This dissertation explores the emergence of the complexity paradigm in our technoscience culture and proposes "scientific fiction" as a genre of cultural studies based on that paradigm. Throughout this dissertation, I use the terms and concepts of complexity theory developed by new science, which revises the reductionism and linearity of classic science. The complexity paradigm signifies a system of all knowledge that conceives the productivity and creativity of the complexity created by interconnective and interactive dynamics among and within systems. As a literary response to the complexity paradigm, scientific fiction emphasizes the productivity and creativity of the complexity, offering the possibility of the human‘s co-evolution with technoscience. These characteristics of scientific fiction help articulate new ontological, ethical, and aesthetic visions for the posthuman. This dissertation ultimately highlights the strong feedback loops of technoscience, literature, and culture, which promote the complexity paradigm. By comparing Pat Cadigan‘s Synners as a scientific fiction novel and William Gibson‘s Neuromancer as a representative postmodern science fiction novel, Chapter II presents the defining characteristics of scientific fiction, reconfiguring humanity in relation to the technoscience environment. Furthermore, analyzing Greg Bear‘s Blood Music, the chapter claims that the human subject is an adaptive, self-organizing, interconnective system. Grounded in such understandings of humanity and subjectivity, the next chapter examines Marge Piercy‘s He, She and It to offer a new ethical perspective, or the complexity ethics, which establishes the interconnective and interactive relationship between the human and the technological as an evolutionary partner. The complexity ethics describes human behaviors and thoughts in our technoscience culture rather than prescribing a moral guideline. Next, in investigating Shelley Jackson‘s Patchwork Girl, a hypertext novel that rewrites Mary Shelley‘s Frankenstein, Chapter IV explores a new aesthetics appreciating the creativity of the complexity produced by interconnective and interactive dynamics. Finally, through the analyses of the scientific fiction novels, this dissertation suggests that scientific fiction is a transdisciplinary field that can offer new cultural visions.

This research examines the trade-off between the cost of adding complexity to a model and the value added to the results within the context of decision-making. It seeks to determine how complex a model should be in order to fit it to the purpose at hand. The report begins with a discussion on general modeling theory and model complexity. It next considers the specific case of petroleum reservoir models and the existing research that has compared modeling results with model complexity levels. Finally, it presents original results applying Monte Carlo sampling to a drilling decision scenario and to a one-dimensional reservoir model where a cylindrical oil field is represented by different numbers of cells and the results compared. / text

Although there are many studies about the relationship between grammar and lexicon, there is a lack of studies examining the relationship between the growth of compound words and complex sentences. This study examined the relationship between compound words at the lexical level, and complex sentences, auxiliaries and modals, and coordinated subjects/objects at the grammatical level, for both brain-injured and typically developing children between the ages 4 and 6. For 10 typically developing children, 3091 utterances, and for 18 brain-injured children, 6460 utterances were examined. The results revealed that both groups showed a significant increase in the use of compound words and coordinated clauses. The growth of auxiliary and modal verbs is significant only for typically developing children and the growth of subordinate clauses is significant just for brain injured children. Moreover, there is an interaction between the growth of coordinate clauses and compound words just for brain injured children.

Although there are many studies about the relationship between grammar and lexicon, there is a lack of studies examining the relationship between the growth of compound words and complex sentences. This study examined the relationship between compound words at the lexical level, and complex sentences, auxiliaries and modals, and coordinated subjects/objects at the grammatical level, for both brain-injured and typically developing children between the ages 4 and 6. For 10 typically developing children, 3091 utterances, and for 18 brain-injured children, 6460 utterances were examined. The results revealed that both groups showed a significant increase in the use of compound words and coordinated clauses. The growth of auxiliary and modal verbs is significant only for typically developing children and the growth of subordinate clauses is significant just for brain injured children. Moreover, there is an interaction between the growth of coordinate clauses and compound words just for brain injured children.

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This dissertation examines the nexus of complexity theory and peace education and its implications for developing educational praxis that engages with the demands of global complexity. In this thesis, I argue that as societies become more globalized and complex (global complexity) there is an onus upon education to adapt its methods so people can understand the workings of these processes better and further develop the ethical and creative resources needed for responding to system dynamics effectively. My central thesis is that the most appropriate way to do this is to use methods that are congruent with the subject matter of global complexity¿that is to align ones pedagogy with one¿s subject area. This dissertation therefore investigates the situated and contingent responses of peace educators working in the field to the challenges and opportunities that arise when attempting to adapt to local/global dynamics. It utilizes ethnography, narrative inquiry, and autoethnography and draws its data from interviews with over 50 educators in India, Japan, and the US. This research demonstrates that when engaging with global complexity, peace educators adapt both their ontological understanding and methodological orientation in ways congruent at times with the insights of complexity theory. While this understanding can be at odds with mass educational methodologies, this tension also is a touchstone for peace educator¿s creative formulation of novel praxis in response to the demands of global complexity. This dissertation thus examines some of the possibilities for learning within complex knowledge production systems and highlights the need for further research into the dynamics and processes at play within global educational ¿networks.¿

The wireless spectrum is limited and the demand for its use is increasing due to technological advancements in wireless communication, resulting in persistent interference issues. Despite progress in addressing interference, it remains a challenge for effective spectrum usage, particularly in the use of license-free and managed shared bands and other opportunistic spectrum access solutions. Therefore, efficient and interference-resistant spectrum usage schemes are critical. In the past, most interference solutions have relied on avoidance techniques and expert system-based mitigation approaches. Recently, researchers have utilized artificial intelligence/machine learning techniques at the physical (PHY) layer, particularly deep learning, which suppress or compensate for the interfering signal rather than simply avoiding it. In addition, deep learning has been utilized by researchers in recent years to address various difficult problems in wireless communications such as, transmitter classification, interference classification and modulation recognition, amongst others. To this end, this dissertation presents a thorough analysis of deep learning techniques for interference classification and suppression, and it thoroughly examines complexity (sample and model) issues that arise from using deep learning. First, we address the knowledge gap in the literature with respect to the state-of-the-art in deep learning-based interference suppression. To account for the limitations of deep learning-based interference suppression techniques, we discuss several challenges, including lack of interpretability, the stochastic nature of the wireless channel, issues with open set recognition (OSR) and challenges with implementation. We also provide a technical discussion of the prominent deep learning algorithms proposed in the literature and also offer guidelines for their successful implementation. Next, we investigate convolutional neural network (CNN) architectures for interference and transmitter classification tasks. In particular, we utilize a CNN architecture to classify interference, investigate model complexity of CNN architectures for classifying homogeneous and heterogeneous devices and then examine their impact on test accuracy. Next, we explore the issues with sample size and sample quality with regards to the training data in deep learning. In doing this, we also propose a rule-of-thumb for transmitter classification using CNN based on the findings from our sample complexity study. Finally, in cases where interference cannot be avoided, it is important to suppress such interference. To achieve this, we build upon autoencoder work from other fields to design a convolutional neural network (CNN)-based autoencoder model to suppress interference thereby ensuring coexistence of different wireless technologies in both licensed and unlicensed bands. / Doctor of Philosophy / Wireless communication has advanced a lot in recent years, but it is still hard to use the limited amount of available spectrum without interference from other devices. In the past, researchers tried to avoid interference using expert systems. Now, researchers are using artificial intelligence and machine learning, particularly deep learning, to mitigate interference in a different way. Deep learning has also been used to solve other tough problems in wireless communication, such as classifying the type of device transmitting a signal, classifying the signal itself or avoiding it. This dissertation presents a comprehensive review of deep learning techniques for reducing interference in wireless communication. It also leverages a deep learning model called convolutional neural network (CNN) to classify interference and investigates how the complexity of the CNN effects its performance. It also looks at the relationship between model performance and dataset size (i.e., sample complexity) in wireless communication. Finally, it discusses a CNN-based autoencoder technique to suppress interference in digital amplitude-phase modulation system. All of these techniques are important for making sure different wireless technologies can work together in both licensed and unlicensed bands.

Control of tuberculosis has often been managed as a simple issue, the belief being that activities to care for people affected by tuberculosis can be uniformly standardised and centred on the diagnosis and treatment of tuberculosis. The DOTS strategy has been the most concrete illustration of this approach. It is undeniable that this has been successful in re-organising unstructured and very inefficient national TB control programmes. Today, many countries’ programmes are better organised and have reached case detection and cure rates close to the targets set by WHO (i.e., 70% of cases effectively detected and 85% of detected cases cured). There are mounting arguments to enlarge the scope of activities to care for people affected by TB beyond the classical standardised strategies for diagnostic and treatment of tuberculosis. Indeed, it has become widely accepted that to increase further coverage of diagnosis and treatment of TB, it is necessary to address the economic and psychosocial problems of the people affected by tuberculosis, particularly for those having the least access to and worse quality of care. This will be possible only if, additionally to the current approaches, customised care can be developed after analysis to capture the complexity of care and interventions that take the specificity of local systems in their context into consideration. In chapter 1-2, we illustrate this through the review of the recent customisation of Directly Observed Treatment (DOT) as its naturally evolve in various contexts world-wide. Developing an analysis that captures complex issues in PATB care means having a proper understanding of the interactions between parts of the local care systems to people affected by TB and identifications of the important patterns of these interactions. That is possible only if information different than the usual quantitative indicators is generated. We illustrate this in the part 2. We took the case of Nicaragua’s TB control programme, which is renowned for its performance in America. In that context, we illustrated the limits of a classical approach to TB control programme evaluation (chapter 2-3) and gave four examples of care process analysis that illustrated the economic and psychosocial problems of people affected by tuberculosis (PATB) (chapter 2-4 to 2-7). Developing customised system-sensitive interventions to improve the care process means recognising that the interventions cannot be isolated from the organisational context and social dynamics during changes. Thinking must therefore move beyond the design of universal, standardised tool kits. We illustrate specifically in the part 3 the importance of combined local, national and international processes in improving the care process for people affected by TB in Nicaragua: lessons from successful and unsuccessful local and customised processes of implementing interventions in four local health systems (chapter 3-4) can be an opportunity for a health system research unit in a public health school to build a strategic process of care improvement at national level (through scaling up and through the building of a conducive environment) (chapter 3-5). As a conclusion of this work, we propose in part 4 a three-level reflection through discussion of patterns emerging from the analysis done in the previous chapters: (1) patterns of care and (2) of organisation of health care system are presented in the form of an analytical framework; (3) patterns of regulation and management to improve care for PATB are presented together with a strategy to work on it.

There has been a lot of work done in recent decades in the field of symbolic dynamics. Much attention has been paid to the so-called "complexity" function, which gives a sense of the rate at which the number of words in the system grow. In this paper, we explore this and several notions of complexity of specific symbolic dynamical systems. In particular, we compute positive entropy and state some k-balancedness properties of a few specific (random) substitutions. We also view certain sequences as subsets of Z², stating several properties and computing bounds on entropy in a specific example. / Graduation date: 2011

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