I argue that Guillermo Del Toro is a modern day storyteller, writing his film script as Ovid, The Grimms, or even Baum would pen their tales. I expand the idea of intertextuality to include not only conversations, thoughts, architecture, emotions, bodily activity, sounds, and innumerable other signifiers, but also analyzing the film "inter-imagically." By considering the film in this way it encourages a world conversation that can influence socio-political transformations in our world. I use the stories and images in myth, fairy tale and children's literature to support the importance of agency, agency in regard to finding your own voice, determining your own path and taking action by making choices that can ultimately result in transformations that are not only personal, but political ramifications in the world. In Echo and Narcissus, I explore issues surrounding the self-realization and loss of agency through Ofelia's refusal to follow someone else's path, allowing others to silence her voice or her potential. Using Little Red Riding Hood, I assert that Ofelia is the modern Little Red, refusing to take the path of her mother, defining her own way. Using The Wizard of Oz, I highlight the labyrinth of choice and the determination to return, restore or transform Ofelia's world. I further proclaim Del Toro is a master storyteller along the lines of Ovid, Grimm and Baum and that through his film, he inspires adults to get back in touch with the childhood disobedience and questioning in order to spark a world conversation that just may transform the world around us.

DNA can be represented abstrzctly as a language with only four nucleotides represented by the letters A, C, G, and T, yet the arrangement of those four letters plays a major role in determining the development of an organism. Understanding the signi cance of certain arrangements of nucleotides can unlock the secrets of how the genome achieves its essential functionality. Regions of DNA particularly enriched with cytosine (C nucleotides) and guanine (G nucleotides), especially the CpG di-nucleotide, are frequently associated with biological function related to gene expression, and concentrations of CpGs referred to as \CpG islands" are known to collocate with regions upstream from gene coding sequences within the promoter region. The pattern of occurrence of these nucleotides, relative to adenine (A nucleotides) and thymine (T nucleotides), lends itself to analysis by machine-learning techniques such as Hidden Markov Models (HMMs) to predict the areas of greater enrichment. HMMs have been applied to CpG island prediction before, but often without an awareness of how the outcomes are a ected by the manner in which the HMM is applied. Two main ndings of this study are: 1. The outcome of a HMM is highly sensitive to the setting of the initial probability estimates. 2. Without the appropriate software techniques, HMMs cannot be applied e ectively to large data such as whole eukaryotic chromosomes. Both of these factors are rarely considered by users of HMMs, but are critical to a successful application of HMMs to large DNA sequences. In fact, these shortcomings were discovered through a close examination of published results of CpG island prediction using HMMs, and without being addressed, can lead to an incorrect implementation and application of HMM theory. A rst-order HMM is developed and its performance compared to two other historical methods, the Takai and Jones method and the UCSC method from the University of California Santa Cruz. The HMM is then extended to a second-order to acknowledge that pairs of nucleotides de ne CpG islands rather than single nucleotides alone, and the second-order HMM is evaluated in comparison to the other methods. The UCSC method is found to be based on properties that are not related to CpG islands, and thus is not a fair comparison to the other methods. Of the other methods, the rst-order HMM method and the Takai and Jones method are comparable in the tests conducted, but the second-order HMM method demonstrates superior predictive capabilities. However, these results are valid only when taking into consideration the highly sensitive outcomes based on initial estimates, and nding a suitable set of estimates that provide the most appropriate results. The rst-order HMM is applied to the problem of producing synthetic data that simulates the characteristics of a DNA sequence, including the speci ed presence of CpG islands, based on the model parameters of a trained HMM. HMM analysis is applied to the synthetic data to explore its delity in generating data with similar characteristics, as well as to validate the predictive ability of an HMM. Although this test fails to i meet expectations, a second test using a second-order HMM to produce simulated DNA data using frequency distributions of CpG island pro les exhibits highly accurate predictions of the pre-speci ed CpG islands, con- rming that when the synthetic data are appropriately structured, an HMM can be an accurate predictive tool. One outcome of this thesis is a set of software components (CpGID 2.0 and TrackMap) capable of ef- cient and accurate application of an HMM to genomic sequences, together with visualization that allows quantitative CpG island results to be viewed in conjunction with other genomic data. CpGID 2.0 is an adaptation of a previously published software component that has been extensively revised, and TrackMap is a companion product that works with the results produced by the CpGID 2.0 program. Executing these components allows one to monitor output aspects of the computational model such as number and size of the predicted CpG islands, including their CG content percentage and level of CpG frequency. These outcomes can then be related to the input values used to parameterize the HMM.

The subject of this monograph can be described as the local properties of geometric Steiner minimal trees in finite-dimensional normed spaces. A Steiner minimal tree of a finite set of points is a shortest connected set interconnecting the points. For a quick introduction to this topic and an overview of all the results presented in this work, see Chapter 1. The relevant mathematical background knowledge needed to understand the results and their proofs are collected in Chapter 2. In Chapter 3 we introduce the Fermat-Torricelli problem, which is that of finding a point that minimizes the sum of distances to a finite set of given points. We only develop that part of the theory of Fermat-Torricelli points that is needed in later chapters. Steiner minimal trees in finite-dimensional normed spaces are introduced in Chapter 4, where the local Steiner problem is given an exact formulation. In Chapter 5 we solve the local Steiner problem for all two-dimensional spaces, and generalize this solution to a certain class of higher-dimensional spaces (CL spaces). The twodimensional solution is then applied to many specific norms in Chapter 6. Chapter 7 contains an abstract solution valid in any dimension, based on the subdifferential calculus. This solution is applied to two specific high-dimensional spaces in Chapter 8. In Chapter 9 we introduce an alternative approach to bounding the maximum degree of Steiner minimal trees from above, based on the illumination problem from combinatorial convexity. Finally, in Chapter 10 we consider the related k-Steiner minimal trees, which are shortest Steiner trees in which the number of Steiner points is restricted to be at most k. / Das Thema dieser Habilitationsschrift kann als die lokalen Eigenschaften der geometrischen minimalen Steiner-Bäume in endlich-dimensionalen normierten Räumen beschrieben werden. Ein minimaler Steiner-Baum einer endlichen Punktmenge ist eine kürzeste zusammenhängende Menge die die Punktmenge verbindet. Kapitel 1 enthält eine kurze Einführung zu diesem Thema und einen Überblick über alle Ergebnisse dieser Arbeit. Die entsprechenden mathematischen Vorkenntnisse mit ihren Beweisen, die erforderlich sind die Ergebnisse zu verstehen, erscheinen in Kapitel 2. In Kapitel 3 führen wir das Fermat-Torricelli-Problem ein, das heißt, die Suche nach einem Punkt, der die Summe der Entfernungen der Punkte einer endlichen Punktmenge minimiert. Wir entwickeln nur den Teil der Theorie der Fermat-Torricelli-Punkte, der in späteren Kapiteln benötigt wird. Minimale Steiner-Bäume in endlich-dimensionalen normierten Räumen werden in Kapitel 4 eingeführt, und eine exakte Formulierung wird für das lokale Steiner-Problem gegeben. In Kapitel 5 lösen wir das lokale Steiner-Problem für alle zwei-dimensionalen Räume, und diese Lösung wird für eine bestimmte Klasse von höher-dimensionalen Räumen (den sog. CL-Räumen) verallgemeinert. Die zweidimensionale Lösung wird dann auf mehrere bestimmte Normen in Kapitel 6 angewandt. Kapitel 7 enthält eine abstrakte Lösung die in jeder Dimension gilt, die auf der Analysis von Subdifferentialen basiert. Diese Lösung wird auf zwei bestimmte höher-dimensionale Räume in Kapitel 8 angewandt. In Kapitel 9 führen wir einen alternativen Ansatz zur oberen Schranke des maximalen Grads eines minimalen Steiner-Baums ein, der auf dem Beleuchtungsproblem der kombinatorischen Konvexität basiert ist. Schließlich betrachten wir in Kapitel 10 die verwandten minimalen k-Steiner-Bäume. Diese sind die kürzesten Steiner-Bäume, in denen die Anzahl der Steiner-Punkte auf höchstens k beschränkt wird.

No description available.

Thesis (Ph.D.)--Bowling Green State University, 2009. / Document formatted into pages; contains vi, 269 p. Includes bibliographical references.

Thesis (M.A.)--Miami University, Dept. of History, 2005. / Title from first page of PDF document. Document formatted into pages; contains [7], 53 p. : ill. Includes bibliographical references (p. 49-53).

University, Diss., 2002--Saarbrücken.

A speaker independent continuous speech phoneme recognition and segmentation system is presented. We discuss the training and recognition phases of the phoneme recognition system as well as a detailed description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for the phoneme states. We describe in detail the three phases of the phoneme joint recognition and segmentation system. First, the extraction of the Mel-Frequency Cepstral Coefficients (MFCC) and the corresponding Delta and Delta Log Power coefficients is described. Second, we describe the operation of the Baum-Welch re-estimation procedure for the training of the phoneme HMM models, including the K-Means and the Expectation-Maximization (EM) clustering algorithms used for the initialization of the Baum-Welch algorithm. Additionally, we describe the structural framework of - and the recognition procedure for - the ergodic Segmental HMM for the phoneme segmentation and recognition. We include test and simulation results for each of the individual systems integrated into the phoneme recognition system and finally for the phoneme recognition/segmentation system as a whole. / Master of Science

Since the 1950s, hidden Markov models (HMMS) have seen widespread use in electrical engineering. Foremost has been their use in speech processing, pattern recognition, artificial intelligence, queuing theory, and communications theory. However, recent years have witnessed a renaissance in the application of HMMs to the analysis and simulation of digital communication systems. Typical applications have included signal estimation, frequency tracking, equalization, burst error characterization, and transmit power control. Of special significance to this thesis, however, has been the use of HMMs to model fading channels typical of wireless communications. This variegated use of HMMs is fueled by their ability to model time-varying systems with memory, their ability to yield closed form solutions to otherwise intractable analytic problems, and their ability to help facilitate simple hardware and/or software based implementations of simulation test-beds. The aim of this thesis is to employ and exploit hidden Markov fading models within an iterative (turbo) decoding framework. Of particular importance is the problem of channel estimation, which is vital for realizing the large coding gains inherent in turbo coded schemes. This thesis shows that a Markov fading channel (MFC) can be conceptualized as a trellis, and that the transmission of a sequence over a MFC can be viewed as a trellis encoding process much like convolutional encoding. The thesis demonstrates that either maximum likelihood sequence estimation (MLSE) algorithms or maximum <I> a posteriori</I> (MAP) algorithms operating over the trellis defined by the MFC can be used for channel estimation. Furthermore, the thesis illustrates sequential and decision-directed techniques for using the aforementioned trellis based channel estimators <I>en masse</I> with an iterative decoder. / Master of Science

Ein zunehmend wichtiges Thema in der Informatik ist der Umgang mit Ähnlichkeit in einer großen Anzahl unterschiedlicher Domänen. Derzeit existiert keine universell verwendbare Infrastruktur für die Ähnlichkeitssuche in allgemeinen metrischen Räumen. Ziel der Arbeit ist es, die Grundlage für eine derartige Infrastruktur zu legen, die in klassische Datenbankmanagementsysteme integriert werden könnte. Im Rahmen einer Analyse des State of the Art wird der M-Baum als am besten geeignete Basisstruktur identifiziert. Dieser wird anschließend zum EM-Baum erweitert, wobei strukturelle Kompatibilität mit dem M-Baum erhalten wird. Die Abfragealgorithmen werden im Hinblick auf eine Minimierung notwendiger Distanzberechnungen optimiert. Aufbauend auf einer mathematischen Analyse der Beziehung zwischen Baumstruktur und Abfrageaufwand werden Freiheitsgrade in Baumänderungsalgorithmen genutzt, um Bäume so zu konstruieren, dass Ähnlichkeitsanfragen mit einer minimalen Anzahl an Anfrageoperationen beantwortet werden können. / A topic of growing importance in computer science is the handling of similarity in multiple heterogenous domains. Currently there is no common infrastructure to support this for the general metric space. The goal of this work is lay the foundation for such an infrastructure, which could be integrated into classical data base management systems. After some analysis of the state of the art the M-Tree is identified as most suitable base and enhanced in multiple ways to the EM-Tree retaining structural compatibility. The query algorithms are optimized to reduce the number of necessary distance calculations. On the basis of a mathematical analysis of the relation between the tree structure and the query performance degrees of freedom in the tree edit algorithms are used to build trees optimized for answering similarity queries using a minimal number of distance calculations.