Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembled the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, and model requirements are satis ed as a post-processing step.

Abstract not provided.

Abstract not provided.

Early 2010 saw a signi cant change in adversarial techniques aimed at network intrusion: a shift from malware delivered via email attachments toward the use of hidden, embedded hyperlinks to initiate sequences of downloads and interactions with web sites and network servers containing malicious software. Enterprise security groups were well poised and experienced in defending the former attacks, but the new types of attacks were larger in number, more challenging to detect, dynamic in nature, and required the development of new technologies and analytic capabilities. The Hybrid LDRD project was aimed at delivering new capabilities in large-scale data modeling and analysis to enterprise security operators and analysts and understanding the challenges of detection and prevention of emerging cybersecurity threats. Leveraging previous LDRD research e orts and capabilities in large-scale relational data analysis, large-scale discrete data analysis and visualization, and streaming data analysis, new modeling and analysis capabilities were quickly brought to bear on the problems in email phishing and spear phishing attacks in the Sandia enterprise security operational groups at the onset of the Hybrid project. As part of this project, a software development and deployment framework was created within the security analyst work ow tool sets to facilitate the delivery and testing of new capabilities as they became available, and machine learning algorithms were developed to address the challenge of dynamic threats. Furthermore, researchers from the Hybrid project were embedded in the security analyst groups for almost a full year, engaged in daily operational activities and routines, creating an atmosphere of trust and collaboration between the researchers and security personnel. The Hybrid project has altered the way that research ideas can be incorporated into the production environments of Sandias enterprise security groups, reducing time to deployment from months and years to hours and days for the application of new modeling and analysis capabilities to emerging threats. The development and deployment framework has been generalized into the Hybrid Framework and incor- porated into several LDRD, WFO, and DOE/CSL projects and proposals. And most importantly, the Hybrid project has provided Sandia security analysts with new, scalable, extensible analytic capabilities that have resulted in alerts not detectable using their previous work ow tool sets.

This SAND report summarizes the activities and outcomes of the Network and Ensemble Enabled Entity Extraction in Information Text (NEEEEIT) LDRD project, which addressed improving the accuracy of conditional random fields for named entity recognition through the use of ensemble methods.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Recent work on eigenvalues and eigenvectors for tensors of order m {>=} 3 has been motivated by applications in blind source separation, magnetic resonance imaging, molecular conformation, and more. In this paper, we consider methods for computing real symmetric-tensor eigenpairs of the form Ax{sup m-1} = {lambda}x subject to {parallel}x{parallel} = 1, which is closely related to optimal rank-1 approximation of a symmetric tensor. Our contribution is a novel shifted symmetric higher-order power method (SS-HOPM), which we showis guaranteed to converge to a tensor eigenpair. SS-HOPM can be viewed as a generalization of the power iteration method for matrices or of the symmetric higher-order power method. Additionally, using fixed point analysis, we can characterize exactly which eigenpairs can and cannot be found by the method. Numerical examples are presented, including examples from an extension of the method to fnding complex eigenpairs.

This report is a summary of the accomplishments of the 'Scalable Solutions for Processing and Searching Very Large Document Collections' LDRD, which ran from FY08 through FY10. Our goal was to investigate scalable text analysis; specifically, methods for information retrieval and visualization that could scale to extremely large document collections. Towards that end, we designed, implemented, and demonstrated a scalable framework for text analysis - ParaText - as a major project deliverable. Further, we demonstrated the benefits of using visual analysis in text analysis algorithm development, improved performance of heterogeneous ensemble models in data classification problems, and the advantages of information theoretic methods in user analysis and interpretation in cross language information retrieval. The project involved 5 members of the technical staff and 3 summer interns (including one who worked two summers). It resulted in a total of 14 publications, 3 new software libraries (2 open source and 1 internal to Sandia), several new end-user software applications, and over 20 presentations. Several follow-on projects have already begun or will start in FY11, with additional projects currently in proposal.

This report is a summary of the accomplishments of the 'Leveraging Multi-way Linkages on Heterogeneous Data' which ran from FY08 through FY10. The goal was to investigate scalable and robust methods for multi-way data analysis. We developed a new optimization-based method called CPOPT for fitting a particular type of tensor factorization to data; CPOPT was compared against existing methods and found to be more accurate than any faster method and faster than any equally accurate method. We extended this method to computing tensor factorizations for problems with incomplete data; our results show that you can recover scientifically meaningfully factorizations with large amounts of missing data (50% or more). The project has involved 5 members of the technical staff, 2 postdocs, and 1 summer intern. It has resulted in a total of 13 publications, 2 software releases, and over 30 presentations. Several follow-on projects have already begun, with more potential projects in development.

Automated analysis of unstructured text documents (e.g., web pages, newswire articles, research publications, business reports) is a key capability for solving important problems in areas including decision making, risk assessment, social network analysis, intelligence analysis, scholarly research and others. However, as data sizes continue to grow in these areas, scalable processing, modeling, and semantic analysis of text collections becomes essential. In this paper, we present the ParaText text analysis engine, a distributed memory software framework for processing, modeling, and analyzing collections of unstructured text documents. Results on several document collections using hundreds of processors are presented to illustrate the exibility, extensibility, and scalability of the the entire process of text modeling from raw data ingestion to application analysis.