A local extremum calculator detects a local maximum sample and a local minimum sample of a digital audio signal. A number-of-sample detector detects a sample interval between the local maximum sample and the local minimum sample. A difference value calculator calculates difference values between adjacent samples. A correction value calculator calculates a first correction value by multiplying the difference value between the local maximum sample and a first adjacent sample by a coefficient and calculates a second correction value by multiplying the difference value between the local minimum sample and a second adjacent sample by the coefficient. When a periodic signal detector detects that the digital audio signal is a single sine wave, an adder/subtractor does not add the first correction value to the first adjacent sample, and does not subtract the second correction value from the second adjacent sample.

An apparatus comprises: selection circuitry to select the two most preferred items from a set of items having ranking information indicative of an order of preference for the set of items. The selection circuitry comprises at least one selection node circuit, each selection node circuit to receive as inputs an indication of a first pair of items and a second pair of items among the set of items, and comprises first selection circuitry and second selection circuitry. The first selection circuitry to first selection circuitry to select as a first selected item a most preferred one of: a most preferred ranked item of the first pair, and a least preferred item of the second pair. The second selection circuitry to select as a second selected item a most preferred one of: a least preferred item of the first pair, and a most preferred item of the second pair.

An apparatus comprises: selection circuitry to select the two most preferred items from a set of items having ranking information indicative of an order of preference for the set of items. The selection circuitry comprises at least one selection node circuit, each selection node circuit to receive as inputs an indication of a first pair of items and a second pair of items among the set of items, and comprises first selection circuitry and second selection circuitry. The first selection circuitry to first selection circuitry to select as a first selected item a most preferred one of: a most preferred ranked item of the first pair, and a least preferred item of the second pair. The second selection circuitry to select as a second selected item a most preferred one of: a least preferred item of the first pair, and a most preferred item of the second pair.

A system, apparatus and method for selecting a value for an independent variable that determines an operating state of a system described by a performance function. In one embodiment, the method includes establishing a range of values for the independent variable, selecting a number of values in the range of values to test the independent variable, and selecting random values within the range of values for the independent variable based on the number of values. The method also includes evaluating the performance function at the random values, and selecting the value of the independent variable from the random values that provides an extremum value for the performance function.

A system, apparatus and method for selecting a value for an independent variable that determines an operating state of a system described by a performance function. In one embodiment, the method includes establishing a range of values for the independent variable, selecting a number of values in the range of values to test the independent variable, and selecting random values within the range of values for the independent variable based on the number of values. The method also includes evaluating the performance function at the random values, and selecting the value of the independent variable from the random values that provides an extremum value for the performance function.

A system and method for constructing a mathematical model of a system. The method includes constructing an initial mathematical system representation with a combination of terms, the terms comprising mathematical functions including independent variables dependent on an input signal. A first set of known data is inputted to the initial mathematical representation to generate a corresponding set of output data. The corresponding set of output data of the initial mathematical representation and a second set of known data, correlated to the first set of known data, is fed to a comparator to generate error signals representing differences between output data and correlated members of the second set of known data. A parameter of the combination of terms is iteratively varied to produce a refined mathematical representation of the system until a measure of the error signals is reduced to a value wherein the set of corresponding output data of the refined mathematical representation over a desired range is approximately equivalent to the second set of known data.

A system and method for predicting a characteristic of an object in an artificial intelligence system. The method includes evaluating the object using a first model to produce a first prediction of a characteristic of the object. The object is evaluated using a second model to produce a second prediction of the characteristic of the object, the second model being dissimilar to the first model. A final prediction of the characteristic of the object is generated as a function of dynamic weightings of the first prediction and the second prediction.

A system and method for estimating a system state. The method includes constructing a first estimate of a system state at a first time including a first covariance matrix describing an accuracy of the first estimate. A second estimate of the state is constructed at a second time, after the first time, including a second covariance matrix. A value of a characteristic of the system state is measured at the second time and the second estimate of the system state and the second covariance matrix are adjusted based on the value of the characteristic. A third estimate of the system state is constructed at a third time, before the second time, including a third covariance matrix describing an accuracy of the third estimate. A fourth estimate of the system state is constructed at a fourth time being after the second time.

A method, system, and computer program product, including generating a contrastive explanation for a decision of a classifier trained on structured data, highlighting an important feature that justifies the decision, and determining a minimal set of new values for features that alter the decision.

A method, system, and computer program product, including generating a contrastive explanation for a decision of a classifier trained on structured data, highlighting an important feature that justifies the decision, and determining a minimal set of new values for features that alter the decision.