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 managing computing resources. In one example, information about resource utilization on a computing node is received from the computing node. Available resource on the computing node is determined based on the information. A model generated in accordance with reinforcement learning based on simulated training data is obtained. An adjusted available resource is generated based on the available resource and the model with respect to the computing node. The adjusted available resource is sent to a scheduler for scheduling one or more jobs to be executed on the computing node based on the adjusted available resource.

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 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: 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 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 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 network processing device calculates variable link metrics and then prioritizes selection of network links for sending packets according to the calculated variable link metrics. The variable link metrics can include a link capacity index that represents a combination of platform and interface capabilities for nodes on opposite ends of the network links. The link metrics can also include an expected retransmission value that indicates the percentage of packets that may have to be transmitted over different links.

Abstract: In one embodiment, a dynamic rate control scheme controls transmission rates and adaptively filters out video packets when a packet queue is full. This allows video streams to be more efficiently transmitted through low bandwidth and dynamically changing links.

Abstract: A system and method are provided for calibrating for an I/Q mismatch of a direct conversion receiver based on a random signal having a two-dimensional I versus Q trajectory, such as radio frequency (RF) noise. In general, the random signal is received and downconverted to a quadrature baseband signal having an in-phase component and a quadrature component. The variance of the in-phase component, the variance of the quadrature component, and the covariance of the in-phase component with the quadrature component are computed based on samples of the quadrature baseband signal. A correction matrix used to compensate for the I/Q mismatch of the receiver and/or I/Q mismatch including a gain mismatch and a phase mismatch of the receiver is then computed based on the variance of the in-phase component, the variance of the quadrature component, and the covariance of the in-phase component with the quadrature component.

Abstract: In one embodiment, a dynamic rate control scheme controls transmission rates and adaptively filters out video packets when a packet queue is full. This allows video streams to be more efficiently transmitted through low bandwidth and dynamically changing links.

Abstract: A network processing device calculates variable link metrics and then prioritizes selection of network links for sending packets according to the calculated variable link metrics. The variable link metrics can include a link capacity index that represents a combination of platform and interface capabilities for nodes on opposite ends of the network links. The link metrics can also include an expected retransmission value that indicates the percentage of packets that may have to be transmitted over different links.