Machine Learning

Systems and methods for formatting data are disclosed. For example, a system may include at least one memory storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving data comprising a plurality of sequences of data values and training a recurrent neural network model to output conditional probabilities of subsequent data values based on preceding data values in the data value sequences. The operations may include generating conditional probabilities using the trained recurrent neural network model and the received data. The operations may include determining a data format of a subset of the data value sequences, based on the generated conditional probabilities, and reformatting at least one of the data value sequences according to the determined data format.

A smart circuit breaker may provide a smart-circuit-breaker power monitoring signal that includes information about power consumption of devices connected to the smart circuit breaker. The smart-circuit-breaker power monitoring signal may be used in conjunction with power monitoring signals from the electrical mains of the building for providing information about the operation of devices in the building. For example, the power monitoring signals may be used to (i) determine the main of the house that provides power to the smart circuit breaker, (ii) identify devices receiving power from the smart circuit breaker, (iii) improve the accuracy of identifying device state changes, and (iv) train mathematical models for identifying devices and device state changes.

Systems and methods for adding process actions to the design of a robotic software process. A context-recognition module recognizes a current state of a process being designed, and passes information on that current state to a recommendation module. The recommendation module evaluates the current state and identifies at least one suitable process action to recommend in response to that current state. The recommendation module then recommends the at least one process action to the human designer. If the designer accepts the recommendation, a design module adds the process action to the process design. The recommendation module may also use information about previous actions in the process and in other processes when identifying suitable process actions. The context-recognition module and the recommendation module may each comprise at least one machine learning module, which may or may not be neural network based.

Systems and methods enable the classification of each value of multiple floating-point values stored in a first vector register, and storage in a second vector register multiple elements that each indicate a respective classification of a respective value of the multiple floating-point values. A system includes a functional unit, first and second vector registers coupled to the functional unit, and processing circuitry. The processing circuitry is configurable, e.g., via an instruction, to cause the functional unit to perform the classification and storage operations.

A transmitting device for wireless communication calculates distortion error based on a non-distorted digital transmit waveform and a non-linearity. The transmitting device compresses the distortion error with an encoder neural network of an auto-encoder. The transmitting device transmits, to a receiving device, the compressed distortion error to compensate for the non-linearity in a power amplifier (PA).