Abstract: A circuit interrupter including a current sensor having a normal sensor output and an over current detection output, a solid state switch module structured to have a closed state to allow current to flow through the circuit interrupter and an open state to interrupt current flowing through the circuit interrupter, a gate driver structured to control the solid state switch module including a desaturation function output, wherein the gate driver is structured to cause the solid state switch module to interrupt current flowing through the circuit interrupter when the DESAT function output changes to the on state, and an electronic trip circuit structured to output a trip signal to the gate driver when the normal sensor output reaches a first threshold level or the overcurrent detection output changes to the on state.

Abstract: Apparatuses, systems, and techniques to collaboratively train one or more machine learning models. Parameter reviewers may be configured to compare sets of machine learning model parameter information in order to generate one or more machine learning models, such as neural networks.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: Methods and systems of securely exchanging customer data are provided. The exchange may be done in privacy-preserving and/or privacy-protective fashion. Customer data from multiple merchants, including transaction data for a first product between a first customer and a first merchant, is maintained in a customer data pool by an e-commerce platform. When a second merchant associated with a second product requests new customer data, a product (i.e., the first product) relevant to the second product is identified. A collection of customers who indicated interest in the first product (including the first customer) are identified from the customer data pool, and a target audience is generated proportional to customer data exchanged by the second merchant with the customer data pool. Communications from the second merchant are then provided to the target audience without disclosing their customer data to the second merchant.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

Abstract: A unified end-user notification platform delivers event alerts to different types of clients including mobile devices and HTTP clients. Users can subscribe to a plurality of notification channels and select from the associated various delivery options via a single user interface. The events are received by the unified notification platform which matches the received events with the user subscription data to identify subscribers and their respective delivery options. Corresponding event alerts are generated and delivered based on the user or subscriber specified options. Multiple event alerts corresponding to public and private data notification channels are provided to a user device via a single connection.

Abstract: The present teaching relates to distributed deep machine learning on a cluster. In one example, a request is received for estimating one or more parameters associated with a machine learning model on a cluster including a plurality of nodes. A set of data is obtained to be used for estimating the one or more parameters. The set of data is divided into a plurality of sub-sets of data, each of which corresponds to one of the plurality of nodes. Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.

Abstract: The present teaching relates to distributed deep machine learning on a cluster. In one example, a request is received for estimating one or more parameters associated with a machine learning model on a cluster including a plurality of nodes. A set of data is obtained to be used for estimating the one or more parameters. The set of data is divided into a plurality of sub-sets of data, each of which corresponds to one of the plurality of nodes. Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.

Abstract: A unified end-user notification platform delivers event alerts to different types of clients including mobile devices and HTTP clients. Users can subscribe to a plurality of notification channels and select from the associated various delivery options via a single user interface. The events are received by the unified notification platform which matches the received events with the user subscription data to identify subscribers and their respective delivery options. Corresponding event alerts are generated and delivered based on the user or subscriber specified options. Multiple event alerts corresponding to public and private data notification channels are provided to a user device via a single connection.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: In various examples, a virtualized computing platform for advanced computing operations—including image reconstruction, segmentation, processing, analysis, visualization, and deep learning—may be provided. The platform may allow for inference pipeline customization by selecting, organizing, and adapting constructs of task containers for local, on-premises implementation. Within the task containers, machine learning models generated off-premises may be leveraged and updated for location specific implementation to perform image processing operations. As a result, and using the virtualized computing platform, facilities such as hospitals and clinics may more seamlessly train, deploy, and integrate machine learning models within a production environment for providing informative and actionable medical information to practitioners.

Abstract: The present teaching relates to identifying content that matches a query. Train data include queries, advertisement, and hyperlinks associated with query sessions. A plurality of subwords for each of the queries in the training data are identified. A query/ads model is then trained by optimizing vectors associated the plurality of subwords for each of the queries, advertisements, and hyperlinks in the training data with respect to an objective function. At least one vector associated with each of the queries is derived based on the plurality of subword vectors in the query/ads model that represent the plurality of subwords of the query.

Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.

Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.

Abstract: A unified end-user notification platform delivers event alerts to different types of clients including mobile devices and HTTP clients. Users can subscribe to a plurality of notification channels and select from the associated various delivery options via a single user interface. The events are received by the unified notification platform which matches the received events with the user subscription data to identify subscribers and their respective delivery options. Corresponding event alerts are generated and delivered based on the user or subscriber specified options. Multiple event alerts corresponding to public and private data notification channels are provided to a user device via a single connection.

Abstract: A unified end-user notification platform delivers event alerts to different types of clients including mobile devices and HTTP clients. Users can subscribe to a plurality of notification channels and select from the associated various delivery options via a single user interface. The events are received by the unified notification platform which matches the received events with the user subscription data to identify subscribers and their respective delivery options. Corresponding event alerts are generated and delivered based on the user or subscriber specified options. Multiple event alerts corresponding to public and private data notification channels are provided to a user device via a single connection.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.