Nonlinear Schrodinger equations with variable coefficients

Ph.DDOCTOR OF PHILOSOPH

Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems without Biased Variance Control

Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback. Moreover, because of its general availability, we see wide adoption of implicit feedback data, such as click signal. There are mainly two challenges for the application of implicit feedback. First, implicit data just includes positive feedback. Therefore, we are not sure whether the non-interacted items are really negative or positive but not displayed to the corresponding user. Moreover, the relevance of rare items is usually underestimated since much fewer positive feedback of rare items is collected compared with popular ones. To tackle such difficulties, both pointwise and pairwise solutions are proposed before for unbiased relevance learning. As pairwise learning suits well for the ranking tasks, the previously proposed unbiased pairwise learning algorithm already achieves state-of-the-art performance. Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias. In this work, we propose an unbiased pairwise learning method, named UPL, with much lower variance to learn a truly unbiased recommender model. Extensive offline experiments on real world datasets and online A/B testing demonstrate the superior performance of our proposed method.Comment: 5 page

Optimizing the thermoelectric performance of zigzag and chiral carbon nanotubes

Using nonequilibrium molecular dynamics simulations and nonequilibrium Green's function method, we investigate the thermoelectric properties of a series of zigzag and chiral carbon nanotubes which exhibit interesting diameter and chirality dependence. Our calculated results indicate that these carbon nanotubes could have higher ZT values at appropriate carrier concentration and operating temperature. Moreover, their thermoelectric performance can be significantly enhanced via isotope substitution, isoelectronic impurities, and hydrogen adsorption. It is thus reasonable to expect that carbon nanotubes may be promising candidates for high-performance thermoelectric materials

Extreme rainfall and snowfall alter responses of soil respiration to nitrogen fertilization : a 3-year field experiment

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 23 (2017): 3403-3417, doi:10.1111/gcb.13620.Extreme precipitation is predicted to be more frequent and intense accompanying global warming, and may have profound impacts on soil respiration (Rs) and its components, i.e., autotrophic (Ra) and heterotrophic (Rh) respiration. However, how natural extreme rainfall or snowfall events affect these fluxes are still lacking, especially under nitrogen (N) fertilization. In this study, extreme rainfall and snowfall events occurred during a 3-year field experiment, allowing us to examine their effects on the response of Rs, Rh and Ra to N supply. In normal rainfall years of 2011/2012 and 2012/2013, N fertilization significantly stimulated Rs by 23.9% and 10.9%, respectively. This stimulation was mainly due to the increase of Ra because of N-induced increase in plant biomass. In the record wet year of 2013/2014, however, Rs was independent on N supply because of the inhibition effect of the extreme rainfall event. Compared with those in other years, Rh and Ra were reduced by 36.8% and 59.1%, respectively, which were likely related to the anoxic stress on soil microbes and decreased photosynthates supply. Although N supply did not affect annual Rh, the response ratio (RR) of Rh flux to N fertilization decreased firstly during growing season, increased in nongrowing season and peaked during spring thaw in each year. Nongrowing season Rs and Rh contributed 5.5–16.4% to their annual fluxes, and were higher in 2012/2013 than other years due to the extreme snowfall inducing higher soil moisture during spring thaw. The RR of nongrowing season Rs and Rh decreased in years with extreme snowfall or rainfall compared to those in normal years. Overall, our results highlight the significant effects of extreme precipitation on responses of Rs and its components to N fertilization, which should be incorporated into models to improve the prediction of carbon-climate feedbacks.This research was funded by the Chinese Academy of Sciences (XDB15020100) and the National Natural Science Foundation of China (31561143011).2017-12-2

Preparation Method of Co 3

Co3O4 nanoparticles were fabricated by a novel, facile, and environment-friendly carbon-assisted method using degreasing cotton. Structural and morphological characterizations were performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The component of the sample obtained at different temperatures was measured by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Nitrogen adsorption and desorption isotherms were utilized to reveal the specific surface areas. The formation mechanism of Co3O4 nanoparticles was also proposed, demonstrating that the additive degreasing cotton played an indispensable role in the process of synthesizing the sample. The resultant Co3O4 sample calcined at 600°C exhibited superior electrochemical performance with better specific capacitance and long-term cycling life, due to its high specific surface areas and pores structures. Additionally, it has been proved that this facile synthetic strategy can be extended to produce other metal oxide materials (e.g., Fe3O4). As a consequence, the carbon-assisted method using degreasing cotton accompanied a promising prospect for practical application