Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a data entity that causes a processing unit to process a computational graph. In one aspect, method includes the actions of receiving data identifying a computational graph, the computational graph including a plurality of nodes representing operations; obtaining compilation artifacts for processing the computational graph on a processing unit; and generating a data entity from the compilation artifacts, wherein the data entity, when invoked, causes the processing unit to process the computational graph by executing the operations represented by the plurality of nodes.

Abstract: A system and method for generating a prediction are disclosed. In one embodiment, the method includes receiving a plurality of time-based documents; receiving a user query including a time period of interest defining a subset of the time-based documents from which to generate a prediction; and a plurality of cloud-based software agents classifying the subset of the plurality of time-based documents into a plurality of classes for the plurality entities, wherein the plurality of cloud-based software agents intercommunicate using distributed processing, wherein each of the plurality of cloud-based software agents is dedicated to one of the entities in the plurality of entities; and generating, using at least one machine learning method, the prediction based on the subset of the plurality of time-based documents for at least one of a plurality of categories. However, other embodiments are disclosed.

Abstract: Computer-implemented methods and systems are provided for automatically performing a task on a remote computer. During a registration stage, the system receives personal information of a human user, obtains an IP address and a device configuration for a computing device, and stores the personal information, IP address, and the device configuration in record in a database. The system receives a request to interact with a remote website to perform a task, the request including the personal information of the human user. The system then retrieves the record from the database using the personal information. The system creates a virtual machine based on the device configuration for the computing device, selects one of a geographically distributed set of proxy servers having an IP geographically address resembling the IP address for the computing device, and executes instructions causing the virtual machine to interact with the remote website using the proxy server to perform the task.

Abstract: Systems and methods are provided for determining a clean endpoint time for a current run of a chamber. The clean endpoint time for the current run may be determined by determining that a chamber parameter, such as a chamber pressure, has stabilized. Historical clean endpoint time data is updated by adding the clean endpoint time for the current run of the chamber. A recommended clean endpoint time is then determined for the chamber based on the updated historical clean endpoint time data.

Abstract: A method and system for predicting the onset of a disease is provided.

Abstract: A system can generate a heavy load pre-warning or an overload pre-warning for distribution transformers. Operation of the system can include selecting data records received from a plurality of data sources; converting the data records in the plurality of different data formats; filtering the data records in the database by using a predetermined threshold and matching each of the filtered data records with one of a plurality of distribution transformers; transforming the matched data records to a plurality of predefined predictor variables; selecting a subset of the plurality of predefined predictor variables; training, testing and tuning a model and forecasting at least one of heavy load or overload for each of the plurality of distribution transformers in a predetermined region.

Abstract: Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

Abstract: Various embodiments train a prediction model for predicting a label to be allocated to a prediction target explanatory variable set. In one embodiment, one or more sets of training data are acquired. Each of the one or more sets of training data includes at least one set of explanatory variables and a label allocated to the at least one explanatory variable set. A plurality of explanatory variable subsets is extracted from the at least one set of explanatory variables. A prediction model is trained utilizing the training data. The plurality of explanatory variable subsets is reflected on a label predicted by the prediction model to be allocated to a prediction target explanatory variable set with each of the plurality of explanatory variable subsets weighted respectively.

Abstract: Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature.

Abstract: The various aspects configure a mobile computing device to efficiently identify, classify, model, prevent, and/or correct the conditions and/or behaviors occurring on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device and that often degrade the performance and/or power utilization levels of the mobile computing device over time. In the various aspects, the mobile computing device may obtain a classifier model that includes, tests, and/or evaluates various conditions, features, behaviors and corrective actions on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device. The mobile computing device may utilize the classifier model to quickly identify and correct undesirable behaviors occurring on the mobile computing device that are related to the one or more connected peripheral devices.

Abstract: Methods are provided for clustering events. Data is received at an extraction engine from managed infrastructure. Events are converted into alerts and the alerts mapped to a matrix M. One or more common steps are determined from the events and clusters of events are produced relating to the alerts and or events.

Abstract: A computer-implemented system and method for detecting anomalies using sample-based rule identification is provided. Data for data is maintained analytics in a database. A set of anomaly rules is defined. A rare pattern in the data is statistically identified. The identified rare pattern is labeled as at least one of anomaly and non-anomaly based on verification by a domain expert. The set of anomaly rules is adjusted based on the labeled anomaly. Other anomalies in the data are detected and classified by applying the adjusted set of anomaly rules to the data.

Abstract: A processor based system and method of generating cognitive pattern knowledge of a sensory input is disclosed. The method comprising the steps of receiving sensory input to create at least one concrete pattern, receiving at least one abstract pattern comprising abstract segments and vertically blending the concrete pattern with the abstract pattern by selectively projecting abstract segments to create a vertically blended pattern whereby the vertically blended pattern represents cognitive pattern knowledge of the sensory input. In some embodiments, the systems and methods further comprise creating a measure of a degree of vertical blending and when the measure of the degree of vertical blending exceeds a threshold, horizontally blending at least two abstract patterns to create a horizontally blended abstract pattern.

Abstract: Systems and methods are provided for estimating arrival time associated with a ride order. An exemplary method may comprise: inputting transportation information to a trained machine learning model. The transportation information may comprise an origin and a destination associated with the ride order, and the trained machine learning model may comprise a wide network, a deep neural network, and a recurrent neural network all coupled to a multilayer perceptron network. The method may further comprise, based on the trained machine learning model, obtaining an estimated time for arriving at the destination via a route connecting the origin and the destination.

Abstract: Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.

Abstract: A method for implementing a reservoir simulator is described. The method comprises developing training data by performing a calculation on an initial set of input data relating to reservoir conditions to obtain corresponding output data; training an artificial neural network (“ANN”) to perform the calculation using the training data; and using the trained ANN to perform the calculation on a second set of input data to obtain corresponding output data for use by the reservoir simulator in performing simulations.

Abstract: Invoking an agent during a dialog between a user and an automated assistant. Some implementations are directed to receiving, during a human-to-automated assistant dialog, natural language input of the user that indicates a desire to engage an agent, but that fails to indicate a particular agent to be engaged. Those implementations are further directed to selecting a particular agent from a plurality of available agents, and transmitting an invocation request to the selected particular agent. In some implementations an agent selection model can be utilized in selecting the particular agent, such as a machine learning model. The machine learning model can be trained to enable generation of output that indicates, for each of a plurality of available agents (and optionally intent(s) for those agents), a probability that the available agent (and optionally intent) will generate appropriate responsive content.

Abstract: A method begins by agriculture equipment collecting current on-site gathered agriculture data regarding an agriculture region and sending at least a representation of the current on-site gathered agriculture data to a host device. The method continues with the host device processing one or more of the at least a representation of the current on-site gathered agriculture data, current off-site gathered agriculture data, historical on-site gathered agriculture data, historical off-site gathered agriculture data, and historical analysis of agriculture predictions regarding the agriculture region to produce a current agriculture prediction for the agriculture region. The method continues with the host device generating an agriculture prescription regarding at least a portion of the agriculture region based on the current agriculture prediction and sending the agriculture prescription to one or more of the agriculture equipment.

Abstract: Provided are methods and systems for knowledge discovery utilizing knowledge profiles.

Abstract: A computer implemented system and method provides a volume of activation (VOA) estimation model that receives as input two or more electric field values of a same or different data type at respective two or more positions of a neural element and determines based on such input an activation status of the neural element. A computer implemented system and method provides a machine learning system that automatically generates a computationally inexpensive VOA estimation model based on output of a computationally expensive system.