Effects of initial soil fabric and mode of shearing on quasi-steady state line for monotonic undrained behavior

The effects of initial soil fabric and mode of shearing on quasi-steady state line in void ratiostress space are studied by employing the Distinct Element Method numerical analysis. The results show that the initial soil fabric and the mode of shearing have a profound effect on the location of the quasi-steady state line. The evolution of the soil fabric during the course of undrained shearing shows that the specimens with different initial soil fabrics reach quasi-steady state at various soil fabric conditions. At quasi-steady state, the soil fabric has a significant adjustment to change its behavior from contractive to dilative. As the stress state approaches the steady state, the soil fabrics of different initial conditions become similar. The numerical analysis results are compared qualitatively with the published experimental data and the effects of specimen reconstitution methods and mode of shearing found in the experimental studies canbe systematically explained by the numerical analysis. © 2009 Taylor & Francis Group.

New space-time trellis codes for two-antenna quasi-static channels

New space-time trellis codes with four- and eight-level phase-shift keying (PSK) and 16-phase quadrature amplitude modulation (QAM) for two transmit antennas in slow-fading channels are presented in this paper. Unlike most of the codes that are reported in the literature, the proposed codes are specifically designed to minimize the frame error probability from a union-bound perspective. The performance of the proposed codes with various memory orders and receive antennas is evaluated by simulation. It is shown that the proposed codes outperform previously known codes in all studied cases.

Phase synchronization between stratospheric and tropospheric quasi-biennial and semi-annual oscillations

A combination of singular systems analysis and analytic phase techniques are used to investigate the possible occurrence in observations of coherent synchronization between quasi-biennial and semi-annual oscillations (QBOs; SAOs) in the stratosphere and troposphere. Time series of zonal mean zonal winds near the Equator are analysed from the ERA-40 and ERA-interim reanalysis datasets over a ∼ 50-year period. In the stratosphere, the QBO is found to synchronize with the SAO almost all the time, but with a frequency ratio that changes erratically between 4:1, 5:1 and 6:1. A similar variable synchronization is also evident in the tropical troposphere between semi-annual and quasi-biennial cycles (known as TBOs). Mean zonal winds from ERA-40 and ERA-interim, and also time series of indices for the Indian and West Pacific monsoons, are commonly found to exhibit synchronization, with SAO/TBO ratios that vary between 4:1 and 7:1. Coherent synchronization between the QBO and tropical TBO does not appear to persist for long intervals, however. This suggests that both the QBO and tropical TBOs may be separately synchronized to SAOs that are themselves enslaved to the seasonal cycle, or to the annual cycle itself. However, the QBO and TBOs are evidently only weakly coupled between themselves and are frequently found to lose mutual coherence when each changes its frequency ratio to its respective SAO. This suggests a need to revise a commonly cited paradigm that advocates the use of stratospheric QBO indices as a predictor for tropospheric phenomena such as monsoons and hurricanes. © 2012 Royal Meteorological Society.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.