Significant effects of weak gravitational lensing on determinations of the cosmology from Type Ia Supernov\ae

Significant adjustments to the values of the cosmological parameters estimated from high-redshift Type Ia Supernov\ae data are reported, almost an order of magnitude greater than previously found. They arise from the effects of weak gravitational lensing on observations of high-redshift sources. The lensing statistics used have been obtained from computations of the three-dimensional shear in a range of cosmological N-body simulations, from which it is estimated that cosmologies with an underlying deceleration parameter q_0 = -0.51 +0.03/-0.24 may be interpreted as having q_0 = -0.55 (appropriate to the currently popular cosmology with density parameter $\Omega_M = 0.3$ and vacuum energy density parameter $\Omega_{\Lambda} = 0.7$). In addition, the standard deviation expected from weak lensing for the peak magnitudes of Type Ia Supernov\ae at redshifts of 1 is expected to be approximately 0.078 magnitudes, and 0.185 magnitudes at redshift 2. This latter value is greater than the accepted intrinsic dispersion of 0.17 magnitudes. Consequently, the effects of weak lensing in observations of high-redshift sources must be taken properly into account.Comment: 9 pages, LaTeX, 4 figure

Detecting hierarchical and overlapping network communities using locally optimal modularity changes

Agglomerative clustering is a well established strategy for identifying communities in networks. Communities are successively merged into larger communities, coarsening a network of actors into a more manageable network of communities. The order in which merges should occur is not in general clear, necessitating heuristics for selecting pairs of communities to merge. We describe a hierarchical clustering algorithm based on a local optimality property. For each edge in the network, we associate the modularity change for merging the communities it links. For each community vertex, we call the preferred edge that edge for which the modularity change is maximal. When an edge is preferred by both vertices that it links, it appears to be the optimal choice from the local viewpoint. We use the locally optimal edges to define the algorithm: simultaneously merge all pairs of communities that are connected by locally optimal edges that would increase the modularity, redetermining the locally optimal edges after each step and continuing so long as the modularity can be further increased. We apply the algorithm to model and empirical networks, demonstrating that it can efficiently produce high-quality community solutions. We relate the performance and implementation details to the structure of the resulting community hierarchies. We additionally consider a complementary local clustering algorithm, describing how to identify overlapping communities based on the local optimality condition.Comment: 10 pages; 4 tables, 3 figure

Cryogenic insulation technology review for the space shuttle

Cryogenic insulation systems for space shuttl

NetzCope: A Tool for Displaying and Analyzing Complex Networks

Networks are a natural and popular mechanism for the representation and investigation of a broad class of systems. But extracting information from a network can present significant challenges. We present NetzCope, a software application for the display and analysis of networks. Its key features include the visualization of networks in two or three dimensions, the organization of vertices to reveal structural similarity, and the detection and visualization of network communities by modularity maximization.Comment: 16 pages, Proceedings of ICQBIC2010; minor improvements to wording in v