Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A generative adversarial network (GAN) may be implemented to recognize named entity types in detection of sensitive information in datasets. The GAN may include a generator and a discriminator. The generator may be trained to produce synthetic data to include information that simulates named entity types representing the sensitive information. The discriminator may be fed with real data that are known to include the sensitive information (as positive examples), together with the synthetic data that simulate the sensitive information (as negative examples), to train to classify the real vs. synthetic data. In field operations, the discriminator may be deployed to perform named entity type recognition to identify data having the sensitive information. The generator may be deployed to provide anonymous data in lieu of real data to facilitate sensitive information sharing and disclosure.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: Implementations of wind energy devices may include a frame coupled to each of a first rotor wheel, a second rotor wheel, a third rotor wheel, and a fourth rotor wheel. Implementations of wind energy devices may also include a first cable configured to rotate about the first rotor wheel and the second rotor wheel and a second cable configured to rotate about the third rotor wheel and the fourth rotor wheel. Implementations of wind energy devices may also include a plurality of airfoils coupled between the first cable and the second cable. Implementations of wind energy devices may include a first generator and a second generator. Implementations of wind energy devices may include a controller coupled to the first generator and the second generator. The controller may be configured to control a speed of rotation of the plurality of airfoils.

Abstract: The present disclosure relates to apparatuses and methods that are useful for one or more aspects of a power production plant. More particularly, the disclosure relates to combustor apparatuses and methods for a combustor adapted to utilize different fuel mixtures derived from gasification of a solid fuel. Combustion of the different fuel mixtures within the combustor can be facilitated by arranging elements of the combustor controlled so that a defined set of combustion characteristics remains substantially constant across a range of different fuel mixtures.

Abstract: The present disclosure relates to apparatuses and methods that are useful for one or more aspects of a power production plant. More particularly, the disclosure relates to combustor apparatuses and methods for a combustor adapted to utilize different fuel mixtures derived from gasification of a solid fuel. Combustion of the different fuel mixtures within the combustor can be facilitated by arranging elements of the combustor controlled so that a defined set of combustion characteristics remains substantially constant across a range of different fuel mixtures.

Abstract: The present disclosure relates to apparatuses and methods that are useful for one or more aspects of a power production plant. More particularly, the disclosure relates to combustor apparatuses and methods for a combustor adapted to utilize different fuel mixtures derived from gasification of a solid fuel. Combustion of the different fuel mixtures within the combustor can be facilitated by arranging elements of the combustor controlled so that a defined set of combustion characteristics remains substantially constant across a range of different fuel mixtures.

Abstract: The present disclosure relates to apparatuses and methods that are useful for one or more aspects of a power production plant. More particularly, the disclosure relates to combustor apparatuses and methods for a combustor adapted to utilize different fuel mixtures derived from gasification of a solid fuel. Combustion of the different fuel mixtures within the combustor can be facilitated by arranging elements of the combustor controlled so that a defined set of combustion characteristics remains substantially constant across a range of different fuel mixtures.

Abstract: A method of optimizing a netlist for a circuit comprising identifying a logic tree with a single output and a plurality of interchangeable inputs, and calculate the optimal permutation of the plurality of inputs. The method further comprising modify the netlist based on the optimal permutation, and optimizing the modified netlist.

Abstract: The present disclosure relates to apparatuses and methods that are useful for one or more aspects of a power production plant. More particularly, the disclosure relates to combustor apparatuses and methods for a combustor adapted to utilize different fuel mixtures derived from gasification of a solid fuel. Combustion of the different fuel mixtures within the combustor can be facilitated by arranging elements of the combustor controlled so that a defined set of combustion characteristics remains substantially constant across a range of different fuel mixtures.

Abstract: A method of optimizing a netlist for a circuit comprising identifying a logic tree with a single output and a plurality of interchangeable inputs, and calculate the optimal permutation of the plurality of inputs. The method further comprising modify the netlist based on the optimal permutation, and optimizing the modified netlist.

Abstract: An automated design process using a computer system includes identifying a set of timing endpoints in a circuit defined by a machine-readable file. Values of slack in the estimated arrival times for the timing endpoints are assigned. Probability distribution functions, such as Gaussian distributions, are assigned for the respective values of slack, and are combined. The combination of probability distribution functions represents a measure of circuit performance. The measure is computed for alternative implementations of the circuit, and used to identify an alternative more likely to meet timing constraints.

Abstract: An automated design process using a computer system includes identifying a set of timing endpoints in a circuit defined by a machine-readable file. Values of slack in the estimated arrival times for the timing endpoints are assigned. Probability distribution functions, such as Gaussian distributions, are assigned for the respective values of slack, and are combined. The combination of probability distribution functions represents a measure of circuit performance. The measure is computed for alternative implementations of the circuit, and used to identify an alternative more likely to meet timing constraints.

Abstract: An automated design process using a computer system includes identifying a set of timing endpoints in a circuit defined by a machine-readable file. Values of slack in the estimated arrival times for the timing endpoints are assigned. Probability distribution functions, such as Gaussian distributions, are assigned for the respective values of slack, and are combined. The combination of probability distribution functions represents a measure of circuit performance. The measure is computed for alternative implementations of the circuit, and used to identify an alternative more likely to meet timing constraints.

Abstract: An automated design process using a computer system includes identifying a set of timing endpoints in a circuit defined by a machine-readable file. Values of slack in the estimated arrival times for the timing endpoints are assigned. Probability distribution functions, such as Gaussian distributions, are assigned for the respective values of slack, and are combined. The combination of probability distribution functions represents a measure of circuit performance. The measure is computed for alternative implementations of the circuit, and used to identify an alternative more likely to meet timing constraints.

Abstract: This invention provides a method for displaying circuit analysis results corresponding to parts of the circuit near the portion of the hardware description language (HDL) specification that generated that part of the circuit. The invention also includes a method for using probe statements in the HDL specification to mark additional points in the initial circuit that should not be eliminated during optimization. This improves the ability to display circuit analysis results near the appropriate part of the HDL specification.

Abstract: A digital circuit is synthesized from a text description of a digital system. During synthesis, a parse tree with parse nodes is constructed and retained. The relationship between the parse nodes and the circuit elements synthesized from those parse nodes is retained. Using that relationship, analysis results associated with circuit elements can be related to the text that generated those circuit elements. In particular, the analysis results can be used to set the display characteristics, such as font or size, of the text associated with those results.