The intent of this thesis is to clarify my artistic working process as well as the resulting thesis exhibition, Only the lull I like, the hum of your valved voice. I will provide explanations and descriptions of my exhibition (comprising a select placement of objects) as well as offer antecedents, informants, and the evolution of my art practice as a whole during my graduate studies. Specifically the work is discussed through the lenses of situational aesthetics, conceptual relationships, and perceptual absence to argue for the complicated semantics of the viewer within an ontology of object-hood and pre-established conditions.

Determining the cosmic star formation history of the Universe is fundamental for our understanding of galaxy formation and evolution. While surveys now suggest that the "epoch" of galaxy formation occurred more than 6 billion years ago, our measurements still suff er from signi ficant scatter and uncertainties due to the use of diff erent indicators, dust extinction and the e ffects of cosmic variance in the current samples. Furthermore, understanding galaxy formation and evolution require us to go much beyond simply determining the star formation history of the Universe with high accuracy: what are the physical mechanisms driving the strong evolution that we observe? How does star formation depend on stellar mass and environment and how does that change with cosmic time? This thesis presents both a completely self-consistent determination of the star formation history of the Universe (based on a single, sensitive and well-calibrated star formation indicator up to redshift z ~ 2:3: the H α luminosity) and investigates its drivers by exploring large area surveys (probing a range of environments and overcoming cosmic variance) obtained with the High-redshift Emission Line Survey (HiZELS). HiZELS is a panoramic extragalactic survey using the WFCAM instrument on the 3.8-m UK Infrared Telescope (UKIRT) which utilizes a set of existing and custom-made narrow-band filters in the J, H and K bands to detect emission line galaxies (main targets are H α emitters at z = 0:84, z = 1:47 and z = 2:23) up to z ~ 9 over square degree areas of extragalactic sky. Detailed measurements of the H α luminosity function and its evolution with redshift are presented, revealing a signi ficant luminosity evolution. The clustering properties of H α emitters at high-redshift are quantifi ed and investigated for the first time, revealing that these distant galaxies reside in Milky-Way type dark matter haloes at z ~ 1. Mass and environment are found to have important and inter-dependent roles on star formation at high-z and the results are able to reconcile previously contradictory results in the literature. Furthermore, by conducting a novel double-narrow band survey at z = 1:47, the relationship between the [Oii]3727 and H α emission lines is studied in detail and directly compared to z ~ 0, showing no signifi cant evolution in the dust properties of star-forming galaxies, despite the very strong luminosity evolution. Finally, this thesis also presents the widest search for very distant Ly α emitters at z ~ 9.

The diploma thesis "Christian-Buddhist Polemic in Japan in the 17th century" deals mainly with the life and work of Fabian Fucan, one of the most noticeable figures of the early Japanese Christianity. The first part of the thesis is focused on the coming of Christianity to Japan, the mission and the subsequent persecution of Christians which later resulted in the final prohibition of Christianity in the country. The main part of the thesis puts brain to the analysis of two key works of Fabian Fucan - Christian apologetics called Myōtei Mondō and then anti-Christian treatise Ha Daiusu that Fucan wrote after leaving the Society of Jesus and becoming attached back to Buddhism. The final chapter pays attention to the phenomenon of "kakure kirishitan" ("hidden Christians"), i.e. groups of Christians who are the descendants of Christians secretly practicing during the Sakoku (literally, "closed country") period.

The primary aim, as set out in the Introduction, is to explore women’s specific difficulties regarding labour market outcomes in the first decade of the 21st century, related to their dual role as mothers and labour force participants. The overarching context of the thesis is a contemporary profile of the working woman in Great Britain who is struggling to balance motherhood and paid work successfully, with the consequences this might have for her mental health. This thesis contains three empirical chapters exploring women’s employment and health interactions, through the consequences of the 2008/9 economic crisis on the UK gender wage gap, the effects of postpartum depression on maternal employment after childbirth, and the potential long-term impacts of postpartum depression on children’s emotional health and cognitive developmental outcomes. Specifically, this thesis seeks to address the following research questions: Did the great recession affect the wage gender gap? Does postpartum depression affect employment? Does postpartum depression predict emotional and cognitive difficulties in 11 year olds? Recent estimates reveal that 1 in 10 children aged 5-16 years have a diagnosable mental health problem and 1 in 5 mothers suffer from perinatal mental disorders, which highlight how widespread mental health problems are and how important the promotion of good mental health and prevention is at crucial stages in development. The results of the three empirical chapters of the thesis point to the need for an innovative and comprehensive approach to the distinct problems faced by different groups and sub-categories within the population.

Finance should be studied as a hard science, where scientific methods apply. When a trading strategy is proposed, the underlying model should be transparent and defined robustly to allow other researchers to understand and examine it thoroughly. Any reports on experimental results must allow other researchers to trace back to the original data and models that produced them. Like any hard sciences, results must be repeatable to allow researchers to collaborate and build upon each other’s results. Large-scale collaboration, when applying the steps of scientific investigation, is an efficient way to leverage crowd science to accelerate research in finance. Unfortunately, the current reality is far from that. Evidence shows that current methods of investigation in finance in most cases do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of financial decisions at all levels, from personal investment choices to overreaching global economic policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and true is blurred. In this research, we advocate the use of a next-generation investigative approach leveraging forces of human diversity, micro-specialized crowds, and proper computer-assisted control methods associated with accessibility, reproducibility, communication, and collaboration. This thesis is structured in three distinctive parts. The first part defines a set of very specific cognitive and non-cognitive enablers for crowd-based scientific investigation: methods of proof, large-scale collaboration, and a domain-specific computational representation. These enablers allow the application of procedures of structured scientific investigation powered by crowds, a “collective brain in which neurons are human collaborators”. The second part defines a specialized computational representation to allow proper controls and collaboration in large-scale in the field of economics. A computational representation is a role-based representation system based on facets, contributions, and constraints of data, and used to define concepts related to a specific domain of knowledge for crowd-based investigation. The third and last part performs an end-to-end investigation of a non-trivial problem in finance by measuring the actual performance of a momentum strategy in technical analysis, applying formal methods of investigation developed over the first and second part of this research.

This thesis examines the relationship between food prices, oil prices, carbon emission prices, exchange rates and global risk perception. To obtain the effects of these variables on the food prices, Toda and Yamamoto procedure is employed for 5-day week daily time series covering the period February 27, 2008 and March 21, 2011. The empirical results indicate that only volatility index Granger causes food prices. Furthermore, according to results of generalized impulse response plots food prices respond to all variables in the short run.

This thesis estimates the quarterly electricity demand of Turkey. First of all proper seasonal time series model are found for the variables: electricity demand, temperature, gross domestic product and electricity price. After the right seasonal time series model are found Hylleberg, Engle, Granger and Yoo (1990) test is applied to each variable. The results of the test show that seasonal unit roots exist for the electricity price even it cannot be seen at the graph. The other variables have no seasonal unit roots when the proper seasonal time series model is chosen. Later, the cointegration is tested by looking at the vector autoregressive model. As the cointegration is seen vector error correction model is found. There is long-run equilibrium when the price is the dependent variable and independent variable is gross domestic product. Temperature is taken as exogenous variable and demand is not statistically significant.

Financial failure is an important subject for both the economical development of the country and for the self - evaluation of individual companies. Increase in the number of financially failed companies points out the misuse of the country resources. Recently, financial failure threatens both small and large companies in Turkey. It is important to determine factors that affect the financial failure by analyzing models and to use these models for auditing the financial situation. In today&rsquo / s Turkey, the statistical methods that are used for this purpose involve single level models applied to cross-sectional data. However, multilevel models applied to panel data are more preferable as they gather more information, and also, enable the calculated financial success probabilities to be more trustworthy. In this thesis, publicly available panel data that are collected from The Istanbul Stock Exchange are investigated. Mainly, financial success of companies from two sectors, namely industry and services, are investigated. For the analysis of this panel data, data exploration methods, missing data imputation, possible solutions to multicollinearity problem, single level logistic regression models and multilevel models are used. By these models, financial success probabilities for each company are calculated / the factors related to the financial failure are determined, and changes in time are observed. Models and early warning systems resulted in correct classification rates of up to 100%. In the services sector, a small number of companies having publicly available data result in a decline in the success of models. It is concluded that sharing data with more subjects observed in a longer time period collected in the same format with academicians, will result in better justified outputs, which are useful for both academicians and managers.

Environmental policymaking is a difficult issue for governments. It is desirable to have the decisions based on the results of quantitative and analytical studies. On the other hand, by their very nature, many such decisions have political aspects, whose subtleties are difficult to be captured by quantitative approaches alone. It is left to the political establishments to decide how best to allocate the efforts to improve environmental conditions. In this respect, evaluating the countries by generating environmental indices and the subsequent ranking of the countries with respect to those indices is a common practice. Perhaps the best known environmental sustainability index, the Environmental Performance Index-2008 (EPI-2008), is a composite index that comprises 6 core policy categories and 25 indicators. While recognizing the qualitative aspects of such decision making, in order to support and guide the policymaking process, we develop analytical tools to assist the process. We carefully delineate our models to be limited only to the provable quantitative properties of the available objective data. However, such data are processed into more meaningful statements concerning the available options. Specifically, using EPI-2008, meaningful mathematical models that shed further light onto the country sustainability measures are developed.

Epidemiological data with disease characteristic information can be modelled in several ways. One way is taking each disease characteristic as a response and constructing binary or polytomous logistic regression model. Second way is using a new response which consists of disease subtypes created by cross-classification of disease characteristic levels, and then constructing polytomous logistic regression model. The former may be disadvantageous since any possible covariation between disease characteristics is neglected, whereas the latter can capture that covariation behaviour. However, cross-classifying the characteristic levels increases the number of categories of response, so that dimensionality problem in parameter space may occur in classical polytomous logistic regression model. A two staged polytomous logistic regression model overcomes that dimensionality problem. In this thesis, study is progressen in two main directions: simulation study and data analysis parts. In simulation study, models that capture the covariation behaviour are compared in terms of the response model parameter estimators. That is, performances of the maximum likelihood estimation (MLE) approach to classical polytomous logistic regression, Bayesian estimation approach to classical polytomous logistic regression and pseudo-conditional likelihood (PCL) estimation approach to two stage polytomous logistic regression are compared in terms of bias and variation of estimators. Results of the simulation study revealed that for small sized sample and small number of disease subtypes, PCL outperforms in terms of bias and variance. For medium scaled size of total disease subtypes situation when sample size is small, PCL performs better than MLE, however when the sample size gets larger MLE has better performance in terms of standard errors of estimates. In addition, sampling variance of PCL estimators of two stage model converges to asymptotic variance faster than the ML estimators of classical polytomous logistic regression model. In data analysis, etiologic heterogeneity in breast cancer subtypes of Turkish female cancer patients is investigated, and the superiority of the two stage polytomous logistic regression model over the classical polytomous logistic model with disease subtypes is represented in terms of the interpretation of parameters and convenience in hypothesis testing.