No Evidence for Dark Energy Dynamics from a Global Analysis of Cosmological Data

We use a variant of principal component analysis to investigate the possible temporal evolution of the dark energy equation of state, w(z). We constrain w(z) in multiple redshift bins, utilizing the most recent data from Type Ia supernovae, the cosmic microwave background, baryon acoustic oscillations, the integrated Sachs-Wolfe effect, galaxy clustering, and weak lensing data. Unlike other recent analyses, we find no significant evidence for evolving dark energy; the data remains completely consistent with a cosmological constant. We also study the extent to which the time-evolution of the equation of state would be constrained by a combination of current- and future-generation surveys, such as Planck and the Joint Dark Energy Mission.Comment: 6 pages, 5 figure

SAnoVs: Secure Anonymous Voting Scheme for clustered ad hoc networks

In this paper we propose a secure anonymous voting scheme (SAnoVS) for re-clustering in the ad-hoc network. SAnoVS extends our previous work of degree-based clustering algorithms by achieving anonymity and confidentiality of the voting procedure applied to select new cluster heads. The security of SAnoVS is based on the difficulty of computing discrete logarithms over elliptic curves, the intractability of inverting a one-way hash function and the fact that only neighboring nodes contribute to the generation of a shared secret. Furthermore, we achieve anonymity since our scheme does not require any identification information as we make use of a polynomial equation system combined with pseudo-random coordinates. The security analysis of our scheme is demonstrated with several attacks scenarios.examined with several attack scenarios and experimental results

Crystallization and melting of bacteria colonies and Brownian Bugs

Motivated by the existence of remarkably ordered cluster arrays of bacteria colonies growing in Petri dishes and related problems, we study the spontaneous emergence of clustering and patterns in a simple nonequilibrium system: the individual-based interacting Brownian bug model. We map this discrete model into a continuous Langevin equation which is the starting point for our extensive numerical analyses. For the two-dimensional case we report on the spontaneous generation of localized clusters of activity as well as a melting/freezing transition from a disordered or isotropic phase to an ordered one characterized by hexagonal patterns. We study in detail the analogies and differences with the well-established Kosterlitz-Thouless-Halperin-Nelson-Young theory of equilibrium melting, as well as with another competing theory. For that, we study translational and orientational correlations and perform a careful defect analysis. We find a non standard one-stage, defect-mediated, transition whose nature is only partially elucidated.Comment: 13 Figures. 14 pages. Submitted to Phys. Rev.

Self-Calibrated Cluster Counts as a Probe of Primordial Non-Gaussianity

We show that the ability to probe primordial non-Gaussianity with cluster counts is drastically improved by adding the excess variance of counts which contains information on the clustering. The conflicting dependences of changing the mass threshold and including primordial non-Gaussianity on the mass function and biasing indicate that the self-calibrated cluster counts well break the degeneracy between primordial non-Gaussianity and the observable-mass relation. Based on the Fisher matrix analysis, we show that the count variance improves constraints on f_NL by more than an order of magnitude. It exhibits little degeneracy with dark energy equation of state. We forecast that upcoming Hyper Suprime-cam cluster surveys and Dark Energy Survey will constrain primordial non-Gaussianity at the level \sigma(f_NL) \sim 8, which is competitive with forecasted constraints from next-generation cosmic microwave background experiments.Comment: 4 pages, 3 figures, accepted for publication in PR

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Tangling clustering of inertial particles in stably stratified turbulence

We have predicted theoretically and detected in laboratory experiments a new type of particle clustering (tangling clustering of inertial particles) in a stably stratified turbulence with imposed mean vertical temperature gradient. In this stratified turbulence a spatial distribution of the mean particle number density is nonuniform due to the phenomenon of turbulent thermal diffusion, that results in formation of a gradient of the mean particle number density, \nabla N, and generation of fluctuations of the particle number density by tangling of the gradient, \nabla N, by velocity fluctuations. The mean temperature gradient, \nabla T, produces the temperature fluctuations by tangling of the gradient, \nabla T, by velocity fluctuations. These fluctuations increase the rate of formation of the particle clusters in small scales. In the laboratory stratified turbulence this tangling clustering is much more effective than a pure inertial clustering that has been observed in isothermal turbulence. In particular, in our experiments in oscillating grid isothermal turbulence in air without imposed mean temperature gradient, the inertial clustering is very weak for solid particles with the diameter 10 microns and Reynolds numbers Re =250. Our theoretical predictions are in a good agreement with the obtained experimental results.Comment: 16 pages, 4 figures, REVTEX4, revised versio

Neutral Aggregation in Finite Length Genotype space

The advent of modern genome sequencing techniques allows for a more stringent test of the neutrality hypothesis of Darwinian evolution, where all individuals have the same fitness. Using the individual based model of Wright and Fisher, we compute the amplitude of neutral aggregation in the genome space, i.e., the probability of finding two individuals at genetic (hamming) distance k as a function of genome size L, population size N and mutation probability per base \nu. In well mixed populations, we show that for N\nu\textless{}1/L, neutral aggregation is the dominant force and most individuals are found at short genetic distances from each other. For N\nu\textgreater{}1 on the contrary, individuals are randomly dispersed in genome space. The results are extended to geographically dispersed population, where the controlling parameter is shown to be a combination of mutation and migration probability. The theory we develop can be used to test the neutrality hypothesis in various ecological and evolutionary systems