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In this study, novel thermally stable zirconia-containing polyimides (PI/ZrO2) with excellent optical properties have been prepared successfully. The obtained flexible PI/ZrO2 hybrid films revealed excellent optical transparency, a tunable refractive index and an Abbe number up to 1.804 and 32.18, respectively, which are crucial for optical devices. In addition, the PI/ZrO2 hybrid films also exhibit a higher Abbe number and transparency in the visible light region due to a larger energy band gap of ZrO2 than the corresponding PI/TiO2 system.

In classification, if a small number of instances is added or removed, incremental and decremental techniques can be applied to quickly update the model. However, the design of incremental and decremental algorithms involves many considerations. In this paper, we focus on linear classifiers including logistic regression and linear SVM because of their simplicity over kernel or other methods. By applying a warm start strategy, we investigate issues such as using primal or dual formulation, choosing optimization methods, and creating practical implementations. Through theoretical analysis and practical experiments, we conclude that a warm start setting on a high-order optimization method for primal formulations is more suitable than others for incremental and decremental learning of linear classification.

Abstract-Logistic regression and linear SVM are useful methods for large-scale classification. However, their distributed implementations have not been well studied. Recently, because of the inefficiency of the MapReduce framework on iterative algorithms, Spark, an in-memory cluster-computing platform, has been proposed. It has emerged as a popular framework for largescale data processing and analytics. In this work, we consider a distributed Newton method for solving logistic regression as well linear SVM and implement it on Spark. We carefully examine many implementation issues significantly affecting the running time and propose our solutions. After conducting thorough empirical investigations, we release an efficient and easy-to-use tool for the Spark community.