Named Entity Recognition (NER) systems are often realized by supervised methods such as CRF and neural network methods, which require large annotated data. In some domains that small annotated training data is available, multi-domain or multi-task learning methods are often used. In this paper, we explore the methods that use news domain and Chinese Word Segmentation (CWS) task to improve the performance of Chinese named entity recognition in weibo domain. We first propose two baseline models combining multi-domain and multi-task information. The two baseline models share information between different domains and tasks through sharing parameters simply. Then, we propose a Double ADVersarial model (DoubADV model). The model uses two adversarial networks considering the shared and private features in different domains and tasks. Experimental results show that our DoubADV model outperforms other baseline models and achieves state-of-the-art performance compared with previous works in multi-domain and multi-task situation.

Due to the complicated composition of cloud and its disordered transformation, the rendering of cloud does not perfectly meet actual prospect by current methods. Based on physical characteristics of cloud, a physical cellular automata model of Dynamic cloud is designed according to intrinsic factor of cloud, which describes the rules of hydro-movement, deposition and accumulation and diffusion. Then a parallel computing architecture is designed to compute the large-scale data set required by the rendering of dynamical cloud, and a GPU-based ray-casting algorithm is implemented to render the cloud volume data. The experiment shows that cloud rendering method based on physical cellular automata model is very efficient and able to adequately exhibit the detail of cloud.

A novel rendering algorithm with a best-matching patch is proposed to address the noise artifacts associated with Monte Carlo renderings. First, in the sampling stage, the representative patch is selected through a modified patch shift procedure, which gathers homogeneous pixels together to stay clear of the edges. Second, each pixel is filtered over a discrete set of filters, where the range kernel is computed using the selected patches. The difference between the selected patch and the filtered value is used as the pixel error, and the single filter that returns the smallest estimated error is chosen. In the reconstruction stage, pixel colors are combined with features of depth, normal and texture to form a cross bilateral filter, which highly preserves scene details while effectively removing noise. Finally, a heuristic metric is calculated to allocate additional samples in difficult regions. Compared with state-of-the art methods, the proposed algorithm performs better both in visual image quality and numerical error.