Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectrometry provide huge amounts of data per measurement and challenge traditional analyses. New strategies of data processing, visualization and functional analysis are inevitable. This thesis presents an approach which applies a machine learning technique known as self organizing maps (SOMs). SOMs enable the parallel sample- and feature-centered view of molecular phenotypes combined with strong visualization and second-level analysis capabilities. We developed a comprehensive analysis and visualization pipeline based on SOMs. The unsupervised SOM mapping projects the initially high number of features, such as gene expression profiles, to meta-feature clusters of similar and hence potentially co-regulated single features. This reduction of dimension is attained by the re-weighting of primary information and does not entail a loss of primary information in contrast to simple filtering approaches. The meta-data provided by the SOM algorithm is visualized in terms of intuitive mosaic portraits. Sample-specific and common properties shared between samples emerge as a handful of localized spots in the portraits collecting groups of co-regulated and co-expressed meta-features. This characteristic color patterns reflect the data landscape of each sample and promote immediate identification of (meta-)features of interest. It will be demonstrated that SOM portraits transform large and heterogeneous sets of molecular biological data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. Spot-clusters of correlated meta-features can be extracted from the SOM portraits in a subsequent step of aggregation. This spot-clustering effectively enables reduction of the dimensionality of the data in two subsequent steps towards a handful of signature modules in an unsupervised fashion. Furthermore we demonstrate that analysis techniques provide enhanced resolution if applied to the meta-features. The improved discrimination power of meta-features in downstream analyses such as hierarchical clustering, independent component analysis or pairwise correlation analysis is ascribed to essentially two facts: Firstly, the set of meta-features better represents the diversity of patterns and modes inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single features. Additionally to the pattern-driven feature selection in the SOM portraits, we apply statistical measures to detect significantly differential features between sample classes. Implementation of scoring measurements supplements the basal SOM algorithm. Further, two variants of functional enrichment analyses are introduced which link sample specific patterns of the meta-feature landscape with biological knowledge and support functional interpretation of the data based on the ‘guilt by association’ principle. Finally, case studies selected from different ‘OMIC’ realms are presented in this thesis. In particular, molecular phenotype data derived from expression microarrays (mRNA, miRNA), sequencing (DNA methylation, histone modification patterns) or mass spectrometry (proteome), and also genotype data (SNP-microarrays) is analyzed. It is shown that the SOM analysis pipeline implies strong application capabilities and covers a broad range of potential purposes ranging from time series and treatment-vs.-control experiments to discrimination of samples according to genotypic, phenotypic or taxonomic classifications.

In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adapts its shape according to the input data. It is capable of growing dynamically in terms of the size of the individual neuron layers to represent data at the desired granularity as well as in depth to model the hierarchical relations in the data. However, the training of the GHSOM requires multiple passes over an input dataset. This makes it difficult to use the GHSOM for massive datasets. In this thesis work, we propose a Map-Reduce variant of the GHSOM called MR-GHSOM, which is capable of processing massive datasets. The MR-GHSOM is implemented using the Apache Spark cluster computing engine and leverages the popular Map-Reduce programming model. This enables us to exploit the usefulness and dynamic capabilities of the GHSOM even for a large dataset. Moreover, the conventional GHSOM algorithm can handle datasets with numeric attributes only. This is owing to the fact that it relies heavily on the Euclidean space dissimilarity measures of the attribute vectors. The MR-GHSOM further extends the GHSOM to handle mixed attribute - numeric and categorical - datasets. It accomplishes this by adopting the distance hierarchy approach of managing mixed attribute datasets. The proposed MR-GHSOM is thus capable of handling massive datasets containing mixed attributes. To demonstrate the effectiveness of the MR-GHSOM in terms of clustering of mixed attribute datasets, we present the results produced by the MR-GHSOM on some popular datasets. We further train our MR-GHSOM on a Census dataset containing mixed attributes and provide an analysis of the results.

We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user. The different sized SOMs are analyzed in terms of how the size changes the representation of specific features. The results confirm that the larger the SOM is chosen the larger will be the root mean square error (RMSE) for the given SOM, which is followed by the fact that a larger number of patterns can reproduce more specific features for the temperature. / Wir benutzten das künstliche neuronale Netzwerk Self Organizing-Map (SOM), um eine Musteranalyse von ERA-Interim Reanalysedaten durchzuführen. Es wurden SOMs mit verschiedener Musteranzahl verglichen. Die Ergebnisse zeigen, dass SOMs mit einer größeren Musteranzahl deutlich spezifischere Muster produzieren im Vergleich zu SOMs mit geringen Musteranzahlen. Dies zeigt sich unter anderem in der Betrachtung der mittleren quadratischen Abweichung (RMSE) der Muster zu den zugeordneten ERA Daten.

by John Pui-fai Sum. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 99-104). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Idea of SSOM --- p.3 / Chapter 1.3 --- Other Approaches --- p.3 / Chapter 1.4 --- Contribution of the Thesis --- p.4 / Chapter 1.5 --- Outline of Thesis --- p.5 / Chapter 2 --- Self-Organizing Map --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Algorithm of SOM --- p.8 / Chapter 2.3 --- Illustrative Example --- p.10 / Chapter 2.4 --- Property of SOM --- p.14 / Chapter 2.4.1 --- Convergence property --- p.14 / Chapter 2.4.2 --- Topological Order --- p.15 / Chapter 2.4.3 --- Objective Function of SOM --- p.15 / Chapter 2.5 --- Conclusion --- p.17 / Chapter 3 --- Algorithms for Soft Self-Organizing Map --- p.18 / Chapter 3.1 --- Competitive Learning and Soft Competitive Learning --- p.19 / Chapter 3.2 --- How does SOM generate ordered map? --- p.21 / Chapter 3.3 --- Algorithms of Soft SOM --- p.23 / Chapter 3.4 --- Simulation Results --- p.25 / Chapter 3.4.1 --- One dimensional map under uniform distribution --- p.25 / Chapter 3.4.2 --- One dimensional map under Gaussian distribution --- p.27 / Chapter 3.4.3 --- Two dimensional map in a unit square --- p.28 / Chapter 3.5 --- Conclusion --- p.30 / Chapter 4 --- Application to Uncover Vowel Relationship --- p.31 / Chapter 4.1 --- Experiment Set Up --- p.32 / Chapter 4.1.1 --- Network structure --- p.32 / Chapter 4.1.2 --- Training procedure --- p.32 / Chapter 4.1.3 --- Relationship Construction Scheme --- p.34 / Chapter 4.2 --- Results --- p.34 / Chapter 4.2.1 --- Hidden-unit labeling for SSOM2 --- p.34 / Chapter 4.2.2 --- Hidden-unit labeling for SOM --- p.35 / Chapter 4.3 --- Conclusion --- p.37 / Chapter 5 --- Application to vowel data transmission --- p.42 / Chapter 5.1 --- Introduction --- p.42 / Chapter 5.2 --- Simulation --- p.45 / Chapter 5.2.1 --- Setup --- p.45 / Chapter 5.2.2 --- Noise model and demodulation scheme --- p.46 / Chapter 5.2.3 --- Performance index --- p.46 / Chapter 5.2.4 --- Control experiment: random coding scheme --- p.46 / Chapter 5.3 --- Results --- p.47 / Chapter 5.3.1 --- Null channel noise (σ = 0) --- p.47 / Chapter 5.3.2 --- Small channel noise (0 ≤ σ ≤1) --- p.49 / Chapter 5.3.3 --- Large channel noise (1 ≤σ ≤7) --- p.49 / Chapter 5.3.4 --- Very large channel noise (σ > 7) --- p.49 / Chapter 5.4 --- Conclusion --- p.50 / Chapter 6 --- Convergence Analysis --- p.53 / Chapter 6.1 --- Kushner and Clark Lemma --- p.53 / Chapter 6.2 --- Condition for the Convergence of Jou's Algorithm --- p.54 / Chapter 6.3 --- Alternative Proof on the Convergence of Competitive Learning --- p.56 / Chapter 6.4 --- Convergence of Soft SOM --- p.58 / Chapter 6.5 --- Convergence of SOM --- p.60 / Chapter 7 --- Conclusion --- p.61 / Chapter 7.1 --- Limitations of SSOM --- p.62 / Chapter 7.2 --- Further Research --- p.63 / Chapter A --- Proof of Corollary1 --- p.65 / Chapter A.l --- Mean Average Update --- p.66 / Chapter A.2 --- Case 1: Uniform Distribution --- p.68 / Chapter A.3 --- Case 2: Logconcave Distribution --- p.70 / Chapter A.4 --- Case 3: Loglinear Distribution --- p.72 / Chapter B --- Different Senses of neighborhood --- p.79 / Chapter B.l --- Static neighborhood: Kohonen's sense --- p.79 / Chapter B.2 --- Dynamic neighborhood --- p.80 / Chapter B.2.1 --- Mou-Yeung Definition --- p.80 / Chapter B.2.2 --- Martinetz et al. Definition --- p.81 / Chapter B.2.3 --- Tsao-Bezdek-Pal Definition --- p.81 / Chapter B.3 --- Example --- p.82 / Chapter B.4 --- Discussion --- p.84 / Chapter C --- Supplementary to Chapter4 --- p.86 / Chapter D --- Quadrature Amplitude Modulation --- p.92 / Chapter D.l --- Amplitude Modulation --- p.92 / Chapter D.2 --- QAM --- p.93 / Bibliography --- p.99

Wong, Wing Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 76-79). / Abstracts in English and Chinese. / Contents --- p.i / Acknowledgments --- p.iii / Abstract --- p.iv / Abbreviations and Acronyms --- p.vii / Publications Originated from the Work of this Thesis --- p.ix / Chapter Part I: --- "Self-Assemblyy Stability Quantification, Controlled Molecular Switching, and Sensing Properties of an Anthracene-Containing Dynamic [2]Rotaxane" / Chapter Chapter 1 - --- Introduction / Chapter 1.1 --- Definition of Rotaxane --- p.2 / Chapter 1.2 --- Dynamic Covalent Chemistry in Rotaxane Synthesis --- p.5 / Chapter 1.3 --- Thermodynamic Template --- p.6 / Chapter 1.4 --- Molecular Sensing Properties in Rotaxane --- p.10 / Chapter 1.5 --- Examples --- p.13 / Chapter Chapter 2 - --- Anthracene-Containing Dynamic [2]Rotaxane / Chapter 2.1 --- Background --- p.17 / Chapter 2.2 --- Modification and Design of Dynamic [2]Rotaxane --- p.18 / Chapter 2.3 --- Self-Assembly of Rotaxane and Synthesis of Components --- p.19 / Chapter 2.4 --- Characterization / Chapter 2.4.1 --- 1H NMR Spectroscopy --- p.21 / Chapter 2.4.2 --- 13C NMR Spectroscopy --- p.23 / Chapter 2.4.3 --- Mass Spectrometry --- p.24 / Chapter 2.4.4 --- X-Ray Crystallography --- p.25 / Chapter 2.4.5 --- UV/Visible Absorption and Fluorescence Spectroscopies --- p.26 / Chapter 2.5 --- Effect of External Stimuli / Chapter 2.5.1 --- Addition of Water --- p.29 / Chapter 2.5.2 --- Addition of Acid --- p.33 / Chapter 2.5.3 --- Addition of Salts --- p.38 / Chapter 2.5.4 --- Addition of Amines --- p.40 / Chapter 2.6 --- Conclusions --- p.43 / Chapter Part II: --- Substituent Effect in Imine-Containing Molecular Tweezers / Chapter Chapter 3 - --- Molecular Tweezers / Chapter 3.1 --- Introduction --- p.46 / Chapter 3.2 --- Synthesis --- p.48 / Chapter 3.3 --- Characterization of Molecular Tweezers / Chapter 3.3.1 --- 1H NMR Spectroscopy --- p.49 / Chapter 3.3.2 --- Mass Spectrometry --- p.51 / Chapter 3.4 --- Characterization of Molecular Tweezers / Chapter 3.4.1 --- 1H NMR Spectroscopy --- p.51 / Chapter 3.4.2 --- X-Ray Crystallography --- p.59 / Chapter 3.4.3 --- Mass Spectrometry --- p.60 / Chapter 3.4.4 --- UV/Visible Absorption Spectroscopy --- p.61 / Chapter 3.5 --- Conclusions --- p.63 / Chapter Chapter 4 - --- Experimental Procedures / Chapter 4.1 --- General Information --- p.64 / Chapter 4.2 --- General Synthetic Procedures for Molecular Tweezers (34-40) --- p.65 / Chapter 4.3 --- Experimental Procedures --- p.65 / Chapter 4.4 --- Determination of Binding Constant K --- p.73 / References --- p.76 / Appendix / List of Spectra --- p.A-l / List of Crystal Data --- p.A-2

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Coherence of self-concept refers to the ability to stabilize on a clear set of views about oneself. This aspect of self-structure is closely linked self-esteem, and similar evidence in emotion research suggests an intricate connection between the self-system and emotion. Evidence suggests that emotions of seemingly opposing valence such as happy and sad can co-occur (i.e., mixed emotion). This study validated a new set of emotional stimuli particularly to elicit mixed emotion and used these stimuli with a mouse task that allowed participants to report positive and negative emotions simultaneously. The study examined possible individual differences in discrete emotional response associated with self-esteem as well as a possible connection between selfconcept coherence and a differential ability to harbor mixed emotions; specifically that individuals with high coherence in self-concept would tend to disambiguate their emotional response, but those with low coherence would be more susceptible to cooccurring positive and negative emotion. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Iterative Learning Control (ILC) iterates with a real world control system repeatedly performing the same task. It adjusts the control action based on error history from the previous iteration, aiming to converge to zero tracking error. ILC has been widely used in various applications due to its high precision in trajectory tracking, e.g. semiconductor manufacturing sensors that repeatedly perform scanning maneuvers. Designing effective feedback controllers for non-minimum phase (NMP) systems can be challenging. Applying Iterative Learning Control (ILC) to NMP systems is particularly problematic. Asking for zero error at sample times usually involves inverting the control system. However, the inverse process is unstable when the system has NMP zeros. The control action will grow exponentially every time step, and the error between time steps also grows exponentially. If there are NMP zeros on the negative real axis, the control action will alternate its sign every time step. ILC must be digital to use previous run data to improve the tracking error in the current run. There are two kinds of NMP digital systems, ones having intrinsic NMP zeros as images of continuous time NMP zeros, and NMP sampling zeros introduced by discretization. Two ILC design methods have been investigated in this thesis to handle NMP sampling zeros, producing zero tracking error at addressed sample times: (1) One can simply start asking for zero error after a few initial time steps, like using multiple zero order holds for the first addressed time step only (2) Or increase the sample rate, ask for zero error at the original rate, making two or more zero order holds per addressed time step. The internal instability can be manifested by the singular value decomposition of the input-output matrix. Non-minimum phase systems have particularly small singular values which are related to the NMP zeros. The aim is to eliminate these anomalous singular values. However, when applying the second approach, there are cases that the original anomalous singular values are gone, but some new anomalous singular values appear in the system matrix that cause difficulties to the inverse problem. Not asking for zero error for a small number of initial addressed time steps is shown to eliminate all anomalous singular values. This suggests that a more accurate statement of the second approach is: using multiple zero order holds per addressed time step, and eliminating a few initial addressed time steps if there are new anomalous singular values. We also extend the use of these methods to systems having intrinsic NMP zeros. By modifying ILC laws to perform pole-zero cancellation inside the unit circle, we observe that all of the rules for sampling zeros are effective for intrinsic zeros. Hence, one can now achieve convergence to zero tracking error at addressed time steps in ILC of NMP systems with a well behaved control action. In addition, this thesis studies the robustness of the two approaches along with several other candidate approaches with respect to model parameter uncertainty. Three classes of ILC laws are used. Both approaches show great robustness. Quadratic cost ILC is seen to have substantially better robustness to parameter uncertainty than the other laws.

University of Technology, Sydney. Faculty of Business. / The need for more dynamic views on international joint ventures' control research has recently become a growing concern. Changes in the complexity of relationships between organisations and their environments have led to an increase in control problems and to a need to investigate a suitable framework of management control. The concept of self-organising systems that has emerged with the science of complexity produces some useful and interesting new ways to examine the behaviour of complex systems. Therefore, extending the recent development in self-organising systems into international joint ventures' control research is an opportunity to explore new insights into the development of joint ventures. This study takes an integrative approach by focusing on the integration of management control and self-organising properties of international joint ventures. The purpose of this study is to investigate the roles of management control systems in affecting international joint ventures' performance, from the perspective of alliance complexity constraints. A model of management control in international joint ventures as self-organising systems, representing a complexity-control-outcomes framework, is developed and tested empirically using the partial least square (FLS) approach, a distinctive structural equation modeling (SEM) based technique. The primary results of this study show that formal control mechanisms and control extent have significant direct effects on management automony and the international joint ventures' performance. Management autonomy as an intervening endogenous construct has a significant direct effect on the international joint ventures performance. Significant direct effects of organisational complexity on the formal control mechanisms and control extent are found, and a significant indirect effect of organisational complexity on the management autonomy is found. The overall results suggest a sound link between the complexity-control framework with the control-outcome framework, and the achievement of fit between these two frameworks is important for superior international joint ventures' performance.

University of Technology, Sydney. Faculty of Business. / The need for more dynamic views on international joint ventures' control research has recently become a growing concern. Changes in the complexity of relationships between organisations and their environments have led to an increase in control problems and to a need to investigate a suitable framework of management control. The concept of self-organising systems that has emerged with the science of complexity produces some useful and interesting new ways to examine the behaviour of complex systems. Therefore, extending the recent development in self-organising systems into international joint ventures' control research is an opportunity to explore new insights into the development of joint ventures. This study takes an integrative approach by focusing on the integration of management control and self-organising properties of international joint ventures. The purpose of this study is to investigate the roles of management control systems in affecting international joint ventures' performance, from the perspective of alliance complexity constraints. A model of management control in international joint ventures as self-organising systems, representing a complexity-control-outcomes framework, is developed and tested empirically using the partial least square (FLS) approach, a distinctive structural equation modeling (SEM) based technique. The primary results of this study show that formal control mechanisms and control extent have significant direct effects on management automony and the international joint ventures' performance. Management autonomy as an intervening endogenous construct has a significant direct effect on the international joint ventures performance. Significant direct effects of organisational complexity on the formal control mechanisms and control extent are found, and a significant indirect effect of organisational complexity on the management autonomy is found. The overall results suggest a sound link between the complexity-control framework with the control-outcome framework, and the achievement of fit between these two frameworks is important for superior international joint ventures' performance.