation of the disjunct population in Mexico, may be the result of a Pliocene or Miocene vicariance hypothesized for many species with similar distributions. The high levels of genetic diversity found within populations are evidence of historical gene flow between the US populations, and the preservation of genetic diversity by the long lived species in its present fragmented distribution

Agglomeration externalities and 1981-2006 regional growth in Brazil

This paper focuses on manufacturing employment growth across the 26 states of Brazil. We employ the Glaeser et al. (1992) approach to identify the role played by knowledge externalities in growth and convergence. To assess robustness of the results, we compare cross-section models, dynamic panel models and pooled-periods fixed-effect models. We find that cross-section models confirm the positive impact of Porter’s and Jacobs’ competition externalities on growth, whereas dynamic panel models and pooled-periods fixed-effect models are consistent with the predictions of Marshall-Arrow-Romer and Porter regarding the role of specialisation in manufacturing vis-à-vis other employment. The results provide new insights into the rapid growth since 1981 in particularly the North and Centre West of Brazil