Factorization and Unitarity in Superstring Theory

The overall coefficient of the two-loop 4-particle amplitude in superstring theory is determined by making use of the factorization and unitarity. To accomplish this we computed in detail all the relevant tree and one-loop amplitudes involved and determined their overall coefficients in a consistent way.Comment: LaTex file, 19 pages, 4 figures; v2, minor corrections and figures corrected; v3, minor corrections with the English, to be published in JHE

Rare decays Bs→l+l−B_s\to l^+l^- and B→Kl+l−B\to Kl^+l^- in \the topcolor-assisted technicolor model

We examine the rare decays $B_s\to l^+l^-$ and $B\to Kl^+l^-$ in the framework of the topcolor-assisted technicolor ($TC2$) model. The contributions of the new particles predicted by this model to these rare decay processes are evaluated. We find that the values of their branching ratios are larger than the standard model predictions by one order of magnitude in wide range of the parameter space. The longitudinal polarization asymmetry of leptons in $B_s \to l^+l^-$ can approach \ord(10^{-2}). The forward-backward asymmetry of leptons in $B \to Kl^+l^-$ is not large enough to be measured in future experiments. We also give some discussions about the branching ratios and the asymmetry observables related to these rare decay processes in the littlest Higgs model with T-parity.Comment: 29 pages, 9 figure, corrected typos, the version to appear in PR

Factorization of the Two Loop Four-Particle Amplitude in Superstring Theory Revisited

We study in detail the factorization of the newly obtained two-loop four-particle amplitude in superstring theory. In particular some missing factors from the scalar correlators are obtained correctly, in comparing with a previous study of the factorization in two-loop superstring theory. Some details for the calculation of the factorization of the kinematic factor are also presented.Comment: 11 pages, 1 figure; v2, minor corrections and references update

Hydrostatic pressure effects on the static magnetism in Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2}

The effects of hydrostatic pressure on the static magnetism in Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ are investigated by complementary electrical resistivity, ac magnetic susceptibility and single-crystal neutron diffraction measurements. A specific pressure-temperature phase diagram of Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ is established. The structural phase transition, as well as the spin-density-wave order of Fe sublattice, is suppressed gradually with increasing pressure and disappears completely above 2.0 GPa. In contrast, the magnetic order of Eu sublattice persists over the whole investigated pressure range up to 14 GPa, yet displaying a non-monotonic variation with pressure. With the increase of the hydrostatic pressure, the magnetic state of Eu evolves from the canted antiferromagnetic structure in the ground state, via a pure ferromagnetic structure under the intermediate pressure, finally to a possible "novel" antiferromagnetic structure under the high pressure. The strong ferromagnetism of Eu coexists with the pressure-induced superconductivity around 2 GPa. The change of the magnetic state of Eu in Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ upon the application of hydrostatic pressure probably arises from the modification of the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between the Eu$^{2+}$ moments tuned by external pressure.Comment: 9 pages, 6 figure

Excitation of nonlinear ion acoustic waves in CH plasmas

Excitation of nonlinear ion acoustic wave (IAW) by an external electric field is demonstrated by Vlasov simulation. The frequency calculated by the dispersion relation with no damping is verified much closer to the resonance frequency of the small-amplitude nonlinear IAW than that calculated by the linear dispersion relation. When the wave number $k\lambda_{De}$ increases, the linear Landau damping of the fast mode (its phase velocity is greater than any ion's thermal velocity) increases obviously in the region of $T_i/T_e < 0.2$ in which the fast mode is weakly damped mode. As a result, the deviation between the frequency calculated by the linear dispersion relation and that by the dispersion relation with no damping becomes larger with $k\lambda_{De}$ increasing. When $k\lambda_{De}$ is not large, such as $k\lambda_{De}=0.1, 0.3, 0.5$, the nonlinear IAW can be excited by the driver with the linear frequency of the modes. However, when $k\lambda_{De}$ is large, such as $k\lambda_{De}=0.7$, the linear frequency can not be applied to exciting the nonlinear IAW, while the frequency calculated by the dispersion relation with no damping can be applied to exciting the nonlinear IAW.Comment: 10 pages, 9 figures, Accepted by POP, Publication in August 1

Upscaling fluxes from towers to regions, continents and global scales using datadriven approaches

Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates

Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction