Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade

© 2017 Elsevier B.V. A heated debate has arisen over whether the Asian premium (i.e., higher prices in Asia than elsewhere) in natural gas trade is due to price discrimination or different market fundamentals. Determining the origin of this premium can help to guide the gas industries and policy makers in Asia, especially when the traditional oil-indexed price mechanism fades away. Using a new systemic time-series approach, this paper explores the extent to which oil prices and market fundamentals contribute to variations in gas prices in Japan, the United States, and Germany. We find clear cross-country differences and time-varying patterns. Gas prices are much less affected by supply and demand factors than oil prices in Japan and Germany, whereas these factors are more important than oil prices in the US market, which has a pricing hub. Through rolling-windows and subsample analysis, we discover that oil prices were important in Japan and Germany, but the level of importance has declined significantly in recent years, though the contribution of fundamentals does not change much. The results show that Asian gas prices are determined more by oil prices than by the market fundamentals; thus the Asian premium is more likely due to this oil indexed pricing mechanism, rather than market fundamentals. This suggests that developing Asia's benchmark prices (through trading hubs) with a better reflection of regional specific fundamentals can lead to a more efficient allocation of gas resources

Robust Preparation of GHZ and W States of Three Distant Atoms

Schemes to generate Greenberger-Horne-Zeilinger(GHZ) and W states of three distant atoms are proposed in this paper. The schemes use the effects of quantum statistics of indistinguishable photons emitted by the atoms inside optical cavities. The advantages of the schemes are their robustness against detection inefficiency and asynchronous emission of the photons. Moreover, in Lamb-Dicke limit, the schemes do not require simultaneous click of the detectors, this makes the schemes more realizable in experiments.Comment: 5 pages, 1 fiure. Phys. Rev. A 75, 044301 (2007

Enhancement of Transition Temperature in FexSe0.5Te0.5 Film via Iron Vacancies

The effects of iron deficiency in FexSe0.5Te0.5 thin films (0.8<x<1) on superconductivity and electronic properties have been studied. A significant enhancement of the superconducting transition temperature (TC) up to 21K was observed in the most Fe deficient film (x=0.8). Based on the observed and simulated structural variation results, there is a high possibility that Fe vacancies can be formed in the FexSe0.5Te0.5 films. The enhancement of TC shows a strong relationship with the lattice strain effect induced by Fe vacancies. Importantly, the presence of Fe vacancies alters the charge carrier population by introducing electron charge carriers, with the Fe deficient film showing more metallic behavior than the defect-free film. Our study provides a means to enhance the superconductivity and tune the charge carriers via Fe vacancy, with no reliance on chemical doping.Comment: 15 pages, 4 figure

Effects of natural soiling and weathering on cool roof energy savings for dormitory buildings in Chinese cities with hot summers

Roofs with high-reflectance (solar reflectance) coating, commonly known as cool roofs, can stay cool in the sun, thereby reducing building energy consumption and mitigating the urban heat island. However, chemical-physical degradation and biological growth can decrease their solar reflectance and the ability to save energy. In this study, the solar spectral reflectance of 12 different roofing products with an initial albedo of 0.56–0.90 was measured before exposure and once every three months over 32 months. Specimens were exposed on the roofs of dormitory buildings in Xiamen and Chengdu, each major urban areas with hot summers. The albedos of high and medium-lightness coatings stabilized in the ranges 0.45–0.62 and 0.36–0.59 in both cities, respectively. This study yielded albedo loss exceeded those reported in the latest Chinese standard by 0.08–0.15. Finally, DesignBuilder (EnergyPlus) simulations estimate that a new cool roof with albedo 0.78 on a six-story dormitory building will yield annual site energy savings (heating and cooling) for the top floor, which are 8.01 kWh/m2 (24.2%) and 9.12 kWh/m2 (26.3%) per unit floor area in Xiamen and Chengdu, respectively; while an aged cool roof with albedo 0.45 and 0.56 will yield the annual savings by 5.12 kWh/m2 (15.4%) and 2.47 kWh/m2 (10.5%) in these two cities

Oscillation Induced Neutrino Asymmetry Growth in the Early Universe

We study the dynamics of active-sterile neutrino oscillations in the early universe using full momentum-dependent quantum-kinetic equations. These equations are too complicated to allow for an analytical treatment, and numerical solution is greatly complicated due to very pronounced and narrow structures in the momentum variable introduced by resonances. Here we introduce a novel dynamical discretization of the momentum variable which overcomes this problem. As a result we can follow the evolution of neutrino ensemble accurately well into the stable growing phase. Our results confirm the existence of a "chaotic region" of mixing parameters, for which the final sign of the asymmetry, and hence the SBBN prediction of He(4)-abundance cannot be accurately determined.Comment: 23 pages, 9 eps-figs, Latex, uses JHEP clas

Facial landmark detection via attention-adaptive deep network

Facial landmark detection is a key component of the face recognition pipeline as well as facial attribute analysis and face verification. Recently convolutional neural network-based face alignment methods have achieved significant improvement, but occlusion is still a major source of a hurdle to achieve good accuracy. In this paper, we introduce the attentioned distillation module in our previous work Occlusion-adaptive Deep Network (ODN) model, to improve performance. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module. It can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, the role of the geometry-aware module is, to excavate geometric relationships between different facial components. The role of attentioned distillation module is, to get rich feature representation and model occlusion. To improve feature representation, we used channel-wise attention and spatial attention. Experimental results show that our method performs better than existing methods

Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising