Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.     

对《网络创业实训》课程的探讨

针对各校对大学生创业实训进行讲授内容的不同,我们在结合本专业的基础上,开展了网络创业实训课程,在创业方向方面不再仅限于网店,而是可以进行多种模式的选择,同时在提高实践性方向,最终的成果也以创业企划书来全面衡量。     

Enhanced Photoelectrochemical Performance of Hematite Photoanode by Decorating NiCoP Nanoparticles Through a Facile Spin Coating Method

Catalysis Letters - Hematite (α-Fe2O3) is a potential photoanode material for photoelectrochemical (PEC) water splitting, but its short hole diffusion length and low water oxidation kinetics...     

One-step molten salt carbonization of tobacco stem for capacitive carbon

Journal of Porous Materials - High-performance carbon materials for supercapacitors were prepared by a one-step molten salt carbonization of tobacco stem in molten carbonate. Physicochemical...     

Adsorption characteristics of hexadecyl ammonium with different numbers of carbon chains in montmorillonite and the structure of the prepared composites

Journal of Porous Materials - The dynamics and thermodynamics of adsorption of hexadecyl ammonium with different numbers of carbon chains in montmorillonite (Mt) with different layer charge density...     

Modulation on the electrical and optical properties of Na and Rh co-doped Ruddlesden-Popper layered Ca3Ti2O7

Layered perovskite Ca_2.91Na_0.09Ti_2-xRh_xO₇ (x?=?0.00, 0.02, 0.04, 0.06) were synthesized by a conventional solid-state reaction. Room temperature ferroelectricity has been confirmed. The remanent polarization increases with an increase of Rh content, which is due to a larger oxygen octahedral distortion by Rh doping. The coercive field increases with Rh doping as the pinning effect of oxygen vacancies reduce the mobility of domain wall. Remanent polarization and coercive field are caused by different mechanisms, so it is possible to modulate them independently to meet the requirement of application in ferroelectric field. The concentration of oxygen vacancy increased with Rh doping, leading to the significant increase of leakage current density. The bandgap of samples doped with Rh drastically decrease and the visible light response of the sample was improved by Rh doping due to the formation of impurity energy levels within the band gap.