Abstract: A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided.

Abstract: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

Abstract: A method is described for automatically correcting metadata errors in a k-mer database. A k-mer database having a self-consistent taxonomy based on genome-genome distance was constructed from a set of sample and reference genomes whose metadata included taxonomic labeling from a reference taxonomy (the standard NCBI taxonomy), which is not based on genetic distance. As a result, genomes of a given taxonomic ID of the self-consistent taxonomy could be separated into clusters based on the differences in the metadata. Genomes of the clusters less than a minimum cluster size Cmin were removed and profiled against the remaining genomes, correcting metadata automatically for those genomes that could be mapped back. The resulting k-mer database showed improved specificity for genetic profiling. Another method is described for identifying and handling chimeric genomes using the self-consistent taxonomy. Another method is described for correcting a classification database.

Abstract: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

Abstract: A lower-dimensional representation (e.g., approximation) of a dataset is determined. The lower-dimensional representation can be used, for example, to perform semantic document analysis. Given a matrix of input data points, where each entry of the matrix indicates a number of times a particular term in a set of terms appears in a particular document in a set of documents, a lower-dimensional compressed matrix is obtained from the matrix by sampling rows of the matrix based on a target rank parameter, a desired accuracy tolerance, leverage scores calculated for the rows, and/or distances from rows of the matrix to a span of the initial set of sampled rows. The compressed matrix is used to determine a similarity metric indicative of a degree of similarity between documents. The documents can then be classified into a same document cluster or different clusters based on whether the similarity metric satisfied a threshold value.

Abstract: A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided.

Abstract: One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Abstract: A method is described for automatically correcting metadata errors in a k-mer database. A k-mer database having a self-consistent taxonomy based on genome-genome distance was constructed from a set of sample and reference genomes whose metadata included taxonomic labeling from a reference taxonomy (the standard NCBI taxonomy), which is not based on genetic distance. As a result, genomes of a given taxonomic ID of the self-consistent taxonomy could be separated into clusters based on the differences in the metadata. Genomes of the clusters less than a minimum cluster size Cmin were removed and profiled against the remaining genomes, correcting metadata automatically for those genomes that could be mapped back. The resulting k-mer database showed improved specificity for genetic profiling. Another method is described for identifying and handling chimeric genomes using the self-consistent taxonomy. Another method is described for correcting a classification database.

Abstract: A lower-dimensional representation (e.g., approximation) of a dataset is determined. The lower-dimensional representation can be used, for example, to perform semantic document analysis. Given a matrix of input data points, where each entry of the matrix indicates a number of times a particular term in a set of terms appears in a particular document in a set of documents, a lower-dimensional compressed matrix is obtained from the matrix by sampling rows of the matrix based on a target rank parameter, a desired accuracy tolerance, leverage scores calculated for the rows, and/or distances from rows of the matrix to a span of the initial set of sampled rows. The compressed matrix is used to determine a similarity metric indicative of a degree of similarity between documents. The documents can then be classified into a same document cluster or different clusters based on whether the similarity metric satisfied a threshold value.

Abstract: A lower-dimensional representation (e.g., approximation) of a dataset is determined. The lower-dimensional representation can be used, for example, to perform semantic document analysis. Given a matrix of input data points, where each entry of the matrix indicates a number of times a particular term in a set of terms appears in a particular document in a set of documents, a lower-dimensional compressed matrix is obtained from the matrix by sampling rows of the matrix based on a target rank parameter, a desired accuracy tolerance, leverage scores calculated for the rows, and/or distances from rows of the matrix to a span of the initial set of sampled rows. The compressed matrix is used to determine a similarity metric indicative of a degree of similarity between documents. The documents can then be classified into a same document cluster or different clusters based on whether the similarity metric satisfied a threshold value.

Abstract: One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Abstract: A system for retrieving stored data includes memory and a processor. The memory stores a first matrix, A, having dimensions n×d, a first sparse matrix, R, and a second sparse matrix, S. The processor receives an input value, k, corresponding to a selected rank to generate a second matrix, AR, by multiplying the first matrix, A, by the first sparse matrix, R. The second matrix, AT, has dimensions n×t. The processor generates a third matrix, SA, by multiplying the second sparse matrix, S, by the first matrix, A. The third matrix, SA, has dimensions t?×n, and the processor generates a fourth matrix, (SAR)?, by calculating a Moore-Penrose pseudo-inverse of a matrix, (SAR), and approximating the first matrix, A by generating a fifth matrix, Ã, the fifth matrix defined as AR×(SAR)?×SA.

Abstract: A lower-dimensional representation (e.g., approximation) of a dataset is determined. The lower-dimensional representation can be used, for example, to perform semantic document analysis. Given a matrix of input data points, where each entry of the matrix indicates a number of times a particular term in a set of terms appears in a particular document in a set of documents, a lower-dimensional compressed matrix is obtained from the matrix by sampling rows of the matrix based on a target rank parameter, a desired accuracy tolerance, leverage scores calculated for the rows, and/or distances from rows of the matrix to a span of the initial set of sampled rows. The compressed matrix is used to determine a similarity metric indicative of a degree of similarity between documents. The documents can then be classified into a same document cluster or different clusters based on whether the similarity metric satisfied a threshold value.

Abstract: Embodiments of the invention relate to sketching for M-estimators for performing regression. One embodiment includes providing one or more sets of input data. A matrix A and a vector b are generated using the input data. A processor device is used for processing the matrix A and the vector b based on a randomized sketching matrix S. A vector x that minimizes a normalized measure function is determined based on the matrix A and the vector b. A relationship between the input data is determined based on the vector x.

Abstract: Embodiments of the present invention relate to a pattern-based system for building dictionaries of terms related to a seed set of terms. In one embodiment, a text is read. The text comprises a plurality of tokens. A first plurality of patterns is read. The first plurality of tokens is searched using the first plurality of patterns to generate a plurality of candidate terms. Each of the plurality of candidate term comprises one or more of the plurality of tokens. A plurality of seed terms is read. Each of the first plurality of patterns is scored based on the plurality of candidate terms and the plurality of seed terms.

Abstract: Embodiments of the present invention relate to a pattern-based system for building dictionaries of terms related to a seed set of terms. In one embodiment, a text is read. The text comprises a plurality of tokens. A first plurality of patterns is read. The first plurality of tokens is searched using the first plurality of patterns to generate a plurality of candidate terms. Each of the plurality of candidate term comprises one or more of the plurality of tokens. A plurality of seed terms is read. Each of the first plurality of patterns is scored based on the plurality of candidate terms and the plurality of seed terms.

Abstract: Embodiments of the invention relate to sketching for M-estimators for performing regression. One embodiment includes providing one or more sets of input data. A matrix A and a vector b are generated using the input data. A processor device is used for processing the matrix A and the vector b based on a randomized sketching matrix S. A vector x that minimizes a normalized measure function is determined based on the matrix A and the vector b. A relationship between the input data is determined based on the vector x.

Abstract: A system for retrieving stored data includes memory and a processor. The memory stores a first matrix, A, having dimensions n×d, a first sparse matrix, R, and a second sparse matrix, S. The processor receives an input value, k, corresponding to a selected rank to generate a second matrix, RA, by multiplying the first matrix, A, by the first sparse matrix, R. The second matrix, RA, has dimensions n×t. The processor generates a third matrix, AST, by multiplying the first matrix, A, by the second sparse matrix, S, transposed. The third matrix, AST, has dimensions d×t?, and the processor generates a fourth matrix, (SART)?, by calculating a Moore-Penrose pseudo-inverse of a matrix, (SART), and approximating the first matrix, A by generating a fifth matrix, Â, the fifth matrix defined as AST×(SART)?×RA.

Abstract: Embodiments of the invention include method of approximating a matrix of data using sparse matrices which includes receiving a first matrix and generating a second matrix based on the first matrix and a first sparse matrix. The method further includes generating a third matrix based on the first matrix and a second sparse matrix and generating a fourth matrix by generating a Moore-Penrose pseudo-inverse matrix based on the first matrix, the second matrix and the third matrix. The method also includes generating a fifth matrix based on a product of the second matrix, the third matrix, and a fourth matrix. The method further includes receiving, by a computer, a request to access at least one entry of the first matrix and responding to the request by accessing an entry of the fifth matrix.

Abstract: A method, system and program product for computer information retrieval is disclosed. A matrix A is received. Random sign matrices S and R are generated. Matrix products of S^T*A, A*R, and S^T*A*R are computed. A Moore-Penrose pseudoinverse C of S^T*A*R is computed. A singular value decomposition is computed of the pseudoinverse C. Three matrices ARU, Sigma, and V^TS^TA are outputted as factorization in applications.