Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Beijing Daxiang Biotech Co., Ltd

General Electric Company

Mobvoi Information Technology Company Limited

National Institute of Biological Sciences, Beijing

Yu Zhou

Jianjun Li

Tian Wu

Xiang Yu

Shupeng Li

Niu Huang

Meng Yao

Yongkang Xie

En Shi

Kuan Shi

Zhenjiang Wang

Xingyu Lin

Zhou Xin

Tieshan Chen

Zihao Pan

Mingren Hu

Qimou Xiong

Hao Cao

Xiaoli Shi

Shihai Zhang

Yingming Zhang

Ge Wu

Rongrong Xiao

Ruyue Ma

Yashuai Li

Zhiquan Wu

David Smith

Dong Wang

A 3D multi-organ co-culture chip is provided by present disclosure and comprises a chip body, wherein one or more groups of culture modules are arranged on the chip body; an each group of the culture modules comprises a fluid storage hole of which an end is open and is positioned at an upper surface of the chip body; a first culture micropore which is positioned below a corresponding fluid storage hole and communicates with the corresponding fluid storage hole; a second culture micropore which is positioned below a corresponding first culture micropore and communicates with the corresponding first culture micropore; and a plurality of second fluid operation holes, wherein an end of an second fluid operation hole is open and is positioned on the upper surface of the chip body and an other end of the second fluid operation hole communicates with a corresponding second culture micropore through a channel.

3D multi-organ co-culture chip

An apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium are provided. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on a file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface.

Apparatus and method for executing customized artificial intelligence production line

Provided are a method and apparatus for determining image depth information, an electronic device, and a medium. The method includes: acquiring first depth information of pixels in a target image output by a first prediction layer; generating the point cloud model of the target image according to the first depth information, and determining initial depth information of the pixels in the target image in a second prediction layer according to the point cloud model; and performing propagation optimization according to the initial depth information, and determining second depth information of the pixels in the target image output by the second prediction layer, where the first prediction layer is configured before the second prediction layer.

Method and apparatus for determining image depth information, electronic device, and media

This application discloses a data processing method and apparatus, an electronic device, and a storage medium. When execution is performed at an operation layer of a neural network model, based on a pre-stored buffer allocation relationship, a first address range for cyclic addressing is set for a first buffer corresponding to input data and a second address range for cyclic addressing is set for a second buffer corresponding to an output result. Subsequently, cyclic addressing can be performed in the first buffer based on the first address range for cyclic addressing, to read the input data for the operation layer; and cyclic addressing can be performed in the second buffer based on the second address range for cyclic addressing, to write the output result of the operation layer into the second buffer. In this way, efficiency of buffer utilization can be effectively improved, and further operation efficiency for the model is improved.

Method and electronic device for data processing, and storage medium

Growing community of inventors

Beijing, China

Yu Zhou