A control system and method for controlling a system, which employs a data set representing a plurality of states and associated trajectories of an environment of the system; and which iteratively determines an estimate of an optimal control policy for the system. The iterative process performs the substeps, until convergence, of estimating a long term value for operation at a respective state of the environment over a series of predicted future environmental states; using a complex return of the data set to determine a bound to improve the estimated long term value; and producing an updated estimate of an optimal control policy dependent on the improved estimate of the long term value. The control system may produce an output signal to control the system directly, or output the optimized control policy. The system preferably is a reinforcement learning system which continually improves.

Systems, methods, and non-transitory computer-readable media according to certain aspects can receive at least one message sent by a user of a social networking system to a page provided by the social networking system, where the page is associated with an entity. A training data set including a plurality of messages can be determined, and the training data set can indicate an intent classification for each of the plurality of messages. The intent classification can be indicative of an intent associated with a particular message. A machine learning model may be trained based at least in part on the training data set. A first intent classification for the at least one message can be determined, based at least in part on the machine learning model.

Systems and methods are described for facilitating operation of a plurality of computing devices. Data indicative of enumerated resources of a computing device is collected. The data is collected without dependency on write permissions to a file system of the one computing device. A condition of the computing device is determined based on historical data associated with enumerated resources of other computing devices. The identified condition can be updated as updated historical data becomes available. A communication to the computing device may be sent based on the identified condition.

Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Certain embodiments described herein relate to an improved disk usage growth prediction system. In some embodiments, one or more components in an information management system can determine usage status data of a given storage device, perform a validation check on the usage status data using multiple prediction models, compare validation results of the multiple prediction models to identify the best performing prediction model, generate a disk usage growth prediction using the identified prediction model, and adjust the available space of the storage device according to the disk usage growth prediction.

An analysis apparatus according to one embodiment includes: an obtainment unit configured to obtain a data set of multiple data items having randomness; and an analysis unit configured to calculate, as an inner product or a norm of probability measures μ and ν being probability measures on the data set and taking values in a von Neumann algebra, by using a mapping Φ that extends kernel mean embedding, an inner product or a norm of Φ(μ) and Φ(ν) mapped onto an RKHM.

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

The present invention relates to a method for alerting drivers and/or autonomous vehicles of high risk scenarios. The method includes obtaining positional data of a vehicle, where the positional data is indicative of geographical position and heading of the vehicle. The method further includes obtaining environmental data of the vehicle, where the environmental data is indicative of state of the surrounding environment of the vehicle. The method includes determining, by means of trained model, accident intensity for upcoming road portion for the vehicle, the trained model being configured to determine accident intensity associated with the upcoming road portion based on the obtained environmental data and the obtained positional data. Then, if the determined accident intensity exceeds threshold, the method comprises transmitting signal indicating approaching high risk scenario to a Human-Machine-Interface, HMI, of the vehicle and/or to a control system of the vehicle.

A system described herein may provide a technique for identifying and remediating potential threat vectors in a system, such as containers or applications in a virtual or cloud computing environment. Attributes of potential threat vectors may be identified, and the potential threat vectors may be scored based on the attributes. Values or scores of individual attributes may be determined through machine learning or other suitable techniques. Scores exceeding a threshold may indicate that a remedial measure should be performed. A remedial measure may be identified using machine learning or other suitable techniques. After the remedial measure is performed, the threat vector may be scored again, and a machine learning model may be refined based on whether the remedial measure was successful.

A system and method are provided for validating data. The method is executed by a device having a data interface coupled to a processor and includes obtaining a validation set comprising at least one validation case, each validation case comprising at least one test condition. The method also includes obtaining, via the data interface, at least one data set to be validated using the validation set. The method also includes applying the validation set to the at least one data set to validate the data in the data set by, for each record in the at least one data set, validating a value in the record according to the at least one test condition. The method also includes outputting a validation result for each record.