Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes program code. Includes bibliographical references (p. 94-96).

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [99]-106).

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains viii, 138 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 69-72).

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008. / Title from title screen (site viewed Oct. 31, 2008). PDF text: iii, 317 p. ; 2 Mb. UMI publication number: AAT 3307116. Includes bibliographical references. Also available in microfilm and microfiche formats.

Frankfurt (Main), Univ., Diss., 2004. / Erscheinungsjahr an der Haupttitelstelle: 2003. Computerdatei im Fernzugriff.

Universiẗat, Diss., 2002--Stuttgart.

Doctor of Philosophy / Security Studies Interdepartmental Program / Andrew G. Long / David R. Stone / This dissertation research examines the effect of organizational lethality on the longevity of terrorist groups. The current scholarship has sought to understand the demise of terrorist groups through means such as group success, government repression, negotiations, internal conflict, reorientation of goals, defeat, leadership decapitation and loss of public support. However, little research is available on the determinants of terrorists’ target selection and its implications for the group’s longevity. This study evaluates the targeting patterns and preferences of 480 terrorist groups that were operational between 1980 and 2011 and disaggre- gates the victims of all terrorist attacks into combatant versus non-combatant target-types. It is hypothesized that organizational lethality – defined as the average number of civilian killings generated by each group in its home-base country – is associated with negative group reputation, which results in faster group mortality. Popular support for violence, however, can influence and result from terrorism at the same time and has been found to be inherently endogenous by many previous studies. Therefore, a Seemingly Unrelated Bivariate Probit Model is employed to examine this endogenous relationship, and the results confirm that there is a significant correlation between negative group reputation and group mortality. Moreover, the study differentiates between terrorist group activity – defined as average at- tacks generated by a group – and group lethality, and employs the Cox Proportional Hazard Model to estimate group duration. The study includes covariates like group size, ideology, positive consistency reputation and other factors affecting group longevity and mortality. The results imply that organizational lethality is associated with higher political risks for terrorist groups and tends to backfire by decreasing their survival probability. However, on the other hand, the study finds that an escalation in terrorist activity (launching more attacks) significantly increase the group longevity over time. The results of this study are tested by conducting group-specific case studies on the Afghan and Pakistani Taliban in Pak- istan using information collected from the English language Pakistani newspaper archives, and Harmony Database from Combat Terrorism Center at West Point, NY.

No description available.

This dissertation develops new approaches for improving the performance of hyperspectral target detection. Different aspects of hyperspectral target detection are reviewed and studied to effectively distinguish target features from background interference. The contributions of this dissertation are detailed as follows. 1) Propose an adaptive background characterization method that integrates region segmentation with target detection. In the experiments, not only unstructured matched filter based detectors are considered, but also two hybrid detectors combining fully constrained least squared abundance estimation with statistic test (i.e., adaptive matched subspace detector and adaptive cosine/coherent detector) are investigated. The experimental results demonstrate that using local adaptive background characterization, background clutters can be better suppressed than the original algorithms with global characterization. 2) Propose a new approach to estimate abundance fractions based on the linear spectral mixture model for hybrid structured and unstructured detectors. The new approach utilizes the sparseness constraint to estimate abundance fractions, and achieves better performance than the popular non-negative and fully constrained methods in the situations when background endmember spectra are not accurately acquired or estimated, which is very common in practical applications. To improve the dictionary incoherence, the use of band selection is proposed to improve the sparseness constrained linear unmixing. 3) Propose random projection based dimensionality reduction and decision fusion approach for detection improvement. Such a data independent dimensionality reduction process has very low computational cost, and it is capable of preserving the original data structure. Target detection can be robustly improved by decision fusion of multiple runs of random projection. A graphics processing unit (GPU) parallel implementation scheme is developed to expedite the overall process. 4) Propose nonlinear dimensionality reduction approaches for target detection. Auto-associative neural network-based Nonlinear Principal Component Analysis (NLPCA) and Kernel Principal Component Analysis (KPCA) are applied to the original data to extract principal components as features for target detection. The results show that NLPCA and KPCA can efficiently suppress trivial spectral variations, and perform better than the traditional linear version of PCA in target detection. Their performance may be even better than the directly kernelized detectors.

Mouse PEA3 is the founding member of the PEA3 subfamily of ETS transcription factors that includes ERM and ER81. Numerous studies implicate PEA3 subfamily members in a diversity of human cancers, especially breast cancer. Dominant-negative PEA3 (L1NPEA3En) effectively represses activated transcription by all three PEA3 subfamily members. When expressed under control of the MMTV promoter, L1NPEA3En significantly delays the appearance of mammary tumors and reduces their number and size in mouse models of HER2 mediated breast cancer. In addition, L1NPEA3En is not expressed in the mammary tumors that do develop in these mice. These findings strongly suggest a required role for PEA3 subfamily members or other ETS proteins with similar DNA binding specificity in HER2-mediated oncogenesis. The primary objective of this research was to identify the PEA3 subfamily target genes that could play a role in the initiation and progression of tumors, specifically in the breast. To achieve this, a recombinant adenovirus carrying L1NPEA3En was constructed to express L1NPEA3En in three human mammary tumor cell lines: MDA-MB-468, BT-549 and MDA-MB-361. Gene expression analysis using Affymetrix® GeneChip® technology identified a common set of 39 downregulated and 2 upregulated genes in cells expressing L1NPEA3En compared to control cells in all three tumor cell lines. Differentially expressed genes included some that have been shown to play key roles in tumorigenesis such as activating transcriptionfactor 3, heat shock 70kD protein lA and interleukin-8. In addition one colon carcinoma cell line, SW620, was used for gene expression analysis and 7 genes identified in the mammary tumor cell lines were also identified in the colon carcinoma cell line. The results suggest a role for PEA3 subfamily genes in a multiple human cancers mediated through a small subset of common target genes. The genes identified as being differentially expressed by ~NPEA3En hold potential value not only as targets for therapeautic drug discovery, but also as diagnostic or prognostic markers for human cancers, specifically breast cancer. / Thesis / Master of Biological Science (MBioSci)