Abstract: A technique selects and serves local information items (e.g., news articles) to users. In a first stage, the technique uses a machine-trained localness-determining system to determine whether a candidate information item contains the kind of information that qualifies as locally-themed. In a second stage, a scope-determining system determines a particular geographic region associated with the information item. The technique then selectively serves the information item to a particular consumer upon determining that the particular consumer is located in the particular geographic region associated with the item. In some implementations, the scope-determining system describes the particular geographic region of the information item using a set of geohashes, and describes the location of the consumer using at least one geohash. The technique uses an ensemble approach to identify the particular geographic region of the item, and to generate training examples for use in training the localness-determining system.

Abstract: A technique selects and serves local information items (e.g., news articles) to users. In a first stage, the technique uses a machine-trained localness-determining system to determine whether a candidate information item contains the kind of information that qualifies as locally-themed. In a second stage, a scope-determining system determines a particular geographic region associated with the information item. The technique then selectively serves the information item to a particular consumer upon determining that the particular consumer is located in the particular geographic region associated with the item. In some implementations, the scope-determining system describes the particular geographic region of the information item using a set of geohashes, and describes the location of the consumer using at least one geohash. The technique uses an ensemble approach to identify the particular geographic region of the item, and to generate training examples for use in training the localness-determining system.

Abstract: There are provided systems and methods for clustering data vectors based on deep neural network embeddings. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users. In order to provide actionable insights into users, accounts, and/or activities associated with the service provider, the service provider may provide clustering of deep embeddings from an embedding layer of a deep neural network model. The clustering may be improved to handle and utilize temporal data, such as time sensitive and/or changing data, using a long short-term memory model with sequential data. The embedding layer may be trained and used for embedding generation using a distribution-wise objective function and a silhouette score to determine cluster membership, cluster loss, and the number of clusters. Once trained, data records may be clustered and relationships between different data records may be identified for taking next-best-actions.

Abstract: Users interact with a computer system, which collects data about individual interactions the users have had with the computer system. The users are sorted into one of a first group or a second group. The computer system generates respective user sequence models for the users using information representing the individual interactions. The computer system analyzes the respective user sequence models using a recurrent neural network with an attention mechanism, which produces respective vectors corresponding to the user sequence models. Individual values in the vectors represent respective individual interactions by a given user and correspond to an amount of correlation between the respective individual interactions and the sorting of the given user into the first group or the second group. The computer system identifies a particular type of interaction that is correlated to users being sorted into the first group by analyzing the respective vectors.

Abstract: Disclosed is an electro-magneto-thermo-mechanical dynamic and synchronous loading device based on a wedge-shaped rotating body. The device comprises a carrier, a wedge-shaped rotating body and a pulse power supply, wherein the wedge-shaped rotating body is positioned above the carrier, the pulse power supply is connected to the carrier and the wedge-shaped rotating body through conductors, a test object is fixed on the carrier, the top of the wedge-shaped rotating body is connected to the output end of a driving shaft through a transmission shaft, the driving shaft drives the wedge-shaped rotating body to rotate and can apply downward pressure, and the wedge-shaped rotating body can be pressed against the test object and rotate on the surface of the test object.

Abstract: Disclosed is a synchronous and dynamic loading method in electro-magneto-thermo-mechanical multi-field coupling conditions. The method comprises the following steps: applying maximum pulse current to a test object by a pulse power supply to realize loading in extreme electric field and magnetic field conditions; meanwhile, generating a large amount of friction heat by the high-speed rotation of a rotating body and the friction of the test object to realize loading in an extreme-temperature field combined with a large amount of Joule heat and arc heat; synchronously applying pressure to the rotating body by a pressure device to realize loading of extreme force combined with the gravity of the rotating body and the friction force between the rotating body and the test object.

Abstract: A method for calculating a maximum output current of multiple thyristor converters connected in parallel, step 1: setting an operating time t; step 2: assuming a trigger angle; step 3: calculating a maximum output current of a single converter according to an output current model for the single converter; step 4: equally dividing a total output DC current into a plurality of parts according to a working duration of six converter bridge arms, thereby obtaining a pulse operating current of a single bridge arm; step 5: checking whether a present junction temperature of a thyristor is below a limiting temperature based on a thermal resistance model for the thyristor, if no, correcting the trigger angle, and repeating step 2 to step 5 until the condition is met; step 6: giving a present trigger angle; and step 7: giving a maximum output current of multiple converters connected in parallel.

Abstract: Users interact with a computer system, which collects data about individual interactions the users have had with the computer system. The users are sorted into one of a first group or a second group. The computer system generates respective user sequence models for the users using information representing the individual interactions. The computer system analyzes the respective user sequence models using a recurrent neural network with an attention mechanism, which produces respective vectors corresponding to the user sequence models. Individual values in the vectors represent respective individual interactions by a given user and correspond to an amount of correlation between the respective individual interactions and the sorting of the given user into the first group or the second group. The computer system identifies a particular type of interaction that is correlated to users being sorted into the first group by analyzing the respective vectors.

Abstract: The present application belongs to the technical field of drug application, relates to a treatment method using a complex of an angiotensin II receptor antagonist metabolite and an NEP inhibitor, and specifically relates to the dosage of the drug.

Abstract: A method for calculating a maximum output current of multiple thyristor converters connected in parallel, step 1: setting an operating time t; step 2: assuming a trigger angle; step 3: calculating a maximum output current of a single converter according to an output current model for the single converter; step 4: equally dividing a total output DC current into a plurality of parts according to a working duration of six converter bridge arms, thereby obtaining a pulse operating current of a single bridge arm; step 5: checking whether a present junction temperature of a thyristor is below a limiting temperature based on a thermal resistance model for the thyristor, if no, correcting the trigger angle, and repeating step 2 to step 5 until the condition is met; step 6: giving a present trigger angle; and step 7: giving a maximum output current of multiple converters connected in parallel.