Motorola Mobility LLC

Nanjing University

Nanjing Noratech Pharmaceuticals Co., Ltd

Xin Zhao

Amit Kumar Agrawal

Yonghua Hu

Fei Liu

Cheng-Gang Lin

Li Gui

Nan Xia

Yun-Zhe Sun

Yunyun Xu

Zhenjie Chen

Wei-Ming Jiang

Manchun Li

Chen Zhou

Boqing Wen

Zian Wang

Haoyang Du

Zhixin Yu

An identification method of urban functional areas based on mixing degree of functions and integrated learning includes the following steps: 1) performing data acquisition and preprocessing; 2) constructing 10 indicator features of an urban functional area identification system; 3) structuring the indicator features: acquiring, by a spatial statistical tool, the 10 indicator features corresponding to each parcel; 4) constructing an independent variable dataset; 5) labeling response variables; 6) dividing a training dataset into a plurality of training subsets according to the mixing degree of functions; 7) training a Stacking-based integrated learning model; and 8) joining an attribute in one table to another table, so as to complete the identification of the urban functional areas on each parcel. The identification method divides the training dataset by grading the mixing degree of functions, and makes predictions based on the prediction dataset with corresponding mixing degree of functions.

Identification method of urban functional areas based on mixing degree of functions and integrated learning

A method for detecting and responding to snap motions. The method includes detecting a triggering event that switches a mobile device into a snap motion gesture detection state to commence a finger-snap gesture motion detection process. The method includes activating, based on detecting the triggering event, an image capturing device to capture a series of image frames of a scene and capturing, using the image capturing device, the series of image frames of the scene that includes a hand. The method includes determining, by analyzing at least two frames among the series of image frames, that a finger-snap gesture is detected in the series of image frames. The method includes executing an action corresponding to a determination that a finger-snap gesture is detected in the series of image frames.

Snap motion gesture detection and response

A method including capturing a series of image frames of a hand. The method includes generating a map of hand landmarks for each in the series of image frames. The method includes determining whether the maps of hand landmarks include at least: (i) a first map that matches a first reference map of hand landmarks representing a hand in substantially a start position of a snap motion and (ii) a subsequent, second map that matches a second reference map of hand landmarks representing a hand in a progressed (e.g., intermediate or end) position of a snap motion. The method includes determining that a finger-snap gesture is detected in response to determining that the first map and second map were generated from image frames captured within a snap time limit; and executing an action corresponding to a determination that a finger-snap gesture is detected in the series of image frames.

The present invention relates to a crystalline form N of aripiprazole, pharmaceutical compositions thereof and the use of crystalline form N in the preparation of a medicament for the treatment of central nervous system diseases, especially schizophrenia.

Crystalline form of aripiprazole

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Nanjing, China

Xin Zhao