Traditional analyses of agency output are typically performed at the institutional level, characterizing the agency in question as a unitary actor with a singular preference. I test these assumptions using a variety of statistical methods, including a dynamic linear model that estimates ideal points of FCC commissioners for every year, 1975-2000. Voting within the FCC is essentially unidimensional and commissioner preferences are stable over time. Aggregate analyses of the ideal points of individual commissioners suggest that FCC commissioner voting has become profoundly ideological only recently. Future agency research must carefully consider the time period of analysis and previous findings should be reexamined.

Temperature dependent magnetization measurements were conducted on Fe-based heterostructures. A linear increase of the magnetic critical temperature with increasing Fe thickness was found for Fe/V superlattices with strong interlayer exchange coupling. For weakly coupled Fe/V superlattices anomalous values of the critical exponent β were attributed to differences in the effective interlayer exchange coupling in the surface region and in the interior of the superlattice stack. Hydrogen loading of a sample containing a thin Fe film, up to a maximum pressure of 4 mbar gave an increase of the magnetic critical temperature of ≈21 K. A sample with a double layer of Fe, exchange coupled over V, showed oscillations in the critical temperature when loaded to increasing pressure of hydrogen. The oscillations in the critical temperature indicate the presence of quasi-2D phases. Superlattices of Fe and V were investigated by x-ray magnetic circular dichroism. It was found that the orbital magnetic moment shows the same trend as the magnetic anisotropy energy with thickness of the Fe layers. A model which takes into account a varying strain and interface density successfully described the changes in the orbital magnetic moment. The magnetization was measured as a function of temperature for a series of magnetically δ-doped Pd samples. A thin film of Fe induced a magnetic moment in surrounding Pd layers, leading to a magnetic thickness one order of magnitude larger than the thickness of the Fe film. A crossover in the magnetic spatial dimensionality was found as the thickness of the Fe film increased from ≈0.4 monolayers to ≈1 monolayer. First principle calculations of the magnetization profile together with a spin wave quantum well model were used to explain the dimensionality crossover by an increase in the available thermal energy for population of perpendicular spin wave modes.

A topic of research that is frequently studied in Structural Biology is the problem of determining the degree of similarity between two protein structures. The most common solution is to perform a three dimensional structural alignment on the two structures. Rigid structural alignment algorithms have been developed in the past to accomplish this but treat the protein molecules as immutable structures. Since protein structures can bend and flex, rigid algorithms do not yield accurate results and as a result, flexible structural alignment algorithms have been developed. The problem with these algorithms is that the protein structures are represented using thousands of atomic coordinate variables. This results in a great computational burden due to the large number of degrees of freedom required to account for the flexibility. Past research in dimensionality reduction techniques has shown that a linear dimensionality reduction technique called Principal Component Analysis (PCA) is well suited for high dimensionality reduction. This thesis introduces a new flexible structural alignment algorithm called FlexSADRA, which uses PCA to perform flexible structural alignments. Test results show that FlexSADRA determines better alignments than rigid structural alignment algorithms. Unlike existing rigid and flexible algorithms, FlexSADRA addresses the problem in a significantly lower dimensionality problem space and assesses not only the structural fit but the structural feasibility of the final alignment.

Prevalence of childhood asthma in Taiwan has increased 9 times from 1.3% to 10-14% in the past 4 decades. Many studies worldwide have demonstrated that many genes in different chromosomes are implicated in childhood asthma in different ethnic populations. A growing body of evidence suggests that allergic sensitization could occur in perinatal stage and correlate to the development of childhood asthma. Epidemiological studies, however, indicate that prevalence of childhood asthma is much higher in developed countries than that in developing countries; and prevalence of childhood asthma in metropolitan area is higher than that in country sites. This suggests that certain genes can interact with the environmental factors in developed countries to promote the development of childhood atopic disorders. Interests are now increasing on what is (are) the real pathogenic gene-gene interaction(s) for childhood atopic disorders under influence of age, gender and environmental factors? In a large perinatal cohort study with 1,211 pregnant women and their offspring from the obstetrics and pediatrics of Kaohsiung Chang Gung Memorial Hospital, we analyzed 159 allergy candidate genes with 384 single nucleotide polymorphisms and showed that 14 genes over 22 somatic and X chromosomes risk to or protective from cord blood immunoglobulin E (CBIgE) elevation are different from those genes associated with IgE elevation in children under 1.5, 3 and 6 years of age (12, 15 and 12 genes, respectively). CX3CL1, IL13, PDGFRA and FGF1 polymorphisms were associated with elevated IgE at earlier ages (newborn, 1.5 and 3 years); HLA-DPA1, HLA-DQA1, CCR5 and IL5RA polymorphisms were associated with IgE production at 6 years of age. Further analysis by multifactor dimensionality reduction (MDR) developed from data reduction strategy, we found that there are interactions among innate immunity, adaptive immunity, and response and remodeling genes on IgE production begin in prenatal stage. For example, The gene-gene interaction among IL13, rs1800925, CYFIP2, rs767007 and PDE2A, rs755933 was significantly associated with IgE production at 3 years of age. This suggests that different genotypes of genes interact one another on the IgE production contributing to the development of allergic diseases. Since the concentration of IgE is an important indicator of atopic disorders and allergic sensitization, we believe after clarifying the natural course of the genomic profiles on IgE elevation, certain early predictor(s) and preventive regimens for allergic sensitization or atopic disorders may be made possible.

With the evolution of Web 2.0, most social-networking sites let their members participate in content generation. Users can label items with tags in these websites. A tag can be anything but it is actually a short description of the item. Because tags represent the reason why a user likes an item, but not how much user likes it / they are better identifiers of user profiles than ratings, which are usually numerical values assigned to items by users. Thus, the tag-based contextual representations of music tracks are concentrated in this study. Items are generally represented by vector space models in the content based recommendation systems. In tag-based recommendation systems, users and items are defined in terms of weighted vectors of social tags. When there is a large amount of tags, calculation of the items to be recommended becomes hard, because working with huge vectors is a time-consuming job. The main objective of this thesis is to represent individual tracks (songs) in lower dimensional spaces. An approach is described for creating music recommendations based on user-supplied tags that are augmented with a hierarchical structure extracted for top level genres from Dbpedia. In this structure, each genre is represented by its stylistic origins, typical instruments, derivative forms, sub genres and fusion genres. In addition to very large vector space models, insufficient number of user tags is another problem in the recommendation field. The proposed method is evaluated with different user profiling methods in case of any insufficiency in the number of user tags. User profiles are extended with multi-domain information. By using multi-domain information, the goal of making more successful and realistic predictions is achieved.

This work considers the sampling design for detection problems.Firstly,we focus on studying the effect of signal shape on sampling design for Gaussian detection problem.We then investigate the sampling design for distributed detection problems and compare the performance with the single sensor context. We also propose a sampling design scheme for the cluster-based wireless sensor networks.The cluster head employs a linear combination fusion to reduce the dimension of the sampled observation.Mathematical verification and simulation result show that the performance loss caused by the dimensionality reduction is exceedingly small as compared with the benchmark scheme,which is the sampling scheme without dimensionality reduction.In particular,there is no performance loss when the identical sampling points are employed at all sensor nodes.

The National Research Council (NRC) outlines an assessment design framework in Knowing What Students Know. This framework proposes the integration of three components in assessment design that can be represented by a triangle, with each corner representing: cognition, or model of student learning in the domain; observation, or evidence of competencies; and interpretation, or making sense of this evidence. This triangle representation signifies the idea of a need for interconnectedness, consistency, and integrated development of the three elements, as opposed to having them as isolated from each other. Based on the recommendations for research outlined in the NRC's assessment report, this dissertation aims to conduct a dimensionality analysis of Programme for International Student Assessment (PISA) mathematics items. PISA assesses 15-year olds' skills and competencies in reading, math, and science literacy, implementing an assessment every three years since 2000. PISA's mathematics assessment framework, as proposed by the Organisation for Economic Co-operation and Development (OECD), has a multidimensional structure: content, processes, and context, each having three to four sub-dimensions. The goal of this dissertation is to show how and to what extent this complex multidimensional nature of assessment framework is reflected on the actual tests by investigating the dimensional structure of the PISA 2003, 2006, and 2009 mathematics items through the student responses from all participating OECD countries, and analyzing the correspondence between the mathematics framework and the actual items change over time through these three implementation cycles. Focusing on the cognition and interpretation components of the assessment triangle and the relationship between the two, the results provide evidence addressing construct validity of PISA mathematics assessment. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used for a dimensionality analysis of the PISA mathematics items in three different cycles: 2003, 2006, and 2009. Seven CFA models including a unidimensional model, three correlated factor (1-level) models, and three higher order factor (2-level) models were applied to the PISA mathematics items for each cycle. Although the results did not contradict the multidimensionality, stronger evidence was found to support the unidimensionality of the PISA mathematics items. The findings also showed that the dimensional structure of the PISA mathematics items were very stable across different cycles. / text

The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.