In this paper, we have developed a new scheme for achieving multilevel annotations of large-scale images automatically. To achieve more sufficient representation of various visual properties of the images, both the global visual features and the local visual features are extracted for image content representation. To tackle the problem of huge intraconcept visual diversity, multiple types of kernels are integrated to characterize the diverse visual similarity relationships between the images more precisely, and a multiple kernel learning algorithm is developed for SVM image classifier training. To address the problem of huge interconcept visual similarity, a novel multitask learning algorithm is developed to learn the correlated classifiers for the sibling image concepts under the same parent concept and enhance their discrimination and adaptation power significantly. To tackle the problem of huge intraconcept visual diversity for the image concepts at the higher levels of the concept ontology, a novel hierarchical boosting algorithm is developed to learn their ensemble classifiers hierarchically. In order to assist users on selecting more effective hypotheses for image classifier training, we have developed a novel hyperbolic framework for large-scale image visualization and interactive hypotheses assessment. Our experiments on large-scale image collections have also obtained very positive results.

An analytical model for plasmon modes in graphene-coated dielectric nanowire is presented. Plasmon modes could be classified by the azimuthal field distribution characterized by a phase factor exp(imφ) in the electromagnetic field expression and eigen equation of dispersion relation for plasmon modes is derived. The characteristic of plasmon modes could be tuned by changing nanowire radius, dielectric permittivity of nanowire and chemical potential of graphene. The proposed model provides a fast insight into the mode behavior of graphene-coated nanowire, which would be useful for applications based on graphene plasmonics in cylindrical waveguide.

An efficient Fe(2)O(3)-pillared rectorite (Fe-R) clay was successfully developed as a heterogeneous catalyst for photo-Fenton degradation of organic contaminants. X-ray diffraction analysis and high-resolution transmission electron microscope analysis clearly showed the existence of the Fe(2)O(3) nanoparticles in the Fe-R catalyst. The catalytic activity of the Fe-R catalyst was evaluated by the discoloration and chemical oxygen demand (COD) removal of an azo-dye rhodamine B (RhB, 100 mg/L) and a typical persistent organic pollutant 4-nitrophenol (4-NP, 50 mg/L) in the presence of hydrogen peroxide (H(2)O(2)) under visible light irradiation (lambda > 420 nm). It was found that the discoloration rate of the two contaminants was over 99.3%, and the COD removal rate of the two contaminants was over 87.0%. The Fe-R catalyst showed strong adsorbability for the RhB in the aqueous solution. Moreover, the Fe-R catalyst still showed good stability for the degradation of RhB after five recycles. Zeta potential and Fourier transform infrared spectroscopy were used to examine the photoreaction processes. Finally, a possible photocatalytic mechanism was proposed.