A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving operational parameters for one or more automation devices, wherein the one or more automation devices are configured to implement control logic generated based on a decision tree. The operations also include receiving an output by the decision tree based on the operational parameters. Further, the operations include determining the output is an anomalous output based on a constraint associated with the decision tree. Further still, the operations include generating an updated decision tree based on the anomalous output. Even further, the operations include generating updated control logic for the one or more automation devices based on the updated decision tree. Even further, the operations include sending the updated control logic to the one or more automation devices.

Systems and methods that address an optimized method in the area of optimization by showing how to generate Ising Hamiltonians automatically for a large class of optimization problems specially handling the constraints. The innovation facilitates qubit reduction in connection with an optimization problem by representing respective integer variables as linear sums of binary variables, wherein depending on the representation, additional equality constraints are provided. Additional slack variables are introduced to change inequality constraints to equality constraints. Based on the equality constraints, an unconstrained pseudo-boolean optimization problem is created. The pseudo-boolean optimization problem is quadratized to generate a quadratic pseudo-boolean function (QPBF) and the number of variables in the QPBF is reduced to facilitate qubit reduction. This results in an automated, problem instance dependent qubit reduction procedure. Thus, this innovation provides an effective method to solve such class of optimization problems by formulating efficient Ising Hamiltonians for integer optimization problems followed by an automated qubit reduction procedure to get the final Ising Hamiltonian, which can be solved using a quantum optimization algorithm.

A spectrum y includes a waveform-of-interest component and a baseline component serving as a wide-band component. An optimum solution of a signal model x is determined according to a first condition to fit a corresponding portion S.sub.IFx of a baseline model Fx with respect to a representative portion y.sub.I of the baseline component, and a second condition to minimize an Lp norm (wherein p≤1) of the signal model x. An estimated baseline component determined from the optimum solution of the signal model x is subtracted from the spectrum y.

A power electronic circuit fault diagnosis method based on Extremely randomized trees (ET) and Stack Sparse auto-encoder (SSAE) algorithm includes the following. First, collect the fault signal and extract fault features. Then, reduce the dimensionality of fault features by calculating the importance value of all features using ET algorithm. A proportion of the features to be eliminated is determined, and a new feature set is obtained according the value of importance. Further extraction of fault features is carried by using SSAE algorithm, and hidden layer features of the last sparse auto-encoder are obtained as fault features after dimensionality reduction. Finally, the fault samples in a training set and a test set are input to the classifier for training to obtain a trained classifier. And mode identification, wherein the fault of the power electronic circuit is identified and located by the training classifier.

A device may receive a job request that requests performance of one or more operations by resources of a high-performance computing environment, and may process the job request, with a policy execution model trained with policy parameters, to identify policies to apply during execution of the job request. The device may process the job request, with a forecast object model trained with job data and profile data, to generate a forecast of resources and licenses required from the high-performance computing environment. The device may process the job request, other job requests, the one or more of the policies, and the forecast, with a heuristic model, to determine a schedule for the job request, and may process the schedule and current constraints on the resources and the licenses, with a linear programming model, to determine an optimized schedule for the job request.

The present disclosure relates to system and methods for predicting performance caused by software code changes. For this purpose, an augmented machine learning model predicts a latency of software module with updated code executed in a production environment. In some aspects, the latency is predicted based on a change of deviation that is determined by comparing the latency of the software module with updated code and the latency of the software module without updated code, whereas the software modules are executed in environments different from the production environment.

A method includes receiving data comprising a plurality of requests and a plurality of responses to the requests. The requests and the responses are associated with a virtual assistant programmed to address the plurality of requests. In the method, a machine learning (ML) classifier is used to partition the requests into a plurality of partitions corresponding to a plurality of request types. An interface for a user is generated to display a subset of the requests corresponding to at least one partition of the plurality of partitions and to display a response corresponding to the subset of the plurality of requests, wherein the response is based on one or more of the plurality of responses. The interface is configured to permit editing of the response by the user. The method also includes processing the response edited by the user, and transmitting the edited response to the virtual assistant.

A method includes receiving data comprising a plurality of requests and a plurality of responses to the requests. The requests and the responses are associated with a virtual assistant programmed to address the plurality of requests. In the method, a machine learning (ML) classifier is used to partition the requests into a plurality of partitions corresponding to a plurality of request types. An interface for a user is generated to display a subset of the requests corresponding to at least one partition of the plurality of partitions and to display a response corresponding to the subset of the plurality of requests, wherein the response is based on one or more of the plurality of responses. The interface is configured to permit editing of the response by the user. The method also includes processing the response edited by the user, and transmitting the edited response to the virtual assistant.

An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based thereon. The device may also include a digital camera to capture an image of a face of an attempted user to enable facial recognition to alter a locked state of the device.

An aerosol delivery device is provided that includes sensor(s) to produce measurements of properties during use of the device, and processing circuitry to record data for a plurality of uses of the device, for each use of which the data includes the measurements of the properties. The processing circuitry is configured to build a machine learning model to predict a target variable, using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties. The processing circuitry is configured to then deploy the machine learning model to predict the target variable, and control at least one functional element of the device based thereon. The device may also include a digital camera to capture an image of a face of an attempted user to enable facial recognition to alter a locked state of the device.