Beijing Baidu Netcom Science and Technology Co., Ltd.

China Building Materials Academy Co., Ltd.

Wenjing Qin

Jin Zhang

Hui Liu

Jing Yan Ma

Chang Liu

Hua Cai

Ang Li

Jinsheng Jia

Guangyao Han

Zeqing Jiang

Yonggang Huang

Guobin Xie

Fu Qu

Xingbo Chen

Hongjiang Du

Jiao Lian

Tiezhu Bo

Shiyong Xie

Yanjiang Liu

Shuaiqi Li

Liqiang Xue

Xiaolan Luo

A capillary array includes a capillary region, including capillaries of a first glass, which are disposed in an axis-parallel manner. A low refractive index layer is disposed on an inner wall of each of the capillaries, the refractive index of each low refractive index layer being less than a refractive index of a liquid scintillator. A second glass material is disposed between adjacent capillaries. A softening point of the first glass is T, a softening point of second glass is T, and a value of Tminus Tis in a range from 30° C. to 50° C. A thermal expansion coefficient of the first glass is α. An edge covering region is disposed on an outer side of the capillary region and makes contact with an outer side face of the capillary region, wherein a material of the edge covering region is a third glass.

Capillary array and preparation method and application therefor

Embodiments of the present disclosure relate to a method and apparatus for generating information. The method may include: receiving a modeling request; determining a target number of initial machine learning pipelines according to a type of training data and a model type; and executing following model generation steps using the target number of initial machine learning pipelines: generating a target number of new machine learning pipelines based on the target number of initial machine learning pipelines; performing model training based on the training data, the target number of initial machine learning pipelines, and the target number of new machine learning pipelines, to generate trained models; evaluating the obtained trained models respectively according to the evaluation indicator; determining whether a preset training termination condition is reached; and determining, in response to determining the preset training termination condition being reached, a target trained model from the obtained trained models according to evaluation results.

Method and apparatus for generating information

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Beijing, China

Wenjing Qin