Visual saliency is a useful cue to locate the conspicuous image content. To estimate saliency, many approaches have been proposed to detect the unique or rare visual stimuli. However, such bottom-up solutions are often insufficient since the prior knowledge, which often indicates a biased selectivity on the input stimuli, is not taken into account. To solve this problem, this paper presents a novel approach to estimate image saliency by learning the prior knowledge. In our approach, the influences of the visual stimuli and the prior knowledge are jointly incorporated into a Bayesian framework. In this framework, the bottom-up saliency is calculated to pop-out the visual subsets that are probably salient, while the prior knowledge is used to recover the wrongly suppressed targets and inhibit the improperly popped-out distractors. Compared with existing approaches, the prior knowledge used in our approach, including the foreground prior and the correlation prior, is statistically learned from 9.6 million images in an unsupervised manner. Experimental results on two public benchmarks show that such statistical priors are effective to modulate the bottom-up saliency to achieve impressive improvements when compared with 10 state-of-the-art methods.
Two water-soluble triscyclometalated organoiridium complexes, 1 and 2, with polar side chains that form nanoparticles emitting bright-red phosphorescence in water were synthesized. The optimal emitting properties are related to both the triscyclometalated structure and nanoparticle-forming ability in aqueous solution. Nonlinear optical properties are also observed with the nanoparticles. Because of their proper cellular uptake in addition to high emission brightness and effective two-photon absorbing ability, cell imaging can be achieved with nanoparticles of 2 bearing quaternary ammonium side chains at ultra-low effective concentrations using NIR incident light via the multiphoton excitation phosphorescence process.
A satellite-based water balance method is developed to model global evapotranspiration (ET) through coupling a water balance (WB) model with a machine-learning algorithm (the model tree ensemble, MTE) (hereafter WB-MTE). The WB-MTE algorithm was firstly trained by combining monthly WB-estimated basin ET with the potential drivers (e.g., radiation, temperature, precipitation, wind speed, and vegetation index) across 95 large river basins (5824 basin-months) and then applied to establish global monthly ET maps at a spatial resolution of 0.5 degrees from 1982 to 2009. The global land ET estimated from WB-MTE has an annual mean of 59317mm for 1982-2009, with a spatial distribution consistent with previous studies in all latitudes but the tropics. The ET estimated by WB-MTE also shows significant linear trends in both annual and seasonal global ET during 1982-2009, though the trends seem to have stalled after 1998. Moreover, our study presents a striking difference from the previous ones primarily in the magnitude of ET estimates during the wet season particularly in the tropics, where ET is highly uncertain due to lack of direct measurements. This may be tied to their lack of proper consideration to solar radiation and/or the rainfall interception process. By contrast, in the dry season, our estimate of ET compares well with the previous ones, both for the mean state and the variability. If we are to reduce the uncertainties in estimating ET, these results emphasize the necessity of deploying more observations during the wet season, particularly in the tropics.Key Points<list list-type="bulleted" id="jgrd51103-list-0001"><list-item id="jgrd51103-li-0001">Developed a satellite-based water balance method to estimate global ET <list-item id="jgrd51103-li-0002">Significant seasonal and spatial variations exist in global terrestrial ET <list-item id="jgrd51103-li-0003">The method improved ET estimations in wet regions and seasons
Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This article extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with a pth-order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.
<正>哈佛大学教授尤查·本克勒(Yochai Benkler)最早开始关注由个人以及或松散或紧密的合作者进行的非市场化、非专有化的生产,认定它们在信息、知识和文化交换中所起的作用日益加大。他为此撰写了煌煌大作《网络的财富》(The Wealth of Networks,2006年出版),该书虽然火花四射,但却是以学术方式撰写的大部头著作,读来令人头疼。或许是为了证明自己也会讲故事,2011年本克勒的下一本著作《企鹅与怪兽》(The Penguin and the Leviathan)一反常态,充满了各种轶事,连一个脚注、一本参考文献都没有。它堪称一本有关人