Distance, Growth Factor, and Dark Energy Constraints from Photometric Baryon Acoustic Oscillation and Weak Lensing Measurements

Baryon acoustic oscillations (BAOs) and weak lensing (WL) are complementary probes of cosmology. We explore the distance and growth factor measurements from photometric BAO and WL techniques and investigate the roles of the distance and growth factor in constraining dark energy. We find for WL that the growth factor has a great impact on dark energy constraints but is much less powerful than the distance. Dark energy constraints from WL are concentrated in considerably fewer distance eigenmodes than those from BAO, with the largest contributions from modes that are sensitive to the absolute distance. Both techniques have some well determined distance eigenmodes that are not very sensitive to the dark energy equation of state parameters w_0 and w_a, suggesting that they can accommodate additional parameters for dark energy and for the control of systematic uncertainties. A joint analysis of BAO and WL is far more powerful than either technique alone, and the resulting constraints on the distance and growth factor will be useful for distinguishing dark energy and modified gravity models. The Large Synoptic Survey Telescope (LSST) will yield both WL and angular BAO over a sample of several billion galaxies. Joint LSST BAO and WL can yield 0.5% level precision on ten comoving distances evenly spaced in log(1+z) between redshift 0.3 and 3 with cosmic microwave background priors from Planck. In addition, since the angular diameter distance, which directly affects the observables, is linked to the comoving distance solely by the curvature radius in the Friedmann-Robertson-Walker metric solution, LSST can achieve a pure metric constraint of 0.017 on the mean curvature parameter Omega_k of the universe simultaneously with the constraints on the comoving distances.Comment: 15 pages, 9 figures, details and references added, ApJ accepte

Effect of Hot Baryons on the Weak-Lensing Shear Power Spectrum

We investigate the impact of the intracluster medium on the weak-lensing shear power spectrum (PS). Using a halo model we find that, compared to the dark matter only case, baryonic pressure leads to a suppression of the shear PS on the order of a few percent or more for $l \gtrsim 1000$. Cooling/cooled baryons and the intergalactic medium can further alter the shear PS. Therefore, the interpretation of future precision weak lensing data at high multipoles must take into account the effects of baryons.Comment: 4 pages, 3 figure

The Miscible-Immiscible Quantum Phase Transition in Coupled Two-Component Bose-Einstein Condensates in 1D Optical Lattices

Using numerical techniques, we study the miscible-immiscible quantum phase transition in a linearly coupled binary Bose-Hubbard model Hamiltonian that can describe low-energy properties of a two-component Bose-Einstein condensate in optical lattices. With the quantum many-body ground state obtained from density matrix renormalization group algorithm, we calculate the characteristic physical quantities of the phase transition controlled by the linear coupling between two components. Furthermore we calculate the Binder cumulant to determine the critical point and draw the phase diagram. The strong-coupling expansion shows that in the Mott insulator regime the model Hamiltonian can be mapped to a spin 1/2 XXZ model with a transverse magnetic field.Comment: 10 pages, 10 figures, submitted to Phys. Rev.

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness

Multiparty Quantum Secret Sharing

Based on a quantum secure direct communication (QSDC) protocol [Phys. Rev. A69(04)052319], we propose a $(n,n)$-threshold scheme of multiparty quantum secret sharing of classical messages (QSSCM) using only single photons. We take advantage of this multiparty QSSCM scheme to establish a scheme of multiparty secret sharing of quantum information (SSQI), in which only all quantum information receivers collaborate can the original qubit be reconstructed. A general idea is also proposed for constructing multiparty SSQI schemes from any QSSCM scheme

Comprehensive profiling of zebrafish hepatic proximal promoter CpG island methylation and its modification during chemical carcinogenesis

Background\ud DNA methylation is an epigenetic mechanism associated with regulation of gene expression and it is modulated during chemical carcinogenesis. The zebrafish is increasingly employed as a human disease model; however there is a lack of information on DNA methylation in zebrafish and during fish tumorigenesis. \ud \ud Results\ud A novel CpG island tiling array containing 44,000 probes, in combination with immunoprecipitation of methylated DNA, was used to achieve the first comprehensive methylation profiling of normal adult zebrafish liver. DNA methylation alterations were detected in zebrafish liver tumors induced by the environmental carcinogen 7, 12-dimethylbenz(a)anthracene. Genes significantly hypomethylated in tumors were associated particularly with proliferation, glycolysis, transcription, cell cycle, apoptosis, growth and metastasis. Hypermethylated genes included those associated with anti-angiogenesis and cellular adhesion. Of 49 genes that were altered in expression within tumors, and which also had appropriate CpG islands and were co-represented on the tiling array, approximately 45% showed significant changes in both gene expression and methylation. \ud \ud Conclusion\ud The functional pathways containing differentially methylated genes in zebrafish hepatocellular carcinoma have also been reported to be aberrantly methylated during tumorigenesis in humans. These findings increase the confidence in the use of zebrafish as a model for human cancer in addition to providing the first comprehensive mapping of DNA methylation in the normal adult zebrafish liver. \ud \u

An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc