Google Inc.

Petrochina Company Limited

Yu Liang

Kai Chen

Ke Yang

Xiaotao Duan

Qingzhou Wang

Lei Zhang

Gang Wang

Dan Wang

Lei Fang

Jesse Shieh

Wencheng Zhang

Xianjun Wu

Jintao Guo

Quanguo Zhang

Guojia Zhang

Chunming Dong

Tiegang Xu

Wenqian Fu

Liyan Guo

Runsheng Shen

Liru Cong

Guoren Cai

Baoli Ma

Weichi Xu

Guangming Wen

Jinhe Song

Tiandi Tang

Mingwei Tan

The invention provides a method for synthesizing a mesoporous zeolite ETS-10 containing a metal without a templating agent. The method according to the invention comprises the steps of: mixing a silicon source with a NaOH solution to obtain a mixed solution so that the content of NaO in the mixed solution is 10.0% to 20.0% by weight; adding a KOH or KF solution so that the content of KO is 10.0% to 25.0% by weight and stirring it well; adding a titanium source solution and stirring it well; adding a precursor compound containing metal Ni and/or Co and stirring it well; and subjecting it to a crystallization reaction to obtain the mesoporous zeolite ETS-10. The mesoporous zeolite ETS-10 obtained by the invention has a specific surface area of 320 to 420 m/g, a mesoporous volume of 0.11 to 0.21 cm/g, and thus can be used as a catalyst and a support thereof in synthesis industry for macromolecular fine chemicals.

Methods for synthesizing mesoporous zeolite ETS-10 containing metal without a templating agent

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.

Classifying resources using a deep network

Methods, systems, and apparatus, including computer program products, for applying custom web page themes. In one aspect, a method includes receiving a request for search results, where the request includes a search query submitted in an input field by a user; obtaining a search results page in response to the search query; identifying one or more features of the search query; selecting a first visual theme from among a plurality of visual themes, where each visual theme has respective trigger data, based on the one or more features of the search query matching the trigger data of the first visual theme, the first visual theme having theme data, the theme data specifying visual features; and applying the visual features to the search results page for display in a user interface.

Custom web page themes

Embodiments generally relate to generating social networks from device specific communications. In one embodiment, a method includes determining social connection data included in communications, the communications being associated together via a device identifier and generating a communications-based social network for an end user from the determined social connection data, the end user being associated with the device identifier.

Generating social networks from social connection data

Methods and systems for predicting a text are described. In an example, a computing device may be configured to receive one or more typed characters that compose a portion of a text; and receive, a voice input corresponding to a spoken utterance of at least a portion of the text. The computing device may be configured to determine, based on the one or more typed characters and the voice input, one or more candidate texts predicting the text. Further, the computing device may be configured to provide the one or more candidate texts.

Methods and systems for predicting a text

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Santa Clara, CA, United States of America

Yu Liang