Abstract: A decision support tool is provided for predicting uterine contractions. The predicted contractions are determined from measurements of uterine activity (UA). The contraction forecast may be made using a plurality of trained predictive models. The forecasts are formed with linear regressive models based, at least in part, on UA data from a reference population. The predicted contractions may be predictions of contraction duration, contraction peak, and time between contractions. In this way, these predicted contractions may be used for decision support for care plans during labor, such as increased monitoring and/or modifying the care plan.

Abstract: A computing device, including a processor configured to receive sensor data from a control device. The control device may include a control processor configured to execute control instructions to control an actuator of a target electromechanical system and may further include one or more sensors. The processor may identify a first subset of the sensor data and a second subset of the sensor data. The processor may generate first control instructions based on the first subset and transmit the first control instructions to the control processor of the control device. The processor may transmit the second subset to a remote computing device. In response to transmitting the second subset to the remote computing device, the processor may receive a remote processing result from the remote computing device. The processor may generate second control instructions from the remote processing result and transmit the second control instructions to the control processor.

Abstract: This disclosure provides session processing methods and apparatuses. In an implementation, a method comprises receiving, from an application function apparatus, a request that comprises a continuity requirement of an application indicating whether a service of the application needs to remain uninterrupted for a user equipment when the user equipment moves, determining, for the application, a session and service continuity mode that satisfies the continuity requirement of the application, and sending policy information that comprises the session and service continuity mode.

Abstract: An extraction unit 141 extracts predetermined pieces of information from each of a plurality of packets and sent from equipment 20. Then, an inference unit 142 infers a piece of equipment information that is information on the equipment 20, for each of the plurality of packets on the basis of the pieces of information extracted by the extraction unit 141. Then, the first selection unit 143 selects a predetermined piece of equipment information for each packet type from among pieces of equipment information on the basis of a level of detail of each piece of equipment information. Then, the second selection unit 144 selects, on the basis of a level of reliability predefined according to each packet type, a predetermined piece of equipment information corresponding to the equipment 20 from among pieces of equipment information inferred by the first selection unit 143.

Abstract: An artificial intelligence engine is trained via a historical data set derived from communication events to and from mobile platforms under various environmental conditions. The artificial intelligence engine makes dynamic predictions about optimum available communications assets and settings over the entire available frequency spectrum. The artificial intelligence engine may determine and store a quality metric associated with each prediction. Such stored data is used to refine the artificial intelligence engine’s predictive algorithms. The artificial intelligence engine is trained via analysis of linear and / or nonlinear equations, including partial differential equations, associating various factors to potential communication channels.

Abstract: To reproduce and measure a network load that occurs suddenly. A quality measurement device 1 includes a load analysis unit 21 which analyzes a load of a measurement target network 2, a reference load acquisition unit 22 which acquires reference load data 14 including a load of a reference network 3, a test condition determination unit 23 which determines, based on a load at a reference point of the reference network 3, a load to be added to a measurement point of the measurement target network 2 corresponding to the reference point, and a test traffic generation unit 24 which gives traffic corresponding to the load that is determined to the measurement point.

Abstract: Apparatuses, methods, and systems are disclosed for selecting a network slice type for media traffic of an IMS session. One apparatus includes a processor and a transceiver that communicates with a mobile communication network (“MCN”) using multiple network connections. The processor receives a first request to establish an IMS session with an IMS network, the first request including multiple media types. Here, the IMS network is reachable via the multiple network connections, each network connection communicating with a different network slice type of the MCN. The processor selects a network connection to be used for communicating media traffic for each media type and transmits a IMS session request to the IMS network using a first network connection. Here, the IMS session request indicates parameters to be used for communicating the media traffic for each media type using the selected network connection.

Abstract: Aspects of the disclosure provide methods and apparatuses for network data analytics. In some examples, an apparatus includes processing circuitry. The processing circuitry transmits a network data analytics function (NWDAF) service discovery request to a network repository function (NRF) network element. The NWDAF service discovery request indicates a requested network data analysis service. The processing circuitry receives an NWDAF service discovery response in response to the NWDAF service discovery request. The NWDAF service discovery response includes performance parameter information of one or more NWDAF network elements for the requested network data analysis service.

Abstract: Systems and methods for graph embedding already-collected but not yet connected data are disclosed. A method includes extracting a first set of actor-related data, a second set of object-related data, and a third set of temporal data from a set of the already-collected but not yet connected data representative of a unit-level contribution to the target activity. The method further includes generating graph data for at least one graph having a plurality of nodes and a plurality of edges using the set of the already-collected but not yet connected data, where each of the plurality of nodes corresponds to the actor or the object, and where an attribute associated with each of the plurality of edges corresponds to a measurement associated with the target activity during a temporal dimension of interest. The method further includes converting the graph data into metric space data using a graph embedding process.

Abstract: A system for generating an immune protocol for identifying and reversing immune disease is presented. The system comprising a computing device configured to receive at least an immune biomarker from a graphical user interface, determine a current immunological state of the user including an immune dysfunction as a function of the immune biomarker and an immune profile, assign an immune category to the immune profile as a function of the current immunological state, identify an effect on the immune profile for each nutritional element of a plurality of nutritional elements, determine at least a nutritional element that contributes to the immune category as a function of an immune machine-learning model and the nutritional input, identify a plurality of protocol elements, wherein each protocol element contains at least a nutrient amount intended to address the immunological dysfunction, and generate an immune protocol as a function of the plurality of protocol elements.

Abstract: Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Abstract: Systems and methods provide for adaptive real-time streaming of Autonomous Vehicle (AV) data. In some embodiments, the AV can receive a request from the remote computing system for real-time streaming of a first type of AV data and adaptively streaming a second type of AV data when one or more streaming conditions are satisfied. The first type of AV data and the second type of AV data can be captured as raw data by sensors, actuators, transducers, and other components of the AV. The AV can stream the first type of AV data to the remote computing system in real-time for a first time period. When the AV determines the streaming conditions are satisfied, the AV can automatically determine the second type of AV data to stream to the remote computing system in real-time for a second time period.

Abstract: Systems and methods for graph embedding already-collected but not yet connected data are disclosed. A method includes extracting a first set of actor-related data, a second set of object-related data, and a third set of temporal data from a set of the already-collected but not yet connected data representative of a unit-level contribution to the target activity. The method further includes generating graph data for at least one graph having a plurality of nodes and a plurality of edges using the set of the already-collected but not yet connected data, where each of the plurality of nodes corresponds to the actor or the object, and where an attribute associated with each of the plurality of edges corresponds to a measurement associated with the target activity during a temporal dimension of interest. The method further includes converting the graph data into metric space data using a graph embedding process.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: Aspects of the disclosure relate to enhanced processing systems for providing dynamic driving metric outputs using improved machine learning methods. A computing platform may receive sensor data from vehicle sensors. The computing platform may generate a pattern deviation output corresponding to an output of a sensor data analysis model, an actual outcome associated with a lowest TTC value, and driving actions that occurred over a prediction horizon corresponding to the pattern deviation output. The computing platform may cluster the pattern deviation outputs to maximize a ratio of inter-cluster variance to intra-cluster variance. The computing platform may train a long short term memory (LSTM) for each cluster, and may verify consistency of the pattern deviation outputs in the respective clusters.

Abstract: Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

Abstract: A mobile application development environment may be maintained in association with a computing platform. A request to produce a first mobile application with a first security policy may be processed. The first mobile application may be automatically produced. The first security policy may be integrated with the first mobile application during creation of the first mobile application. The first mobile application may be provided to a mobile application provider. The first security policy may be included in the first mobile application when the first mobile application is downloaded from the mobile application provider.

Abstract: As described herein, a machine learning system, method, and computer program are provided for inferring user presence in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, the network usage data is processed by a machine learning algorithm to infer whether a user is present in the residential space. Further, the inference is output for performing one or more related actions.

Abstract: A system that provides services to clients may receive and service requests, various ones of which may require different amounts of work. The system may determine whether it is operating in an overloaded or underloaded state based on a current work throughput rate, a target work throughput rate, a maximum request rate, or an actual request rate, and may dynamically adjust the maximum request rate in response. For example, if the maximum request rate is being exceeded, the maximum request rate may be raised or lowered, dependent on the current work throughput rate. If the target or committed work throughput rate is being exceeded, but the maximum request rate is not being exceeded, a lower maximum request rate may be proposed. Adjustments to the maximum request rate may be made using multiple incremental adjustments. Service request tokens may be added to a leaky token bucket at the maximum request rate.