A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model for predicting event tags. The system obtains asset data for an electric power distribution system in a geographic area. The asset data includes: for each of a plurality of electrical assets of the electrical power distribution system, data indicating one or more characteristics of the electrical asset. The system further obtains sensor data for the electric power distribution system. The sensor data includes measurement data from a plurality of electric sensors. The system generates, by processing the asset data and the sensor data, partition data that includes, for each of the plurality of electrical assets, an assignment that assigns the electrical asset to one of a set of feeder networks.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model for predicting event tags. The system obtains asset data for an electric power distribution system in a geographic area. The asset data includes: for each of a plurality of electrical assets of the electrical power distribution system, data indicating one or more characteristics of the electrical asset. The system further obtains sensor data for the electric power distribution system. The sensor data includes measurement data from a plurality of electric sensors. The system generates, by processing the asset data and the sensor data, partition data that includes, for each of the plurality of electrical assets, an assignment that assigns the electrical asset to one of a set of feeder networks.

Systems and methods for an On-Demand Autonomy (ODA) service. The system includes a set of functional modules enabled for ODA service activities disposed in a follower vehicle (Fv) in communication with an ODA server that includes a user request module configured to receive request information from the Fv from the ODA server, and to process and communicate the request information to the schedule module; the schedule module configured to coordinate an arrival time information with the ODA server for pickup of the Fv based on the request information, and communicate the arrival time information to the schedule execution module; the schedule execution module configured to direct the Fv to a pickup point based on the arrival time information, and communicate the pickup point to the indication module; and the indication module configured to provide alerts to vehicles in the vicinity of the pickup of the Fv via the ODA service.

Systems and methods for an On-Demand Autonomy (ODA) service. The system includes a set of functional modules enabled for ODA service activities disposed in a follower vehicle (Fv) in communication with an ODA server that includes a user request module configured to receive request information from the Fv from the ODA server, and to process and communicate the request information to the schedule module; the schedule module configured to coordinate an arrival time information with the ODA server for pickup of the Fv based on the request information, and communicate the arrival time information to the schedule execution module; the schedule execution module configured to direct the Fv to a pickup point based on the arrival time information, and communicate the pickup point to the indication module; and the indication module configured to provide alerts to vehicles in the vicinity of the pickup of the Fv via the ODA service.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

A test and measurement instrument has an input configured to receive a signal from a device under test, a memory, a user interface to allow the user to input settings for the test and measurement instrument, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to: acquire a waveform representing the signal received from the device under test; generate one or more tensor arrays based on the waveform; apply machine learning to the one or more tensor arrays to produce equalizer tap values; and apply equalization to the waveform using the equalizer tap values to produce an equalized waveform; and perform a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test. A method of testing a device under test includes acquiring a waveform representing a signal received from the device under test, generating one or more tensor arrays based on the waveform, applying machine learning to the one or more tensor arrays to produce equalizer tap values, applying the equalizer taps values to the waveform to produce an equalized waveform, performing a measurement on the equalized waveform to produce a value related to a performance requirement for the device under test.

A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform. A method of testing devices under test includes receiving a waveform created by operation of a device under test, generating one or more tensor arrays, applying machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, applying machine learning to a second tensor array of the one or more tensor arrays to produce predicted tuning parameters for the device under test, using the equalizer tap values to produce a Transmitter Dispersion Eye Closure Quaternary (TDECQ) value, and providing the TDECQ value and the predicted tuning parameters to a test automation platform.

System and method for device constellation according to certain embodiments. For example, a method for device constellation, the method includes the steps of: receiving a request, the request including a plurality of request parameters; decomposing the request into one or more tasks; selecting one or more edge devices based at least in part on the plurality of request parameters; assigning the one or more tasks to the one or more selected edge devices to cause the one or more selected edge devices to perform the one or more tasks; and receiving one or more task results from the one or more selected edge devices.