Abstract: Mechanisms are provided for performing machine learning training of a computer model. A perturbation generator generates a modified training data comprising perturbations injected into original training data, where the perturbations cause a data corruption of the original training data. The modified training data is input into a prediction network of the computer model and processing the modified training data through the prediction network to generate a prediction output. Machine learning training is executed of the prediction network based on the prediction output and the original training data to generate a trained prediction network of a trained computer model. The trained computer model is deployed to an artificial intelligence computing system for performance of an inference operation.

Abstract: A graphical user interface and a range slider are provided. Each of a first and second slider thumb is positioned at a respective location along a slider track, and associated with a corresponding value along the slider track. User interaction is detected and identified to include at least one of a tap or a drag. A hardware processor responds to the user interaction by processing a value representing the tap or the drag to determine a location along the slider track of the user interaction. The processor identifies the interaction as being with one of the first and second slider thumbs, and repositions the identified one slider thumb to the determined location along the slider track. In addition, a graphical selection overlay is on the selected slider thumb, and by determining a slider track value associated with the repositioned location of the selected one slider thumb along the slider track.

Abstract: Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Computer-implemented systems and methods are provided for analyzing and determining properties of virtual environments rendered on a display. The disclosed embodiments include, for example, a method for rendering a virtual environment, the method comprising operations performed with one or more processors. The operations of the method may include generating a plurality of object layers, the object layers representing permissible height values. The method may also include populating the environment with a plurality of objects, wherein each object is associated with a height value corresponding to one of the object layers. The method may also include determining whether any two objects form an occluded pair. The method may also include calculating a cast shadow index for each occluded pair reflecting a magnitude of a height differential between occluding object and the occluded object. The method may also include rendering the virtual environment in accordance with the calculated cast shadow indices.

Abstract: Computer-implemented systems and methods are provided for analyzing and determining properties of virtual environments rendered on a display. The disclosed embodiments include, for example, a method for obtaining, by one or more processors, one or more depth parameters comprising one or more display parameters reflecting characteristics of the display, wherein the display parameters include a height and width of the display, and one or more environment depth multipliers reflecting a scaling factor to optimize display performance. The method may also include calculating, by the one or more processors, a diagonal display distance based on the display parameters. The method may also include calculating, by the one or more processors, an environment depth based on the diagonal display distance and the one or more environment depth multipliers. The method may also include setting, by the one or more processors, the depth of the display equal to the environment depth.

Abstract: Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Abstract: Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Abstract: Computer-implemented systems and methods are provided for analyzing and determining properties of virtual environments rendered on a display. The disclosed embodiments include, for example, a method for rendering a virtual environment, the method comprising operations performed with one or more processors. The operations of the method may include generating a plurality of object layers, the object layers representing permissible height values. The method may also include populating the environment with a plurality of objects, wherein each object is associated with a height value corresponding to one of the object layers. The method may also include determining whether any two objects form an occluded pair. The method may also include calculating a cast shadow index for each occluded pair reflecting a magnitude of a height differential between occluding object and the occluded object. The method may also include rendering the virtual environment in accordance with the calculated cast shadow indices.

Abstract: Computer-implemented systems and methods are provided for analyzing and determining properties of virtual environments rendered on a display. The disclosed embodiments include, for example, a method for obtaining, by one or more processors, one or more depth parameters comprising one or more display parameters reflecting characteristics of the display, wherein the display parameters include a height and width of the display, and one or more environment depth multipliers reflecting a scaling factor to optimize display performance. The method may also include calculating, by the one or more processors, a diagonal display distance based on the display parameters. The method may also include calculating, by the one or more processors, an environment depth based on the diagonal display distance and the one or more environment depth multipliers. The method may also include setting, by the one or more processors, the depth of the display equal to the environment depth.

Abstract: Various embodiments sort data. In one embodiment, a matrix D including a set of data values is received. A matrix Q is received, and includes a set of columns and a set of rows. The matrix Q further includes a sorting of each column of the matrix D. Each of these rows corresponds to a sorting. Each of a set of values in each of the set of columns in the matrix Q identifies a row in the matrix D. At least one sub-matrix D? of the matrix D is identified. A set of columns of the sub-matrix D? is restricted to one or more columns of the matrix D. A processor sorts the sub-matrix D? by rows based on the sorting of the set of columns of the matrix D as given in the matrix Q, and based on the set of data values in the matrix D.

Abstract: Various embodiments sort data. In one embodiment, a matrix D including a set of data values is received. A matrix Q is received, and includes a set of columns and a set of rows. The matrix Q further includes a sorting of each column of the matrix D. Each of these rows corresponds to a sorting. Each of a set of values in each of the set of columns in the matrix Q identifies a row in the matrix D. At least one sub-matrix D? of the matrix D is identified. A set of columns of the sub-matrix D? is restricted to one or more columns of the matrix D. A processor sorts the sub-matrix D? by rows based on the sorting of the set of columns of the matrix D as given in the matrix Q, and based on the set of data values in the matrix D.

Abstract: Various embodiments select features from a feature space. In one embodiment a candidate feature set of k? features is selected from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria. A target feature set of k features is identified from the candidate feature set, where k?>k. Each a plurality of features in the target feature set is iteratively updated with each of a plurality of k??k features from the candidate feature set. The feature from the plurality of k??k features is maintained in the target feature set, for at least one iterative update, based on a current MRMR score of the target feature set satisfying a threshold. The target feature set is stored as a top-k feature set of the at least one set of features after a given number of iterative updates.

Abstract: Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Abstract: Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features.

Abstract: Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold.

Abstract: Various embodiments select features from a feature space. In one embodiment a candidate feature set of k? features is selected from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria. A target feature set of k features is identified from the candidate feature set, where k?>k. Each a plurality of features in the target feature set is iteratively updated with each of a plurality of k??k features from the candidate feature set. The feature from the plurality of k??k features is maintained in the target feature set, for at least one iterative update, based on a current MRMR score of the target feature set satisfying a threshold. The target feature set is stored as a top-k feature set of the at least one set of features after a given number of iterative updates.

Abstract: Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected.

Abstract: Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected.