In a metrology sampling method, various index values that can detect various status changes of a process tool (such as maintenance operation, parts changing, parameter adjustment, etc.), and/or information abnormalities of the process tool (such as abnormal process data, parameter drift/shift, abnormal metrology data, etc.) appear in a manufacturing process are applied to develop an intelligent sampling decision (ISD) scheme for reducing sampling rate while VM accuracy is still sustained. The indices includes a reliance Index (RI), a global similarity index (GSI), a process data quality index (DQI.sub.X) and a metrology data quality index (DQI.sub.y).

A method for operating a neuromorphic memory circuit. The method includes accumulating a dendrite LIF charge over time on a conductive dendrite LIF line. A first transmitting operation transmits an axon LIF pulse on a conductive axon LIF line. A first switching operation switches on a LIF transistor by the axon LIF pulse such that the LIF transistor provides a discharge path for the dendrite LIF charge through a programmable resistive memory element when the axon LIF line transmits the axon LIF pulse. A second transmitting operation transmits a dendrite STDP pulse if the dendrite LIF charge falls below a threshold voltage. A third transmitting operation transmits an axon STDP pulse on a conductive axon STDP line. A second switching operation switches on a STDP transistor by the axon STDP pulse. The STDP transistor provides an electrical path for the dendrite STDP pulse through the programmable resistive memory element when the axon STDP line transmits the axon STDP pulse.

The present tool and method relate to device fault detection, diagnosis and prognosis. More particularly, the present tool and method store in a database a plurality of measured indicators representative of at least one dynamic condition of the device. The present tool and method further binarize by a processor the plurality of measured indicators, and analyze the plurality of binarized measured indicators using a machine learning data tool for extracting at least one pattern from the binarized measured indicators by adding at least one different constraint to each iteration. The at least one extracted pattern is indicative of whether the device has a fault or not.

One or more social interactive goals for an automated entity such as an avatar may be determined during a social interaction between the automated entity and a selected entity such as a human. Identity attributes of identity images from an identity model of the automated entity may be used to determine a set of behavioral actions the automated entity is to take for the determined goals. Paralanguage elements expressed for the automated entity via a user interface may be altered based on the determined set of behavioral actions. The automated entity may refer to a computer implemented automaton that simulates a human in the user interface of an interactive computing environment. By way of example, an avatar cybernetic goal seeking behavior may be implemented in accordance with an identity theory model.

Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of generated artificial data samples for a minority data class of the imbalanced training data set. The number of kernels within the model is generated based on the set of generated artificial data samples. A likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set is calculated. Parameters of each kernel in the number of kernels are updated based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set. Each kernel in the number of kernels is adjusted based on the updated parameters.

A computer-implementable method for providing cognitive insights comprising: receiving streams of data from a plurality of data sources; processing streams of data from a plurality of data sources via a plurality of agents, the processing the streams of data from the plurality of data sources via the plurality of agents performing a respective plurality of cognitive operations on the streams of data; and, providing cognitive insights based upon the performing the respective plurality of cognitive operations on the streams of data from the plurality of data sources.

A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source, the public data source comprising publicly available travel information, the private data source comprising privately managed, company specific travel information; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system.

Embodiments of the invention provide a neurosynaptic system comprising a first set of one or more neurosynaptic core circuits configured to receive input data comprising multiple input regions, and extract a first set of features from the input data. The features of the first set are computed based on different input regions. The system further comprises a second set of one or more neurosynaptic core circuits configured to receive the first set of features, and generate a second set of features by combining the first set of features based on synaptic connectivity information of the second set of core circuits.

A computer system and method for improving the accuracy of predictions of the amount of renewable energy, such as solar energy and wind energy, available to an electric utility, and/or refine such predictions, by providing improved integration of meteorological forecasts. Coefficient values are calculated for a renewable energy generation model by performing a regression analysis with the forecasted level of renewable energy posted by the utility, forecasted weather conditions and measures of seasonality as explanatory variables. Accuracy is further enhanced through the inclusion of a large number of time series variables that reflect the systematic nature of the energy/weather system. The model also uses the original forecast posted by the system operator as well as variables to control for season.

A method and an apparatus for data processing in data modeling, where the method includes performing, according to a data transformation function corresponding to a preset data processing category identifier, data transformation on a data column corresponding to each characteristic in original data in order to generate a corresponding extended characteristic column, combining extended characteristic columns corresponding to all the characteristics in the original data in order to generate an extended characteristic set, determining a correlation coefficient of each characteristic in the extended characteristic set, selecting a characteristic whose correlation coefficient satisfies a specified condition as an important characteristic, and obtaining, by screening from the extended characteristic set, a data column corresponding to the important characteristic. Therefore, problems such as a long consumed time and a large calculation amount caused because data modeling is performed by exhaustively listing data preprocessing methods are avoided, thereby improving calculation efficiency.