Visa International Service Association

Applied Materials, Inc.

Amazon Technologies, Inc.

E Ink California, LLC

Fei Wang

Hao Yang

Pravin K Narwankar

Jonathan Stuart Frankel

Miaojun Wang

Yu-San Lin

Abhijit Basu Mallick

Wei Zhang

Robert Jan Visser

Rui Cheng

Hongmei Zang

Xin Liu

Shivkumar Chiruvolu

Yajuan Chen

Jack Hou

Yi-Shung Chaug

Mangesh Bendre

Chiranjeet Chetia

Colin Costano Neikirk

Kangtai Ren

Yan Zheng

Michael Yeh

Yuhang Wu

Mahashweta Das

Robert Brian Christensen

Shubham Agrawal

Chia-Pu Chang

Huiyuan Chen

Balaji Ganapathy

Lan Wang

Linyun He

Jing-Den Chen

Ishita Bindlish

Venkataramana Kini

Ravi Kiran Divvela

Zhen Wen

Devendra Pratap Yadav

Embodiments of a content recommendation or selection system are described. The system uses a pipeline of machine learning models to select content for a user. In embodiments, a first user model generates a first score of content categories for the user based on short-term user data. A second user model generates a second score of the categories for the user based on long-term user data. The two scores are combined to select the categories to include on the content user interface. In embodiments, new categories are added to the recommendations based on an exploration-exploitation algorithm. In embodiments, content categories are organized on the user interface in a manner to promote neighborhood diversity. Advantageously, the machine learning pipeline enables independent configurability of various objectives of the content recommendation or selection system and reduces the amount of machine learning resources needed to implement the system.

Machine learning pipeline for content selection

Provided are systems, methods, and computer program products for generating node embeddings. The system includes at least one processor programmed or configured to generate a graph comprising a plurality of nodes, generate an embedding for each node of the plurality of nodes, each embedding comprising at least one polar angle and a vector length, store each embedding of a plurality of embeddings in memory, and in response to processing the graph with a machine-learning algorithm, convert at least one embedding of the plurality of embeddings to Cartesian coordinates.

Systems, methods, and computer program products for generating node embeddings

A pharmaceutical composition containing a metal oxide coated particle comprising 1) an amorphous solid dispersion (ASD) core containing an active pharmaceutical ingredient (API) and a polymer; and 2) a metal oxide coating, and the method of making said metal oxide coated particle by atomic layer deposition (ALD). The metal oxide coated particle is useful because it prevents the ASD from crystallization and helps maintain the ASD in an amorphous form.

Coated drug compositions and methods of preparing the same

Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

System, method, and computer program product for multi-domain ensemble learning based on multivariate time sequence data

A method of preparing a pharmaceutical composition having a drug-containing core enclosed by one or more silicon oxide materials is provided. The method entails alternating exposing the particles to gaseous or vaporous SiCland gaseous or vaporous HO at a reduced temperature and in the absence of a catalyst.

Low temperature process for preparing silicon oxide coated pharmaceuticals

A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

Structured graph convolutional networks with stochastic masks for network embeddings

Embodiments of the present disclosure relate to processes for filling trenches. The process includes depositing a first amorphous silicon layer on a surface of a layer and a second amorphous silicon layer in a portion of a trench formed in the layer, and portions of side walls of the trench are exposed. The first amorphous silicon layer is removed. The process further includes depositing a third amorphous silicon layer on the surface of the layer and a fourth amorphous silicon layer on the second amorphous silicon layer. The third amorphous silicon layer is removed. The deposition/removal cyclic processes may be repeated until the trench is filled with amorphous silicon layers. The amorphous silicon layers form a seamless amorphous silicon gap fill in the trench since the amorphous silicon layers are formed from bottom up.

Method for Si gap fill by PECVD

Described are a system, method, and computer program product for efficiently joining time-series data tables. The method includes loading a first table and a second table into a memory and generating a set of first key-value pairs based on a set of first time-series records and a set of second key-value pairs based on a set of second time-series records. The method also includes sorting the set of first key-value pairs and the set of second key-value pairs. The method further includes interleaving the set of first key-value pairs with the set of second key-value pairs and sequentially matching the sets of time-series records to form a joined table. The method further includes, in response to matching each respective second time-series record with the respective first time-series record, removing the respective second time-series record from the at least one memory.

System, method, and computer program product for efficiently joining time-series data tables

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Fremont, CA, United States of America

Fei Wang