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1.The Discursive Construction of Black Masculinity: Intersections of Race, Gender, and Sexuality
1.1.The Plight of Black Men: A History of Lynchings and Castrations
1.2.The Discursive Construction of the Black Man as Otherrn
1.3.Black Corporeality and the Scopic Regime of Racism
2. Ralph Ellison's 'Invisible man'
2.1.Invisible Black Men: Between Emasculation and Hypermasculinityrn
2.2.Transcending Invisibility
This dissertation focuses on the link between labour market institutions and precautionary savings. It is evaluated whether private households react to changes in social insurance provision such as the income replacement in case of unemployment by increased savings for precautionary reasons. The dissertation consists of three self-contained chapters, each focusing on slightly different aspects of the topic. The first chapter titled "Precautionary saving and the (in)stability of subjective earnings uncertainty" empirically looks at the influence of future income uncertainty on household saving behavior. Numerous cross-section studies on precautionary saving use subjective expectations regarding the income variance one year ahead as a proxy for income uncertainty. Using such proxies observed only at one point in time, however, may give rise to biased estimates for precautionary wealth if expectations are not stable over time. Survey data from the Dutch DNB Household Survey suggest that subjective future income distributions are not stable over the mid-term. Moreover, in this study I contrast estimates of precautionary wealth using the variation coefficient observed at one point in time with those using a simple mid-term average. Estimates of precautionary wealth based on the average are about 40% to 80% higher than the estimates using the variation coefficient observed only once. In addition to that, wealth accumulation for precautionary reasons is estimated for different parts of the income distribution. The share of precautionary wealth is highest for households at the center of the income distribution. By linking saving behaviour with unemployment insurance, the following chapters then shed some light on an issue that has largely been neglected in the literature on labour market institutions so far. Whereas the third chapter models the relevance of unemployment insurance for income uncertainty and intertemporal decision making during institutional reform processes, chapter 4 seeks to establish empirically a relationship between saving behavior and unemployment insurance. Social insurance, especially unemployment insurance, provides agents with income insurance against not marketable income risks. Since the early 1990s, reform measures like more activating policies as suggested by the OECD Jobs Study in 1994 have been observed in Europe. In the third chapter it is argued that such changes in unemployment insurance reduce public insurance and increase income uncertainty. Moreover, a simple three period model is discussed which shows a link between a welfare state reform and agents' saving decisions as one possible reaction of agents to self-insure against income risk. Two sources of uncertainty seem to be important in this context: (1) uncertain results of the reform process concerning the replacement rate, and (2) uncertainty regarding the timing of information about the content of the reform. It can be shown that the precautionary motive for saving explains an increased accumulation of capital in times of reform activities. In addition to that, early information about the expected replacement rate increases agents' utility and reduces under and oversaving. Following the argument of the previous chapters, that an important feature of labour market institutions in modern welfare states is to provide cash transfers as income replacement in case of unemployment, it is hypothesised that unemployment benefits reduce the motive to save for precautionary reasons. Based on consumer sentiment data from the European Commission's Consumer Survey, chapter four finally provides some evidence that aggregate saving intentions are significantly influenced by unemployment benefits. It can be shown that higher benefits lower the intention to save.
On the Influence of Ignored Stimuli: Generalization and Application of Distractor-Response Binding.
(2011)
In selection tasks where target stimuli are accompanied by distractors, responses to target stimuli, target stimuli and the distractor stimuli can be encoded together as one episode in memory. Subsequent repetition of any aspect of such an episode can lead to the retrieval of the whole episode including the response. Thus, repeating a distractor can retrieve responses given to previous targets; this mechanism was labeled distractor-response binding and has been evidenced in several visual setups. Three experiments of the present thesis implemented a priming paradigm with an identification task to generalize this mechanism to auditory and tactile stimuli as well as to stimulus concepts. In four more experiments the possible effect of distractor-response binding on drivers' reactions was investigated. The same paradigm was implemented using more complex stimuli, foot responses, go/no-go responses, and a dual task setup with head-up and head-down displays. The results indicate that distractor-response binding effects occur with auditory and tactile stimuli and that the process is mediated by a conceptual representation of the distractor stimuli. Distractor-response binding effects also revealed for stimuli, responses, and framework conditions likely to occur in a driving situation. It can be concluded that the effect of distractor-response binding needs to be taken into account for the design of local danger warnings in driver assistance systems.
This thesis centers on formal tree languages and on their learnability by algorithmic methods in abstractions of several learning settings. After a general introduction, we present a survey of relevant definitions for the formal tree concept as well as special cases (strings) and refinements (multi-dimensional trees) thereof. In Chapter 3 we discuss the theoretical foundations of algorithmic learning in a specific type of setting of particular interest in the area of Grammatical Inference where the task consists in deriving a correct formal description for an unknown target language from various information sources (queries and/or finite samples) in a polynomial number of steps. We develop a parameterized meta-algorithm that incorporates several prominent learning algorithms from the literature in order to highlight the basic routines which regardless of the nature of the information sources have to be run through by all those algorithms alike. In this framework, the intended target descriptions are deterministic finite-state tree automata. We discuss the limited transferability of this approach to another class of descriptions, residual finite-state tree automata, for which we propose several learning algorithms as well. The learnable class by these techniques corresponds to the class of regular tree languages. In Chapter 4we outline a recent range of attempts in Grammatical Inference to extend the learnable language classes beyond regularity and even beyond context-freeness by techniques based on syntactic observations which can be subsumed under the term 'distributional learning', and we describe learning algorithms in several settings for the tree case taking this approach. We conclude with some general reflections on the notion of learning from structural information.
Psychiatric/Behavioral disorders/traits are usually polygenic in nature, where a particular phenotype is the manifestation of multiple genes. However, the existence of large families with numerous members who are affected by these disorders/traits steers us towards a Mendelian (or monogenic) possibility, where the phenotype is caused by a single gene. In order to better understand the genetic architecture of general psychiatric/behavioral disorders/traits, this thesis investigates large pedigrees that display a Mendelian pattern for attention-deficit/hyperactivity disorder, schizophrenia and bipolar disorder. Numerous challenges in the field of psychiatric and behavioral sciences have impeded the genetic investigation of such disorders/traits. Examples include frequent cross-disorders, genetic heterogeneity across subjects as well as the use of diagnostic tools that can be subjective at times. To overcome these challenges, this thesis investigates large multi-generational pedigrees, which comprise a significant number of members who exhibit specific psychiatric/behavioral phenotypes. These pedigrees provide high-resolution experimental setups that can dissect the genetic complexities of psychiatric/behavioral disorders/traits. This thesis adopts a classical two-stage genetic approach to investigate the various psychiatric/behavioral disorders/traits in large pedigrees. The classical two-stage genetic approach is commonly used by many human geneticists to study a wide spectrum of human physiological disorders but is only being applied to the field of psychiatric and behavioral genetics recently. Through the study of large pedigrees, this thesis discovers the genomic regions that may play a causative role in the expression of certain psychiatric/behavioral disorders/traits within the vast genome.
In addition to the well-recognised effects of both, genes and adult environment, it is now broadly accepted that adverse conditions during pregnancy contribute to the development of mental and somatic disorders in the offspring, such as cardiovascular disorders, endocrinological disorders, metabolic disorders, schizophrenia, anxious and depressive behaviour and attention deficit hyperactivity disorder (ADHD). Early life events may have long lasting impact on tissue structure and function and these effects appear to underlie the developmental origins of vulnerability to chronic diseases. The assumption that prenatal adversity, such as maternal emotional states during pregnancy, may have adverse effects on the developing infant is not new. Accordant references can be found in an ancient Indian text (ca. 1050 before Christ), in biblical texts and in documents originating during the Middle Ages. Even Hippocrates stated possible effects of maternal emotional states on the developing fetus. Since the mid-1950s, research examining the effects of maternal psychosocial stress during pregnancy appeared in the literature. Extensive research in this field has been conducted since the early 1990s. Thus, the relationship between early life events and long-term health outcomes was already postulated over 20 years ago. David Barker and colleagues demonstrated that children of lower birth weight - which represents a crude marker of an adverse intrauterine environment - were at increased risk of high blood pressure, cardiovascular disorders, and type-2 diabetes later in life. These provocative findings led to a large amount of subsequent research, initially focussing on the role of undernutrition in determining fetal outcomes. The phenomenon of prenatal influences that determine in part the risk of suffering from chronic disease later in life has been named the "fetal origins of health and disease" paradigm. The concept of "prenatal programming" has now been extended to many other domains, such as the effects of prenatal maternal stress, prenatal tobacco exposure, alcohol intake, medication, toxins, as well as maternal infection and diseases. During the process of prenatal programming, environmental agents are transmitted across the placenta and act on specific fetal tissues during sensitive periods of development. Thus, developmental trajectories are changed and the organisation and function of tissue structure and organ system is altered. The biological purpose of those "early life programming" may consist in evolutionary advantages. The offspring adapts its development to the expected extrauterine environment which is forecast by the clues available during fetal life. If the fetus receives signals of a challenging environment, e.g. due to maternal stress hormones or maternal undernutrition, its survival may be promoted due to developmental adaptation processes. However, if the expected environment does not match with the real environment, maladapation and later disease risk may result. For example, a possible indicator of a "response ready" trait, such as hyperactivity/inattention may have been advantageous in an adverse ancient environment. However, it is of disadvantage when the postnatal environment demands oppositional skills, such as attention and concentration " e.g. in the classroom, at school, to achieve academic success. Borderline personality disorder (BPD) is a prevalent psychiatric disorder, characterized by impulsivity, affective instability, dysfunctional interpersonal relationships and identity disturbance. Although many studies report different risk factors, the exact etiologic mechanisms are not yet understood. In addition to the well-recognised effects of genetic components and adverse childhood experiences, BPD may potentially be co-determined by further environmental influences, acting very early in life: during pre- and perinatal period. There are several hints that may suggest possible prenatal programming processes in BPD. For example, patients with BPD are characterized by elevated stress sensitivity and reactivity and dysfunctions of the neuroendocrine stress system, such as the hypothalamic pituitary adrenal (HPA) axis. Furthermore, patients with BPD show a broad range of somatic comorbidities " especially those disorders for which prenatal programming processes have been described. During infancy and childhood, BPD patients already show behavioural and emotional abnormalities as well as pronounced temperamental traits, such as impulsivity, emotional dysregulation and inattention that may potentially be co-determined by prenatal programming processes. Such temperamental traits - similar to those, seen in patients with ADHD - have been described to be associated with low birthweight which indicates a suboptimal intrauterine environment. Moreover, the functional and structural alterations in the central nervous system (CNS) in patients with BPD might also be mediated in part by prenatal agents, such as prenatal tobacco exposure. Prenatal adversity may thus constitute a further, additional component in the multifactorial genesis of BPD. The association between BPD and prenatal risk factors has not yet been studied in such detail. We are not aware of any further study that assessed pre- and perinatal risk factors, such as maternal psychoscocial stress, smoking, alcohol intake, obstetric complications and lack of breastfeeding in patients with BPD.
Variational inequality problems constitute a common basis to investigate the theory and algorithms for many problems in mathematical physics, in economy as well as in natural and technical sciences. They appear in a variety of mathematical applications like convex programming, game theory and economic equilibrium problems, but also in fluid mechanics, physics of solid bodies and others. Many variational inequalities arising from applications are ill-posed. This means, for example, that the solution is not unique, or that small deviations in the data can cause large deviations in the solution. In such a situation, standard solution methods converge very slowly or even fail. In this case, so-called regularization methods are the methods of choice. They have the advantage that an ill-posed original problem is replaced by a sequence of well-posed auxiliary problems, which have better properties (like, e.g., a unique solution and a better conditionality). Moreover, a suitable choice of the regularization term can lead to unconstrained auxiliary problems that are even equivalent to optimization problems. The development and improvement of such methods are a focus of current research, in which we take part with this thesis. We suggest and investigate a logarithmic-quadratic proximal auxiliary problem (LQPAP) method that combines the advantages of the well-known proximal-point algorithm and the so-called auxiliary problem principle. Its exploration and convergence analysis is one of the main results in this work. The LQPAP method continues the recent developments of regularization methods. It includes different techniques presented in literature to improve the numerical stability: The logarithmic-quadratic distance function constitutes an interior point effect which allows to treat the auxiliary problems as unconstrained ones. Furthermore, outer operator approximations are considered. This simplifies the numerical solution of variational inequalities with multi-valued operators since, for example, bundle-techniques can be applied. With respect to the numerical practicability, inexact solutions of the auxiliary problems are allowed using a summable-error criterion that is easy to implement. As a further advantage of the logarithmic-quadratic distance we verify that it is self-concordant (in the sense of Nesterov/Nemirovskii). This motivates to apply the Newton method for the solution of the auxiliary problems. In the numerical part of the thesis the LQPAP method is applied to linearly constrained, differentiable and nondifferentiable convex optimization problems, as well as to nonsymmetric variational inequalities with co-coercive operators. It can often be observed that the sequence of iterates reaches the boundary of the feasible set before being close to an optimal solution. Against this background, we present the strategy of under-relaxation, which robustifies the LQPAP method. Furthermore, we compare the results with an appropriate method based on Bregman distances (BrPAP method). For differentiable, convex optimization problems we describe the implementation of the Newton method to solve the auxiliary problems and carry out different numerical experiments. For example, an adaptive choice of the initial regularization parameter and a combination of an Armijo and a self-concordance step size are evaluated. Test examples for nonsymmetric variational inequalities are hardly available in literature. Therefore, we present a geometric and an analytic approach to generate test examples with known solution(s). To solve the auxiliary problems in the case of nondifferentiable, convex optimization problems we apply the well-known bundle technique. The implementation is described in detail and the involved functions and sequences of parameters are discussed. As far as possible, our analysis is substantiated by new theoretical results. Furthermore, it is explained in detail how the bundle auxiliary problems are solved with a primal-dual interior point method. Such investigations have by now only been published for Bregman distances. The LQPAP bundle method is again applied to several test examples from literature. Thus, this thesis builds a bridge between theoretical and numerical investigations of solution methods for variational inequalities.
The role of cortisol and cortisol dynamics in patients after aneurysmal subarachnoid hemorrhage
(2011)
Spontaneous aneurysmal subarachnoid hemorrhage (SAH) is a form of stroke which constitutes a severe trauma to the brain and often leads to serious long-term medical and psychosocial sequels which persist for years after the acute event. Recently, adrenocorticotrophic hormone deficiency has been identified as one possible consequence of the bleeding and is assumed to occur in around 20% of all survivors. Additionally, a number of studies report a high prevalence of post-SAH symptoms such as lack of initiative, fatigue, loss of concentration, impaired quality of life and psychiatric symptoms such as depression. The overlap of these symptoms and those of patients with untreated partial or complete hypopituitarism lead to the suggestion that neuroendocrine dysregulations may contribute to the psychosocial sequels of SAH. Therefore, one of the aims of this work is to gain insights into the role of neuroendocrine dysfunction on quality of life and the prevalence of psychiatric sequels in SAH-patients. Additionally, as data on cortisol dynamics after SAH are scarce, diurnal cortisol profiles are investigated in patients in the acute and chronic phase, as well as the cortisol awakening response and feedback sensitivity in the chronic phase after SAH. As a result, it can be shown that some SAH patients exhibit lower serum cortisol levels but at the same time a higher cortisol awakening response in saliva than healthy controls. Also, patients in the chronic phase after SAH do have a stable diurnal cortisol rhythm while there are disturbances in around 50% of all patients in the acute phase, leading to the conclusion that a single baseline measurement of cortisol is of no substantial use for diagnosing cortisol dysregulations in the acute phase after SAH. It is assumed that in SAH patients endocrine changes occur over time and that a combination of adrenal exhaustion and a subsequent downregulation of corticosteroid binding globulin may be the most probable causes for the dissociation of serum cortisol concentrations and salivary cortisol profiles in the investigated SAH patients. These changes may be an emergency response after SAH and, as elevated free cortisol levels are connected to a better psychosocial outcome in patients in the chronic phase after SAH, this reaction may even be adaptive.
The demand for reliable statistics has been growing over the past decades, because more and more political and economic decisions are based on statistics, e.g. regional planning, allocation of funds or business decisions. Therefore, it has become increasingly important to develop and to obtain precise regional indicators as well as disaggregated values in order to compare regions or specific groups. In general, surveys provide the information for these indicators only for larger areas like countries or administrative divisions. However, in practice, it is more interesting to obtain indicators for specific subdivisions like on NUTS 2 or NUTS 3 levels. The Nomenclature of Units for Territorial Statistics (NUTS) is a hierarchical system of the European Union used in statistics to refer to subdivisions of countries. In many cases, the sample information on such detailed levels is not available. Thus, there are projects such as the European Census, which have the goal to provide precise numbers on NUTS 3 or even community level. The European Census is conducted amongst others in Germany and Switzerland in 2011. Most of the participating countries use sample and register information in a combined form for the estimation process. The classical estimation methods of small areas or subgroups, such as the Horvitz-Thompson (HT) estimator or the generalized regression (GREG) estimator, suffer from small area-specific sample sizes which cause high variances of the estimates. The application of small area methods, for instance the empirical best linear unbiased predictor (EBLUP), reduces the variance of the estimates by including auxiliary information to increase the effective sample size. These estimation methods lead to higher accuracy of the variables of interest. Small area estimation is also used in the context of business data. For example during the estimation of the revenues of specific subgroups like on NACE 3 or NACE 4 levels, small sample sizes can occur. The Nomenclature statistique des activités économiques dans la Communauté européenne (NACE) is a system of the European Union which defines an industry standard classification. Besides small sample sizes, business data have further special characteristics. The main challenge is that business data have skewed distributions with a few large companies and many small businesses. For instance, in the automotive industry in Germany, there are many small suppliers but only few large original equipment manufacturers (OEM). Altogether, highly influential units and outliers can be observed in business statistics. These extreme values in connection with small sample sizes cause severe problems when standard small area models are applied. These models are generally based on the normality assumption, which does not hold in the case of outliers. One way to solve these peculiarities is to apply outlier robust small area methods. The availability of adequate covariates is important for the accuracy of the above described small area methods. However, in business data, the auxiliary variables are hardly available on population level. One of several reasons for that is the fact that in Germany a lot of enterprises are not reflected in business registers due to truncation limits. Furthermore, only listed enterprises or companies which trespass specific thresholds are obligated to publish their results. This limits the number of potential auxiliary variables for the estimation. Even though there are issues with available covariates, business data often include spatial dependencies which can be used to enhance small area methods. Next to spatial information based on geographic characteristics, group-specific similarities like related industries based on NACE codes can be used. For instance, enterprises from the same NACE 2 level, e.g. sector 47 retail trade, behave more similar than two companies from different NACE 2 levels, e.g. sector 05 mining of coal and sector 64 financial services. This spatial correlation can be incorporated by extending the general linear mixed model trough the integration of spatially correlated random effects. In business data, outliers as well as geographic or content-wise spatial dependencies between areas or domains are closely linked. The coincidence of these two factors and the resulting consequences have not been fully covered in the relevant literature. The only approach that combines robust small area methods with spatial dependencies is the M-quantile geographically weighted regression model. In the context of EBLUP-based small area models, the combination of robust and spatial methods has not been considered yet. Therefore, this thesis provides a theoretical approach to this scientific and practical problem and shows its relevance in an empirical study.
There is a lot of evidence for the impact of acute glucocorticoid treatment on hippocampus-dependent explicit learning and memory (memory for facts and events). But there have been few studies, investigating the effect of glucocorticoids on implicit learning and memory. We conducted three studies with different methodology to investigate the effect of glucocorticoids on different forms of implicit learning. In Study 1, we investigated the effect of cortisol depletion on short-term habituation in 49 healthy subjects. 25 participants received oral metyrapone (1500 mg) to suppress endogenous cortisol production, while 24 controls received oral placebo. Eye blink electromyogram (EMG) responses to 105 dB acoustic startle stimuli were assessed. Effective endogenous cortisol suppression had no effect on short-term habituation of the startle reflex, but startle eye blink responses were significantly increased in the metyrapone group. The latter findings are in line with previous human studies, which have shown that excess cortisol, sufficient to fully occupy central nervous system (CNS) corticosteroid receptors, may reduce startle eye blink. This effect may be mediated by CNS mechanisms controlling cortisol feedback. In Study 2, we investigated delay or trace eyeblink conditioning in a patient group with a relative hypocortisolism (30 patients with fibromyaligia syndrome/FMS) compared to 20 healthy control subjects. Conditioned eyeblink response probability was assessed by EMG. Morning cortisol levels, ratings of depression, anxiety and psychosomatic complaints as well as general symptomatology and psychological distress were assessed. As compared to healthy controls FMS patients showed lower morning cortisol levels, and trace eyeblink conditioning was facilitated whereas delay eyeblink conditioning was reduced. Cortisol measures correlate significantly only with trace eyeblink conditioning. Our results are in line with studies of pharmacologically induced hyper- and hypocortisolism, which affected trace eyeblink conditioning. We suggest that endocrine mechanisms affecting hippocampus-mediated forms of associative learning may play a role in the generation of symptoms in these patients.rnIn Study 3, we investigated the effect of excess cortisol on implicit sequence learning in healthy subjects. Oral cortisol (30 mg) was given to 29 participants, whereas 31 control subjects received placebo. All volunteers performed a 5-choice serial reaction time task (SRTT). The reaction speed of every button-press was determined and difference-scores were calculated as a proof of learning. Compared to the control group, we found a delayed learning in the cortisol group at the very beginning of the task. This study is the first human investigation, indicating impaired implicit memory function after exogenous administration of the stress hormone cortisol. Our findings support a previous neuroimaging study, which suggested that the medial temporal lobe (including the hippocampus) is also active in implicit sequence learning, but our results may also depend on the engagement of other brain structures.