Grid service orchestration using the Business Process Execution Language (BPEL)

Modern scientific applications often need to be distributed across grids. Increasingly applications rely on services, such as job submission, data transfer or data portal services. We refer to such services as grid services. While the invocation of grid services could be hard coded in theory, scientific users want to orchestrate service invocations more flexibly. In enterprise applications, the orchestration of web services is achieved using emerging orchestration standards, most notably the Business Process Execution Language (BPEL). We describe our experience in orchestrating scientific workflows using BPEL. We have gained this experience during an extensive case study that orchestrates grid services for the automation of a polymorph prediction application

Fast quasi-synchronous harmonic algorithm based on weight window function- mixed radix FFT

According to the requirements of IEC61850-9-2LE, digital energy metering devices mainly adopt 80×fr fixed sampling rate. When the harmonic analysis is carried out under asynchronous sampling, it will produce large errors due to spectral leakage. Quasi-Synchronous Algorithm has high accuracy, but the calculation process is complicated and the hardware overheads are high. Based on the characteristics of digital energy metering devices, this paper puts forward a Fast Quasi-Synchronous Harmonic Algorithm using weight window function combined with Mixed Radix Fast Fourier Transform Algorithm. It will reduce the calculation by more than 94%. Compared with the Triangle/Hanning/Nuttall4(III)-Windowed Interpolated FFT Algorithm, the proposed algorithm will perform better in accuracy and has the feature that the more asynchronous of the sampling, the more obvious the error will be

On the Rigorous Derivation of the 3D Cubic Nonlinear Schr\"odinger Equation with A Quadratic Trap

We consider the dynamics of the 3D N-body Schr\"{o}dinger equation in the presence of a quadratic trap. We assume the pair interaction potential is N^{3{\beta}-1}V(N^{{\beta}}x). We justify the mean-field approximation and offer a rigorous derivation of the 3D cubic NLS with a quadratic trap. We establish the space-time bound conjectured by Klainerman and Machedon [30] for {\beta} in (0,2/7] by adapting and simplifying an argument in Chen and Pavlovi\'c [7] which solves the problem for {\beta} in (0,1/4) in the absence of a trap.Comment: Revised according to the referee report. Accepted to appear in Archive for Rational Mechanics and Analysi

Stripes in Quantum Hall Double Layer Systems

We present results of a study of double layer quantum Hall systems in which each layer has a high-index Landau level that is half-filled. Hartree-Fock calculations indicate that, above a critical layer separation, the system becomes unstable to the formation of a unidirectional coherent charge density wave (UCCDW), which is related to stripe states in single layer systems. The UCCDW state supports a quantized Hall effect when there is tunneling between layers, and is {\it always} stable against formation of an isotropic Wigner crystal for Landau indices $N \ge 1$. The state does become unstable to the formation of modulations within the stripes at large enough layer separation. The UCCDW state supports low-energy modes associated with interlayer coherence. The coherence allows the formation of charged soliton excitations, which become gapless in the limit of vanishing tunneling. We argue that this may result in a novel {\it ``critical Hall state''}, characterized by a power law $I-V$ in tunneling experiments.Comment: 10 pages, 8 figures include

Boronic Acid Derivatives Targeting HIV-1

A series of novel boronic acid derivatives containing either a pyrimidine or purine base was synthesized. The preparation involved the condensation of 4-bromobutyl boronic acid with the appropriate base. These acyclic nucleosides were designed as potential antiviral agents especially targeting the human immunodeficiency virus. Two analogues, 6-chloro-9-(4-dihydroxyborylbutyl)purine and 2,6-dichloro-9-(4-dihydroxyborylbutyl)purine, exhibited EC50 values of 7.7 µM and 0.99 µM, respectively, in an HIV-1 syncytial plaque reduction assay

Thermal Unparticles: A New Form of Energy Density in the Universe

Unparticle \U with scaling dimension d_\U has peculiar thermal properties due to its unique phase space structure. We find that the equation of state parameter \omega_\U, the ratio of pressure to energy density, is given by 1/(2d_\U +1) providing a new form of energy in our universe. In an expanding universe, the unparticle energy density \rho_\U(T) evolves dramatically differently from that for photons. For d_\U >1, even if \rho_\U(T_D) at a high decoupling temperature $T_D$ is very small, it is possible to have a large relic density \rho_\U(T^0_\gamma) at present photon temperature $T^0_\gamma$, large enough to play the role of dark matter. We calculate $T_D$ and \rho_\U(T^0_\gamma) using photon-unparticle interactions for illustration.Comment: 5 pages; v3, journal version

Enhanced local-type inflationary trispectrum from a non-vacuum initial state

We compute the primordial trispectrum for curvature perturbations produced during cosmic inflation in models with standard kinetic terms, when the initial quantum state is not necessarily the vacuum state. The presence of initial perturbations enhances the trispectrum amplitude for configuration in which one of the momenta, say $k_3$, is much smaller than the others, $k_3 \ll k_{1,2,4}$. For those squeezed configurations the trispectrum acquires the so-called local form, with a scale dependent amplitude that can get values of order $\epsilon ({k_1}/{k_3})^2$. This amplitude can be larger than the prediction of the so-called Maldacena consistency relation by a factor $10^6$, and can reach the sensitivity of forthcoming observations, even for single-field inflationary models.Comment: 11 pages, 1 figure. References added, typos corrected, minor change

S3 x Z2 model for neutrino mass matrices

We propose a model for lepton mass matrices based on the seesaw mechanism, a complex scalar gauge singlet and a horizontal symmetry S_3 \times \mathbbm{Z}_2. In a suitable weak basis, the charged-lepton mass matrix and the neutrino Dirac mass matrix are diagonal, but the vacuum expectation value of the scalar gauge singlet renders the Majorana mass matrix of the right-handed neutrinos non-diagonal, thereby generating lepton mixing. When the symmetry $S_3$ is not broken in the scalar potential, the effective light-neutrino Majorana mass matrix enjoys $\mu$--$\tau$ interchange symmetry, thus predicting maximal atmospheric neutrino mixing together with $U_{e3} = 0$. A partial and less predictive form of $\mu$--$\tau$ interchange symmetry is obtained when the symmetry $S_3$ is softly broken in the scalar potential. Enlarging the symmetry group S_3 \times \mathbbm{Z}_2 by an additional discrete electron-number symmetry \mathbbm{Z}_2^{(e)}, a more predicitive model is obtained, which is in practice indistinguishable from a previous one based on the group $D_4$.Comment: 13 pages, 3 figures, final version for publication in JHE

Continuous-variable Werner state: separability, nonlocality, squeezing and teleportation

We investigate the separability, nonlocality and squeezing of continuous-variable analogue of the Werner state: a mixture of pure two-mode squeezed vacuum state with local thermal radiations. Utilizing this Werner state, coherent-state teleportation in Braunstein-Kimble setup is discussed.Comment: 7 pages, 4 figure

A novel structure-aware sparse learning algorithm for brain imaging genetics

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings