On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. annual mortality rate, along with higher-frequency regressors, e.g. daily temperature and precipitation. Practitioners use summaries of the higher-frequency regressors in fixed effects panel models. The choice over summary statistics amounts to model selection. Some practitioners use Monte Carlo cross-validation (MCCV) to justify a particular specification. However, conventional implementation of MCCV with fixed testing-to-full sample ratios tends to select over-fit models. This paper presents conditions under which MCCV, and also information criteria, can deliver consistent model selection. Previous work has established that the Bayesian information criterion (BIC) can be inconsistent for non-nested selection. We illustrate that the BIC can also be inconsistent in our framework, when all candidate models are misspecified. Our results have practical implications for empirical conventions in climate change impact studies. Specifically, they highlight the importance of a priori information provided by the scientific literature to guide the models considered for selection. We emphasize caution in interpreting model selection results in settings where the scientific literature does not specify the relationship between the outcome and the weather variables.Comment: Additional simulation results available from authors by reques

Berry's phase with quantized field driving: effects of inter-subsystem coupling

The effect of inter-subsystem couplings on the Berry phase of a composite system as well as that of its subsystem is investigated in this paper. We analyze two coupled spin-$\frac 1 2$ particles with one driven by a quantized field as an example, the pure state geometric phase of the composite system as well as the mixed state geometric phase for the subsystem is calculated and discussed.Comment: 4 pages, 1 figur

Coil combination using linear deconvolution in k-space for phase imaging

Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper. Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method. Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase. Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future

Multiparticle Entanglement in the Lipkin-Meshkov-Glick Model

The multiparticle entanglement in the Lipkin-Meshkov-Glick model has been discussed extensively in this paper. Measured by the global entanglement and its generalization, our calculation shows that the multiparticle entanglement can faithfully detect quantum phase transitions. For an antiferromagnetic case the multiparticle entanglement reaches the maximum at the transition point, whereas for ferromagnetic coupling, two different behaviors of multiparticle entanglement can be identified, dependent on the anisotropic parameter in the coupling.Comment: 7 pages and 5 figure

Full Length Research Paper Production of emodin from Aspergillus ochraceus at preparative scale

In order to study the chemical constituents in the pigmented culture produced from Aspergillus ochraceus, solid phase extraction method was employed to isolate the pigment molecules from the primary culture, followed by fractionation on preparative liquid chromatography. Structuralcharacterization confirmed that one of the two major pigment components in the culture was emodin (1,3,8 -trihydroxy-6-methyl-anthraquinone). It was observed that production of emodin started 2 days after the culture had reached the stationary phase. The culture conditions  were subsequently optimized to improve the yield of the emodin production. It was found that optimal production of emodin was achieved when fermentation was carried out at 32°C with the pH value of the culture medium at 7.0. Other conditions were also optimized, leading to the yield reaching as high as 0.8% of dry mass of A. ochraceus. The method described here offers an efficient approach for large scale production of emodin

Entanglement Effect on Off-diagonal Geometric Phase

The effect of entanglement on off-diagonal geometric phases is investigated in the paper. Two spin-1/2 particles in magnetic fields along the $y$ direction are taken as an example. Three parameters (the purity of state $r$, the mixing angle $\theta$ and the relative phase $\beta$) are chosen to characterize the initial states. The nodal points at which the usual geometric phases disappear are calculated and illustrated as a function of the three parameters.Comment: final version; appearing in Europhys. Lett. 74, 757(2006