A computer-implemented method is provided for autonomously controlling a vehicle to perform a vehicle operation. The method includes steps of applying a passive actor-critic reinforcement learning method to passively-collected data relating to the vehicle operation, to learn a control policy configured for controlling the vehicle so as to perform the vehicle operation with a minimum expected cumulative cost; and controlling the vehicle in accordance with the control policy to perform the vehicle operation.

A symbol prediction method includes storing a statistic for each of a set of symbols w in at least one context, each context including a string of k preceding symbols and a string of l subsequent symbols, the statistic being based on observations of a string kwl in training data. For an input sequence of symbols, a prediction is computed for at least one symbol in the input sequence, based on the stored statistics. The computing includes, where the symbol is in a context in the sequence not having a stored statistic, computing the prediction for the symbol in that context based on a stored statistic for the symbol in a more general context.

The present subject matter discloses system 102 and method to enable machine learning based analytics platform. The method may comprise generating a graphical user interface to enable one or more stake holders to generate and manage a model for predictive analysis. The method may further comprise enabling a business user to define the business problem, and generate models to perform predictive analysis. The method may further comprise deploying the model, in distributed environment, over a target platform. The method may further comprise monitoring the model to identify at least one error in the model and re-training the model for performing predictive analysis based on the at least one error, thereby enable machine learning based analytics platform.

The present teaching relates to method, system, and programs for training a ranking model for ranking content items. In one example, a set of content items is obtained. A plurality types of online user activities performed with respect to the set of content items are obtained. For each of the set of content items, a plurality of user engagement scores are determined. Each of the plurality of user engagement scores is determined based on a corresponding one of the plurality types of online user activities. For each of the set of content items, an aggregated score is calculated based on the plurality of user engagement scores to generate aggregated scores. A ranking model is trained based on the aggregated scores.

An online system identifies an impression opportunity for a target user of the online system. The online system accesses predictors for a third party system, each predictor determining a prediction value indicating a likelihood of users to provide a specified benefit to the third party system after a specified timeframe from the performance of a specified type of action by the users at the online system, each predictor trained using a training feature set extracted from an impressions log including metadata for past impression opportunities made to users. The online system determines a combined bid value for the third party system based on prediction values determined by the predictors trained for the third party system. In response to determining that the combined bid value for the third party system is a winning bid value, the online system presents a sponsored content from the third party system to the user.

Quantum bit (qubit) circuits, coupler circuit structures and coupling techniques are described. Such circuits and techniques may be used to provide multi-qubit circuits suitable for use in multichip modules (MCMs).

In one embodiment, a method includes one or more computing devices detecting a triggering action by a user of a social-networking system, wherein the detecting includes receiving information about the triggering action from a client device associated with the user and accessing a queue including multiple notifications. The method also includes, for each of one or more of the notifications, calculating using a machine-learning model, a click-through probability that the user will interact with the notification upon display of the notification, wherein the machine-learning model is based at least in part on one or more features associated with the user or the notification, determining whether the click-through probability satisfies a threshold, and if the click-through probability satisfies the threshold, then sending the notification to the client device associated with the user for display, else, removing the notification from the queue.

A diagnostic service system includes one or plurality of factory monitoring systems configured to perform monitoring of at least one machine; a service center management device that is connected with the one or plurality of factory monitoring systems via a network; one or plurality of service centers that are connected with the service center management device; and a plurality of service terminals connected with one service center or each of the plurality of service centers via a service control. The plurality of service terminals are used by each responder capable of fault diagnosis of the machine, and when fault of a machine occurs, one of the plurality of service terminals is selected via the service center management device and the one service center or plurality of service centers.

The programmable communication system supports communication between both user devices message broker server(s) using a processor-based programmable modular block device implementing an execution engine and programmed to communicate with other processors through a message broker server using a predefined communication protocol. The block device includes a device port for coupling to sensor(s) and actuator(s), and a communication port to communicate with other processors using said predefined communication protocol. An editor program discovers and acquires information about the block device and about other devices in communication with the block device directly or via a message broker. The editor generates and downloads to the block device a rules-based program based on the acquired information. The block device uses the execution engine to execute the program and thereby obtain information through the ports and provide information and control signals.

A computer-implemented method, computerized apparatus and computer program product for claim generation, the method comprising: selecting at least one subject according to a given topic; selecting at least one verb from a first data source; selecting at least one object from a second data source; generating one or more candidate claim sentences, each of which composed of a subject selected from the at least one subject, a verb selected from the at least one verb and an object selected from the at least on object; and determining validity of the candidate claim sentences using a machine learning process.