Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract: A method for optimizing an operation of at least one wind turbine includes defining a plurality of test parameters that include a plurality of test points for at least one wind turbine operational parameter, wherein each test point includes a plurality of test values for the wind turbine operational parameter, and defining at least one test sequence of the plurality of test points. The method also includes initiating a test that executes the plurality of test points within the at least one randomized test sequence and measures at least one operating condition of the at least one wind turbine at each test point.

Abstract: A method for optimizing an operation of at least one wind turbine includes defining a plurality of test parameters that include a plurality of test points for at least one wind turbine operational parameter, wherein each test point includes a plurality of test values for the wind turbine operational parameter, and defining at least one test sequence of the plurality of test points. The method also includes initiating a test that executes the plurality of test points within the at least one randomized test sequence and measures at least one operating condition of the at least one wind turbine at each test point.

Abstract: A system and method is provided for indicating a masking event using transmission wiring delegated for indicating intrusion and tampering events. The method of indicating a masking event utilizes measurable changes in resistance to indicate the current masking state of a detector. When a detector is not masked, a first resistance value is measurable on the transmission wiring, while when a detector is masked, a second resistance value is measurable on the transmission wiring. The transmission wiring connects the detector to a control unit that executes a security-related response based on the value of the measured resistance.

Abstract: A technique is disclosed for generating variance data and a variance map from measured projection data acquired from a tomography system. The method comprises accessing the measured projection data from the tomography system. The method further comprises generating the variance map from the measured projection data and displaying, analyzing or processing the variance map. The variance data is determined based upon a statistical model from measured image data, and may be used for image analysis, data acquisition, in computer aided diagnosis routines, and so forth.