Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium.

Abstract: Embodiments are directed to a computer-based system for processing data of a sample. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive, from a sample analysis system, observed data of at least one element in the sample. The processor system is further configured to receive actual data of the at least one element, and identify error data of the observed data of the at least one element, wherein identifying the error data comprises running a simulation model that models the sample analysis system to identify properties of a relationship between the observed data of the at least one element in the sample and the actual data of the at least one element.

Abstract: Embodiments are directed to a computer-based system for processing data of a sample. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive, from a sample analysis system, observed data of at least one element in the sample. The processor system is further configured to receive actual data of the at least one element, and identify error data of the observed data of the at least one element, wherein identifying the error data comprises running a simulation model that models the sample analysis system to identify properties of a relationship between the observed data of the at least one element in the sample and the actual data of the at least one element.

Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium (LD).

Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium (LD).

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 features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices.

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. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices.

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 set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices.

Abstract: Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates.

Abstract: Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates.

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 provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates.

Abstract: Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates.

Abstract: A nanodevice includes a nanochannel disposed through a dielectric material. A first electrode is disposed on a first side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel. A second electrode is disposed on a second side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel opposite the first electrode. A power circuit is connected between the first and second electrodes to create a potential difference between the first and second electrodes such that portions of a molecule can be identified by a change in electrical properties across the first and second electrodes as the molecule passes.

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 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.