The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is, shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorith...

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Partitioning sparse matrices with eigenvectors of graphs

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus- and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a real-world patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches.

/pdf/text-mining-techniques-for-patent-analysis-2ok026opgi.pdf

Text mining techniques for patent analysis

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

This survey highlights the recent advances in algorithms for numericallinear algebra that have come from the technique of linear sketching,whereby given a matrix, one first compresses it to a much smaller matrixby multiplying it by a (usually) random matrix with certain properties.Much of the expensive computation can then be performed onthe smaller matrix, thereby accelerating the solution for the originalproblem. In this survey we consider least squares as well as robust regressionproblems, low rank approximation, and graph sparsification.We also discuss a number of variants of these problems. Finally, wediscuss the limitations of sketching methods.

https://researcher.watson.ibm.com/researcher/files/us-dpwoodru/journal.pdf

Sketching as a Tool for Numerical Linear Algebra

Methods, apparatus and computer program products are provided for retrieving information from a text data collection and for classifying a document into none, one or more of a plurality of predefined classes. In each aspect, a representation of at least a portion of the original matrix is projected into a lower dimensional subspace and those portions of the subspace representation that relate to the term(s) of the query are weighted following the projection into the lower dimensional subspace. In order to retrieve the documents that are most relevant with respect to a query, the documents are then scored with documents having better scores being of generally greater relevance. Alternatively, in order to classify a document, the relationship of the document to the classes of documents is scored with the document then being classified in those classes, if any, that have the best scores.

Methods, apparatus and computer program products for information retrieval and document classification utilizing a multidimensional subspace

A text mining program is provided that allows a user to perform text mining operations, such as: information retrieval, term and document visualization, term and document clustering, term and document classification, summarization of individual documents and groups of documents, and document cross-referencing. This is accomplished by representing the text of a document collection using subspace transformations. This subspace transformation representation is performed by: constructing a term frequency matrix of the term frequencies for each of the documents, transforming the term frequencies for statistical purposes, and projecting the documents or the terms into a lower dimensional subspace. As the document collection is updated, the subspace is dynamically updated to reflect the new document collection.

Method and system for text mining using multidimensional subspaces

This study's objective was to identify clinical characteristics of patients with a blunt traumatic injury that increased the risk of peritoneal or pericardial fluid collections and abdominal organ injuries not identified by a bedside focused abdominal sonogram for trauma (FAST) examination. This observational study used a retrospective chart review of a cohort of patients identified through a query of the University of Nebraska Medical Center's trauma registry, a tertiary referral center for portions of Nebraska, Iowa, and Missouri. Adult patients presenting to the Emergency Department (ED) for an evaluation of blunt traumatic injury from September 1996 to December 2002 were eligible if their ED course included admission to the trauma service after completion of a bedside FAST examination (US) and a confirmatory study (Conf) such as an abdominopelvic computed tomography scan or exploratory laparotomy within 12 h of completion of the ED FAST examination. The medical records of those patients with a US+/Conf+ or US-/Conf+ examination were reviewed. Clinical characteristics were recorded on a standard data collection form. Statistically significant predictors of a US-/Conf+ examination were found using a stepwise logistic regression procedure. A query of the trauma registry for the study period revealed 1453 adult individuals with blunt abdominal trauma, with 458 patients meeting the inclusion criteria. The clinical characteristics of the 79 US+/Conf+ examinations were compared to those of the 53 US-/Conf+ examinations. The presence of a radiographically proven pelvic fracture (odds ratio 3.459; 95% confidence interval of 1.308-9.157) and a radiographically or operatively proven renal injury (odds ratio 3.667; 95% confidence interval of 1.013-13.275) were found to be significant predictors. The presence of a pelvic fracture or renal injury in adult victims of blunt abdominal trauma increases the likelihood of a US-/Conf+ examination. Patients with a negative FAST examination and pelvic fracture may benefit from additional radiographic or operative evaluations for occult injuries.

Clinical predictors of injuries not identified by focused abdominal sonogram for trauma (fast) examinations

Incremental condition estimation provides an estimate for the smallest singular value of a triangular matrix. In particular, it gives a running estimate of the smallest singular value of a triangular factor matrix as the factor is generated one column or row at a time. An incremental condition estimator for dense matrices was originally suggested by Bischof. In this paper this scheme is generalized to handle sparse triangular matrices, especially those that are factors of sparse matrices. Numerical experiments on a variety of matrices demonstrate the reliability of this scheme in estimating the smallest singular value. A partial description of its implementation in a sparse matrix factorization code further illustrates its practicality.

Incremental condition estimation for sparse matrices

A new algorithm for the computation of a pseudoperipheral node of a graph is presented, and the application of this algorithm to reordering algorithms for the solution of sparse linear systems is discussed. Numerical tests on large sparse matrix problems show the efficiency of the new algorithm. When used for some of the reordering algorithms for reducing the profile and bandwidth of a sparse matrix, the results obtained with the pseudoperipheral nodes of the new algorithm are comparable to the results obtained with the pseudoperipheral nodes produced by the SPARSPAK version of the Gibbs—Poole—Stockmeyer algorithm. The advantage of the new algorithm is that it accesses the adjacency structure of the sparse matrix in a regular pattern. Thus this algorithm is much more suitable both for a parallel and for an out-of-core implementation of the ordering phase for sparse matrix problems.

Daniel J. Pierce

Papers

Methods, apparatus and computer program products for information retrieval and document classification utilizing a multidimensional subspace

Method and system for text mining using multidimensional subspaces

Clinical predictors of injuries not identified by focused abdominal sonogram for trauma (fast) examinations

Incremental condition estimation for sparse matrices

A new algorithm for finding a pseudoperipheral node in a graph