Integrated or segmented? a wavelet transform analysis on relationship between stock and real estate markets

The goal of this paper is to investigate the relationship between stock and real estate markets via wavelet analysis. Based on wavelet transform, stock price index and REITs index are firstly decomposed into “volatility componentsâ€, that is, the wavelet coefficients. Secondly, we test the causality relationship between stock price index and REITs index of each subband under the concept of multi-resolution representation. The result revealed that the relationship between stock and real estates markets is neither simply segmented nor purely integrated; the behaviors would vary not only over various observation time scales but also with different REITs.REITS markets, stock markets, causality, wavelet transform

Direct Observation of Long-Term Durability of Superconductivity in YBa2_2Cu3_3O7_7-Ag2_2O Composites

We report direct observation of long-term durability of superconductivity of several YBa$_2$Cu$_3$O$_7$-Ag$_2$O composites that were first prepared and studied almost 14 years ago [J. J. Lin {\it et al}., Jpn. J. Appl. Phys. {\bf 29}, 497 (1990)]. Remeasurements performed recently on both resistances and magnetizations indicate a sharp critical transition temperature at 91 K. We also find that such long-term environmental stability of high-temperature superconductivity can only be achieved in YBa$_2$Cu$_3$O$_7$ with Ag$_2$O addition, but not with pure Ag addition.Comment: to be published in Jpn. J. Appl. Phy

The motion of a neutrally buoyant particle of an elliptic shape in two dimensional shear flow: a numerical study

In this paper, we investigate the motion of a neutrally buoyant cylinder of an elliptic shape freely moving in two dimensional shear flow by direct numerical simulation. An elliptic shape cylinder in shear flow, when initially being placed at the middle between two walls, either keeps rotating or has a stationary inclination angle depending on the particle Reynolds number $Re=G_r r_a^2/\nu$, where $G_r$ is the shear rate, $r_a$ is the semi-long axis of the elliptic cylinder and $\nu$ is the kinetic viscosity of the fluid. The critical particle Reynolds number $Re_{cr}$ for the transition from a rotating motion to a stationary orientation depends on the aspect ratio $AR=r_b/r_a$ and the confined ratio $K=2r_a/H$ where $r_b$ is the semi-short axis of the elliptic cylinder and $H$ is the distance between two walls. Although the increasing of either parameters makes an increase in $Re_{cr}$, the dynamic mechanism is distinct. The $AR$ variation causes the change of geometry shape; however, the $K$ variation influences the wall effect. The stationary inclination angle of non-rotating slender elliptic cylinder with smaller confined ratio seems to depend only on the value of $Re-Re_{cr}$. An expected equilibrium position of the cylinder mass center in shear flow is the centerline between two walls, but when placing the particle away from the centerline initially, it migrates either toward an equilibrium height away from the middle between two walls or back to the middle depending on the confined ratio and particle Reynolds number.Comment: arXiv admin note: substantial text overlap with arXiv:1209.080

Orbital Symmetry and Electron Correlation in Na_{x}CoO_2

Measurements of polarization-dependent soft x-ray absorption reveal that the electronic states determining the low-energy excitations of Na$_{x}$CoO$_2$ have predominantly $a_{1g}$ symmetry with significant O $2p$ character. A large transfer of spectral weight observed in O $1s$ x-ray absorption provides spectral evidence for strong electron correlations in the layered cobaltates. Comparing Co $2p$ x-ray absorption with calculations based on a cluster model, we conclude that Na$_{x}$CoO$_2$ exhibits a charge-transfer electronic character rather than a Mott-Hubbard character

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Anomalous electronic Raman scattering in Na_xCoO_2 H_2O

Raman scattering experiments on Na_{x}CoO_2 yH_2O single crystals show a broad electronic continuum with a pronounced peak around 100 cm-1 and a cutoff at approximately 560 cm-1over a wide range of doping levels. The electronic Raman spectra in superconducting and non-superconducting samples are similar at room temperature, but evolve in markedly different ways with decreasing temperature. For superconducting samples, the low-energy spectral weight is depleted upon cooling below T* sim 150K, indicating a opening of a pseudogap that is not present in non-superconducting materials. Weak additional phonon modes observed below T* suggest that the pseudogap is associated with charge ordering.Comment: 5 pages, 4 figures, for further information see www.peter-lemmens.d