科研成果 by Year: 2012

In this paper, a group-sensitive multiple kernel learning (GS-MKL) method is proposed for object recognition to accommodate the intra-class diversity and the inter-class correlation. By introducing the group between the object category and individual images as an intermediate representation, GS-MKL attempts to learn group-sensitive multi-kernel combinations together with the associated classifier. For each object category, the image corpus from the same category is partitioned into groups. Images with similar appearance are partitioned into the same group, which corresponds to the sub-category of the object category. Accordingly, intra-class diversity can be represented by the set of groups from the same category but with diverse appearances; Inter-class correlation can be represented by the correlation between the groups from different categories. GS-MKL provides a tractable solution to adapt multikernel combination to local data distribution and to seek a trade-off between capturing the diversity and keeping the invariance for each object category. Different from the simple hybrid grouping strategy that solves sample grouping and GS-MKL training independently, two sample grouping strategies are proposed to integrate sample grouping and GS-MKL training. The first one is looping hybrid grouping method where global kernel clustering method and GS-MKL interact with each other by sharing group-sensitive multi- kernel combination. The second one is dynamic divisive grouping method where hierarchical kernel-based grouping process interacts with GS-MKL. Experimental results show that performance of GS-MKL does not vary significantly with different grouping strategies, but looping hybrid grouping method produces slightly better results. On four challenging datasets, our proposed method has achieved encouraging performance comparable to the state-of-the-art and outperformed several existing MKL methods.

How can one country narrow the regional disparity during the tremendous expansion of higher education? This issue remains unexamined and critical analysis is needed to unveil the spatial dynamics behind expansion of higher education. The spatial analysis shows that there is significant strategic interaction among neighboring provinces in China during the expansion of higher education. It implies that the expansion of higher education intensifies spatial interaction among provinces which facilitates narrowing gap of higher education among provinces in China. The convergence analysis shows that the speed of σ-convergence is 1.3% and the average speed of β-convergence is 2.1%, which give robust evidences to support the increasing spatial equalization of China’s higher education. Meanwhile, local spatial dynamics analysis shows that the catch-up strategy and cluster growth strategies of China are effective to achieve the balanced expansion goal.

This paper analyzes the effect of health investment, and hence of health capital, on physical capital accumulation and long-run economic growth in an extended Ramsey model with an Arrow–Romer production function and a Grossman (1972) utility function. The paper concludes that economic growth is related to both the health growth rate and the health level. While growth in health capital always facilitates economic growth, the gross effect of health level on the rate of economic growth depends on how it affects physical capital accumulation. If the negative effect of health on economic growth through its influence on physical capital accumulation is not taken into consideration, then health level has a positive effect on the rate of economic growth by improving the efficiency of labor production. However, since health investment may crowd out physical capital investment and thus influence physical capital accumulation, excessive investment in health may have a negative effect on economic growth. Empirical tests of these theoretical hypotheses using panel data from individual provinces of China produce results that are consistent with our theoretical conclusions.

As a representative folding system that features a conjugated backbone, a series of monodispersed (o-phenyleneethynylene)-alt-(p-phenyleneethynylene) (PE) oligomers of varied chain length and different side chains were studied. Molecules with the same backbone but different side-chain structures were shown to exhibit similar helical conformations in respectively suitable solvents. Specifically, oligomers with dodecyloxy side chains folded into the helical structure in apolar aliphatic solvents, whereas an analogous oligomer with tri(ethylene glycol) (Tg) side chains adopted the same conformation in polar solvents. The fact that the oligomers with the same backbone manifested a similar folded conformation independent of side chains and the nature of the solvent confirmed the concept that the driving force for folding was the intramolecular aromatic stacking and solvophobic interactions. Although all were capable of inducing folding, different solvents were shown to bestow slightly varied folding stability. The chain-length dependence study revealed a nonlinear correlation between the folding stability with backbone chain length. A critical size of approximately 10 PE units was identified for the system, beyond which folding occurred. This observation corroborated the helical nature of the folded structure. Remarkably, based on the absorption and emission spectra, the effective conjugation length of the system extended more effectively under the folded state than under random conformations. Moreover, as evidenced by the optical spectra and dynamic light-scattering studies, intermolecular association took place among the helical oligomers with Tg side chains in aqueous solution. The demonstrated ability of such a conjugated foldamer in self-assembling into hierarchical supramolecular structures promises application potential for the system.