Abstract: An oxygen production system (100) may include a main control module (120) and a molecular sieve module (140). The molecular sieve module (140) may include a molecular sieve and a molecular sieve information unit. The molecular sieve information unit may be configured to store information of the molecular sieve. The main control module (120) may be configured to read, write and/or update the information of the molecular sieve stored in the molecular sieve information unit. When reading, in response to at least part of the information of the molecular sieve exceeding a preset range, the main control module (120) may control the oxygen production system (100) to perform a corresponding operation. The oxygen production system (100) may occupy small space, have good performance and a high oxygen production efficiency, and enable a user to obtain a more user-friendly experience.

Abstract: A computer-implemented method to generate a second machine learning model based on a first machine learning model, wherein the second machine learning model is structured for more efficient computation, is provided. The method includes processing an input with a hidden layer of a student machine-learned model to obtain an intermediate output. The method includes providing an encoded message descriptive of the input and the intermediate output for processing with a teacher machine-learned model. The method includes, responsive to providing the encoded message, obtaining a second encoded message descriptive of a second intermediate output of one or more hidden layers of the teacher machine-learned model. The method includes performing a knowledge distillation training process to train the student machine-learned model based on a difference between the intermediate output and the second intermediate output.

Abstract: This disclosure provides embodiments for context based vehicular traffic prediction. A trained neural network modeling a relationship between historical traffic data and associated historical contextual data for a roadway link is obtained. Expected contextual data for a future time period for the roadway link is acquired. Predicted traffic data for the future time period for the roadway link is generated with the trained neural network based on the expected contextual data.

Abstract: Embodiments of the present disclosure relate to generation of sentence representation. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words, the sentence graph comprising nodes representing the words and edges connecting the nodes to indicate relationships between the words. Word representations for the plurality of words are determined based on the sentence graph by applying a graph convolution operation on respective sets of neighbor nodes for respective ones of the nodes, a set of neighbor nodes for a node having edges connected with the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: An oxygen production system (100) may include a main control module (120) and a molecular sieve module (140). The molecular sieve module (140) may include a molecular sieve and a molecular sieve information unit. The molecular sieve information unit may be configured to store information of the molecular sieve. The main control module (120) may be configured to read, write and/or update the information of the molecular sieve stored in the molecular sieve information unit. When reading, in response to at least part of the information of the molecular sieve exceeding a preset range, the main control module (120) may control the oxygen production system (100) to perform a corresponding operation. The oxygen production system (100) may occupy small space, have good performance and a high oxygen production efficiency, and enable a user to obtain a more user-friendly experience.

Abstract: Embodiments of the present disclosure relate to attention-based neural language processing. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words. The sentence graph comprises nodes representing words and edges connecting the nodes, at least one of the edges being constructed to indicate a syntactic relationship between words represented by nodes connected therebetween. Word representations for the words are determined based on the sentence graph by applying an attention mechanism on respective ones of the nodes and respective sets of neighbor nodes for the nodes. A set of neighbor nodes for a node has edges connected to the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: This disclosure provides embodiments for context based vehicular traffic prediction. A trained neural network modeling a relationship between historical traffic data and associated historical contextual data for a roadway link is obtained. Expected contextual data for a future time period for the roadway link is acquired. Predicted traffic data for the future time period for the roadway link is generated with the trained neural network based on the expected contextual data.

Abstract: Embodiments of the present disclosure relate to generation of sentence representation. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words, the sentence graph comprising nodes representing the words and edges connecting the nodes to indicate relationships between the words. Word representations for the plurality of words are determined based on the sentence graph by applying a graph convolution operation on respective sets of neighbor nodes for respective ones of the nodes, a set of neighbor nodes for a node having edges connected with the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.

Abstract: Embodiments of the present disclosure relate to attention-based neural language processing. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words. The sentence graph comprises nodes representing words and edges connecting the nodes, at least one of the edges being constructed to indicate a syntactic relationship between words represented by nodes connected therebetween. Word representations for the words are determined based on the sentence graph by applying an attention mechanism on respective ones of the nodes and respective sets of neighbor nodes for the nodes. A set of neighbor nodes for a node has edges connected to the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.