Neutrino Emission From Direct Urca Processes in Pion Condensed Quark Matter

We study neutrino emission from direct Urca processes in pion condensed quark matter. In compact stars with high baryon density, the emission is dominated by the gapless modes of the pion condensation which leads to an enhanced emissivity. While for massless quarks the enhancement is not remarkable, the emissivity is significantly larger and the cooling of the condensed matter is considerably faster than that in normal quark matter when the mass difference between $u$- and $d$-quarks is sizable.Comment: 12 pages,6 figures, published versio

Neutrino Emission From Inhomogeneous Pion Condensed Quark Matter

It is believed that quark matter can exist in neutron star interior if the baryon density is high enough. When there is a large isospin density, quark matter could be in a pion condensed phase. We compute neutrino emission from direct Urca processes in such a phase, particularly in the inhomogeneous Larkin-Ovchinnikov-Fulde-Ferrell (LOFF) states. The neutrino emissivity and specific heat are obtained, from which the cooling rate is estimated.Comment: 10 pages, 5 figures. A new reference added,published versio

The uncertainty analysis of the MODIS GPP product in global maize croplands

Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD was underestimated by 6%‒58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPPMOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPPMOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle

Asymmetric Fermion Superfluid with Inter- and Intra-Species Pairings

We investigate the phase structure of an asymmetric fermion superfluid with inter- and intra-species pairings. The introduction of the intra-species pairing mechanism in canonical ensemble changes significantly the phase diagram and brings in a new state with coexisting inter- and intra-species pairings. Different from the case with only inter-species pairing, all the fermion excitations are fully gapped in the region with intra-species pairing.Comment: 5 pages, 4 figure

Numerical Modeling of a Teeth-shaped Nano-plasmonic Waveguide Filter

In this paper, tooth-shaped and multiple-teeth-shaped plasmonic filters in the metal-insulator-metal (MIM) waveguides are demonstrated numerically. By introducing a three-port waveguide splitter, a modified model based on the multiple-beam-interference and the scattering matrix is given. The ransmittance spectrum as a function of teeth width, depth, period and period number are respectively addressed. The result shows the new structure not only performs the filtering function as well as MIM grating-like structures, but also is of submicrometer size for ultra-high integration and relatively easy fabrication.Comment: 21pages, 7 figure

Effect of Chiral Symmetry Restoration on Pentaquark Θ+\Theta^+ Mass and Width at Finite Temperature and Density

We investigate the effect of chiral phase transition on the pentaquark $\Theta^+$ mass and width at one-loop level of $N\Theta^+K$ coupling at finite temperature and density. The behavior of the mass, especially the width in hadronic medium is dominated by the characteristics of chiral symmetry restoration at high temperature and high density. The mass and width shifts of positive-parity $\Theta^+$ are much larger than that of negative-parity one, which may be helpful to determine the parity of $\Theta^+$ in high energy nuclear collisions.Comment: 7 pages, 5 figure

Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions

The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of “uninformative” samples that should be ignored, thus incurring extra calculations. To solve this open problem, we propose to design bandit sampling method to make these algorithms focus on “informative” samples during training process. Our contribution is twofold: first, we propose a convex optimization algorithm with bandit sampling, termed AdaBeliefBS, and prove that it converges faster than its original version; second, we prove that bandit sampling works well for strongly convex algorithms, and propose a generalized SAdam, called SAdamBS, that converges faster than SAdam. Finally, we conduct a series of experiments on various benchmark datasets to verify the fast convergence rate of our proposed algorithms

FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity

AdaBelief, one of the current best optimizers, demonstrates superior generalization ability over the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in which it has a data-dependent O(√T) regret bound when objective functions are convex, where T is a time horizon. It remains, however, an open problem whether the convergence rate can be further improved without sacrificing its generalization ability. To this end, we make the first attempt in this work and design a novel optimization algorithm called FastAdaBelief that aims to exploit its strong convexity in order to achieve an even faster convergence rate. In particular, by adjusting the step size that better considers strong convexity and prevents fluctuation, our proposed FastAdaBelief demonstrates excellent generalization ability and superior convergence. As an important theoretical contribution, we prove that FastAdaBelief attains a data-dependent O(log T) regret bound, which is substantially lower than AdaBelief in strongly convex cases. On the empirical side, we validate our theoretical analysis with extensive experiments in scenarios of strong convexity and nonconvexity using three popular baseline models. Experimental results are very encouraging: FastAdaBelief converges the quickest in comparison to all mainstream algorithms while maintaining an excellent generalization ability, in cases of both strong convexity or nonconvexity. FastAdaBelief is, thus, posited as a new benchmark model for the research community