Baidu Online Network Technology (beijing) Co., Ltd.

Shanghai Xiaodu Technology Co. Ltd.

Chinese Academy of Sciences

Xiaomi Inc.

Beijing University of Posts and Telecommunications

Beijing Ten Dimensions Technology Co., Ltd.

Li Wan

Mengmeng Zhang

Ran Xu

Gang Zhang

Qiang Ju

Zhongji Fan

Lei Zhang

Jing Wang

Wei Li

Bo Zhang

Jia Liu

Chun Long Wang

Lei Shi

Wei Shen

Shuai Wang

Wei Dai

Yuqing Li

Chao Guo

Zhen Shen

Yi Xie

Li Cheng

Feiyue Wang

Jian Xie

Tong Xu

Xiangtao Jiang

Wensong He

Deqing Liang

Gang Xiong

Xi Zhang

Wenming Wang

Hang Gao

Chao Yin

Jianxin Liao

Yafei Miao

Xinya Zhang

Liuhui Zhang

Hualong Zu

Xiaonan He

Jingyu Ye

Xiaochen Jia

Xiuqin Shang

Jingya Wang

Lingjin Xu

Jiaguang Lu

Meihua Zhao

Xisong Dong

Zhengpeng Wu

Sunhonglu Dong

Xiaomin Zhu

Lejian Zhang

Qiwei Shen

Limin Fan

A hydrate kinetic inhibitor, which is prepared by a polymerization of mercaptoethanol and N-vinylcaprolactam, is hydroxyl terminated poly(N-vinylcaprolactam) having a structure of formula (I) below, wherein n=10 to 1000. The inhibitor is a novel hydrate kinetic inhibitor, which has low effective concentration and high cloud point, and is effective when the degree of supercooling is relatively high.

Hydrate kinetics inhibitor

A method, an apparatus, an electronic device and a storage medium for training a user click model, which relate to the artificial intelligence field, are disclosed. is the method may include: collecting a plurality of pieces of behavior data from a log database of users on a network, each piece of behavior data including a user's feedback information regarding resources in the network within a preset time period; generating a plurality of resource access features respectively corresponding to the plurality of pieces of behavior data, based on a pre-created header resource library and the plurality of pieces of behavior data; and training the user click model with the plurality of resource access features. The technical solution provides a lossless modeling manner which, compared to the existing modeling manners, may effectively optimize the precision and improve the accuracy of the user click model.

Method, apparatus, electronic device and storage medium for training user click model

The present disclosure provides an intelligent interaction processing method and apparatus, a device and a computer storage medium. The method comprises: performing intention recognition for a preceding feedback item already returned to the user; continuing to return a subsequent feedback item to the user based on the intention of the preceding feedback item. According to the present disclosure, it is possible to guess the user's subsequent intention based on the preceding feedback item, and continue to return the desired subsequent feedback item to the user without the user's operations, so that the present disclosure is more intelligentized and richer and simplifies the user's operations.

Intelligent interaction processing method and apparatus, device and computer storage medium

A neural network-based error compensation method for 3D printing includes: compensating an input model by a deformation network/inverse deformation network constructed and trained according to a 3D printing deformation function/inverse deformation function, and performing the 3D printing based on the compensated model. Training samples of the deformation network/inverse deformation network include to-be-printed model samples and printed model samples. The deformation network constructed according to the 3D printing deformation function is marked as a first network. During training of the first network, the to-be-printed model samples are used as real input models, and the printed model samples are used as real output models. The inverse deformation network constructed according to the 3D printing inverse deformation function is marked as a second network. During training of the second network, the printed model samples are used as real input models, and the to-be-printed model samples are used as real output models.

Neural network-based error compensation method, system and device for 3D printing

Embodiments of the present disclosure disclose an interaction method and apparatus. A specific embodiment of the method includes: generating, in response to determining that a request input by a user satisfies a guiding condition, guiding information, and feeding back the guiding information to the user, the guiding condition including one of the following: associating with a plurality of query intents, or associating with no query intent; and generating, based on the request and a feedback input by the user corresponding to the guiding information, an intent-clear request, and feeding back push information bound with the intent-clear request to the user. Realizing that in the process of interacting with the user, for conditions such as the request input by the user is associated with a plurality of query intents or incompleteness, an intent-clear request associated with an explicit query intent is determined through the interaction with the user.

Interaction method and apparatus

The present disclosure provides a resource pushing method, a device and a storage medium for a smart device, the method includes: acquiring a first pushing resource according to a preset rule, where the first pushing resource is used by the smart device to interact with a user, pushing the first pushing resource to the user. In the solution, relevant resources are actively acquired according to behavior information of the user, triggering and hotspots, and are pushed to the user, thereby saving the cost in resource querying by the user, and increasing the exposure of platform resources.

Resource pushing method, device, and storage medium for smart device

A computer-implemented method for processing user information provided in online ordering, includes receiving an instruction to perform an anonymous process on user information selected from the provided user information; performing, in response to the instruction, an anonymous process on the selected user information, to obtain anonymous information corresponding to the selected user information; and generating an order containing the anonymous information.

Method and apparatus for processing user information

A method for selecting and configuring network supernodes includes (i) in a first set period T, each node, other than a control node in a network, regularly sending information on interactions between the node and other nodes to the control node; and (ii) in a second set period T, the control node dividing the nodes into a plurality of node clusters according to the received information on interactions among the nodes and selecting supernodes from each node cluster, where each node belonging uniquely to a node cluster.

Method for selecting and configuring network supernodes

Growing community of inventors

Beijing, China

Li Wan