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In recent years, the study of dynamical systems has developed into a central research area in mathematics. Actually, in combination with keywords such as "chaos" or "butterfly effect", parts of this theory have been incorporated in other scientific fields, e.g. in physics, biology, meteorology and economics. In general, a discrete dynamical system is given by a set X and a self-map f of X. The set X can be interpreted as the state space of the system and the function f describes the temporal development of the system. If the system is in state x ∈ X at time zero, its state at time n ∈ N is denoted by f^n(x), where f^n stands for the n-th iterate of the map f. Typically, one is interested in the long-time behaviour of the dynamical system, i.e. in the behaviour of the sequence (f^n(x)) for an arbitrary initial state x ∈ X as the time n increases. On the one hand, it is possible that there exist certain states x ∈ X such that the system behaves stably, which means that f^n(x) approaches a state of equilibrium for n→∞. On the other hand, it might be the case that the system runs unstably for some initial states x ∈ X so that the sequence (f^n(x)) somehow shows chaotic behaviour. In case of a non-linear entire function f, the complex plane always decomposes into two disjoint parts, the Fatou set F_f of f and the Julia set J_f of f. These two sets are defined in such a way that the sequence of iterates (f^n) behaves quite "wildly" or "chaotically" on J_f whereas, on the other hand, the behaviour of (f^n) on F_f is rather "nice" and well-understood. However, this nice behaviour of the iterates on the Fatou set can "change dramatically" if we compose the iterates from the left with just one other suitable holomorphic function, i.e. if we consider sequences of the form (g∘f^n) on D, where D is an open subset of F_f with f(D)⊂ D and g is holomorphic on D. The general aim of this work is to study the long-time behaviour of such modified sequences. In particular, we will prove the existence of holomorphic functions g on D having the property that the behaviour of the sequence of compositions (g∘f^n) on the set D becomes quite similarly chaotic as the behaviour of the sequence (f^n) on the Julia set of f. With this approach, we immerse ourselves into the theory of universal families and hypercyclic operators, which itself has developed into an own branch of research. In general, for topological spaces X, Y and a family {T_i: i ∈ I} of continuous functions T_i:X→Y, an element x ∈ X is called universal for the family {T_i: i ∈ I} if the set {T_i(x): i ∈ I} is dense in Y. In case that X is a topological vector space and T is a continuous linear operator on X, a vector x ∈ X is called hypercyclic for T if it is universal for the family {T^n: n ∈ N}. Thus, roughly speaking, universality and hypercyclicity can be described via the following two aspects: There exists a single object which allows us, via simple analytical operations, to approximate every element of a whole class of objects. In the above situation, i.e. for a non-linear entire function f and an open subset D of F_f with f(D)⊂ D, we endow the space H(D) of holomorphic functions on D with the topology of locally uniform convergence and we consider the map C_f:H(D)→H(D), C_f(g):=g∘f|_D, which is called the composition operator with symbol f. The transform C_f is a continuous linear operator on the Fréchet space H(D). In order to show that the above-mentioned "nice" behaviour of the sequence of iterates (f^n) on the set D ⊂ F_f can "change dramatically" if we compose the iterates from the left with another suitable holomorphic function, our aim consists in finding functions g ∈ H(D) which are hypercyclic for C_f. Indeed, for each hypercyclic function g for C_f, the set of compositions {g∘f^n|_D: n ∈ N} is dense in H(D) so that the sequence of compositions (g∘f^n|_D) is kind of "maximally divergent" " meaning that each function in H(D) can be approximated locally uniformly on D via subsequences of (g∘f^n|_D). This kind of behaviour stands in sharp contrast to the fact that the sequence of iterates (f^n) itself converges, behaves like a rotation or shows some "wandering behaviour" on each component of F_f. To put it in a nutshell, this work combines the theory of non-linear complex dynamics in the complex plane with the theory of dynamics of continuous linear operators on spaces of holomorphic functions. As far as the author knows, this approach has not been investigated before.
The reduction of information contained in model time series through the use of aggregating statistical performance measures is very high compared to the amount of information that one would like to draw from it for model identification and calibration purposes. It is readily known that this loss imposes important limitations on model identification and -diagnostics and thus constitutes an element of the overall model uncertainty as essentially different model realizations with almost identical performance measures (e.g. r-² or RMSE) can be generated. In three consecutive studies the present work proposes an alternative approach towards hydrological model evaluation based on the application of Self-Organizing Maps (SOM; Kohonen, 2001). The Self-Organizing Map is a type of artificial neural network and unsupervised learning algorithm that is used for clustering, visualization and abstraction of multidimensional data. It maps vectorial input data items with similar patterns onto contiguous locations of a discrete low-dimensional grid of neurons. The iterative training of the SOM causes the neurons to form a discrete, data-compressed representation of the high-dimensional input data. Using appropriate visualization techniques, information on distributions, patterns and relationships in complex data sets can be extracted. Irrespective of their potential, SOM applications have earned very little attention in hydrological modelling compared to other artificial neural network techniques. Therefore, the aim of the present work is to demonstrate that the application of Self-Organizing Maps has very high potential to address fundamental issues of model evaluation: It is shown that the clustering and classification of model time series by means of SOM can provide useful insights into model behaviour. In combination with the diagnostic properties of Signature Indices (Gupta et al., 2008; Yilmaz et al., 2008) SOM provides a novel tool for interpreting the model parameters in the hydrological context and identifying parameter sets that simultaneously meet multiple objectives, even if the corresponding model realizations belong to different models. Moreover, the presented studies and reviews also encourage further studies on the application of SOM in hydrological modelling.
Evidence points to autonomy as having a place next to affiliation, achievement, and power as one of the basic implicit motives; however, there is still some research that needs to be conducted to support this notion.
The research in this dissertation aimed to address this issue. I have specifically focused on two issues that help solidify the foundation of work that has already been conducted on the implicit autonomy motive, and will also be a foundation for future studies. The first issue is measurement. Implicit motives should be measured using causally valid instruments (McClelland, 1980). The second issue addresses the function of motives. Implicit motives orient, select, and energize behavior (McClelland, 1980). If autonomy is an implicit motive, then we need a valid instrument to measure it and we also need to show that it orients, selects, and energizes behavior.
In the following dissertation, I address these two issues in a series of ten studies. Firstly, I present studies that examine the causal validity of the Operant Motive Test (OMT; Kuhl, 2013) for the implicit affiliation and power motives using established methods. Secondly, I developed and empirically tested pictures to specifically assess the implicit autonomy motive and examined their causal validity. Thereafter, I present two studies that investigated the orienting and energizing effects of the implicit autonomy motive. The results of the studies solidified the foundation of the OMT and how it measures nAutonomy. Furthermore, this dissertation demonstrates that nAutonomy fulfills the criteria for two of the main functions of implicit motives. Taken together, the findings of this dissertation provide further support for autonomy as an implicit motive and a foundation for intriguing future studies.
The present thesis addresses the validity of Binge Eating Disorder (BED) as well as underlying mechanisms of BED from three different angles. Three studies provide data discriminating obesity with BED from obesity without BED. Study 1 demonstrates differences between obese individuals with and without BED regarding eating in the natural environment, psychiatric comorbidity, negative affect as well as self reported tendencies in eating behavior. Evidence for possible psychological mechanisms explaining increased intake of BED individuals in the natural environment was given by analyzing associations of negative affect, emotional eating, restrained eating and caloric intake in obese BED compared to NBED controls. Study 2 demonstrated stress-induced changes in the eating behavior of obese individuals with BED. The impact of a psychosocial stressor, the Trier Social Stress Test (TSST, Kirschbaum, Pirke, & Hellhammer, 1993), on behavioral patterns of eating behavior in laboratory was investigated. Special attention was given to stress-induced changes in variables that reflect mechanisms of appetite regulation in obese BED individuals compared to controls. To further explore by which mechanisms stress might trigger binge eating, study 3 investigated differences in stress-induced cortisol secretion after a socially evaluated cold pressure test (SECPT, Schwabe, Haddad, & Schachinger, 2008) in obese BED as compared to obese NBED individuals.
Der digitale Fortschritt der vergangenen Jahrzehnte beruht zu einem großen Teil auf der Innovationskraft junger aufstrebender Unternehmen. Während diese Unternehmen auf der einen Seite ihr hohes Maß an Innovativität eint, entsteht für diese zeitgleich auch ein hoher Bedarf an finanziellen Mitteln, um ihre geplanten Innovations- und Wachstumsziele auch in die Tat umsetzen zu können. Da diese Unternehmen häufig nur wenige bis keine Unternehmenswerte, Umsätze oder auch Profitabilität vorweisen können, gestaltet sich die Aufnahme von externem Kapital häufig schwierig bis unmöglich. Aus diesem Umstand entstand in der Mitte des zwanzigsten Jahrhunderts das Geschäftsmodell der Risikofinanzierung, des sogenannten „Venture Capitals“. Dabei investieren Risikokapitalgeber in aussichtsreiche junge Unternehmen, unterstützen diese in ihrem Wachstum und verkaufen nach einer festgelegten Dauer ihre Unternehmensanteile, im Idealfall zu einem Vielfachen ihres ursprünglichen Wertes. Zahlreiche junge Unternehmen bewerben sich um Investitionen dieser Risikokapitalgeber, doch nur eine sehr geringe Zahl erhält diese auch. Um die aussichtsreichsten Unternehmen zu identifizieren, sichten die Investoren die Bewerbungen anhand verschiedener Kriterien, wodurch bereits im ersten Schritt der Bewerbungsphase zahlreiche Unternehmen aus dem Kreis potenzieller Investmentobjekte ausscheiden. Die bisherige Forschung diskutiert, welche Kriterien Investoren zu einer Investition bewegen. Daran anschließend verfolgt diese Dissertation das Ziel, ein tiefergehendes Verständnis darüber zu erlangen, welche Faktoren die Entscheidungsfindung der Investoren beeinflussen. Dabei wird vor allem auch untersucht, wie sich persönliche Faktoren der Investoren, sowie auch der Unternehmensgründer, auf die Investitionsentscheidung auswirken. Ergänzt werden diese Untersuchungen zudem durch die Analyse der Wirkung des digitalen Auftretens von Unternehmensgründern auf die Entscheidungsfindung von Risikokapitalgebern. Des Weiteren verfolgt diese Dissertation als zweites Ziel einen Erkenntnisgewinn über die Auswirkungen einer erfolgreichen Investition auf den Unternehmensgründer. Insgesamt umfasst diese Dissertation vier Studien, die im Folgenden näher beschrieben werden.
In Kapitel 2 wird untersucht, inwiefern sich bestimmte Humankapitaleigenschaften des Investors auf dessen Entscheidungsverhalten auswirken. Mithilfe vorangegangener Interviews und Literaturrecherchen wurden insgesamt sieben Kriterien identifiziert, die Risikokapitalinvestoren in ihrer Entscheidungsfindung nutzen. Daraufhin nahmen 229 Investoren an einem Conjoint Experiment teil, mithilfe dessen gezeigt werden konnte, wie wichtig die jeweiligen Kriterien im Rahmen der Entscheidung sind. Von besonderem Interesse ist dabei, wie sich die Wichtigkeit der Kriterien in Abhängigkeit der Humankapitaleigenschaften der Investoren unterscheiden. Dabei kann gezeigt werden, dass sich die Wichtigkeit der Kriterien je nach Bildungshintergrund und Erfahrung der Investoren unterscheidet. So legen beispielsweise Investoren mit einem höheren Bildungsabschluss und Investoren mit unternehmerischer Erfahrung deutlich mehr Wert auf die internationale Skalierbarkeit der Unternehmen. Zudem unterscheidet sich die Wichtigkeit der Kriterien auch in Abhängigkeit der fachlichen Ausbildung. So legen etwa Investoren mit einer fachlichen Ausbildung in Naturwissenschaften einen deutlich stärkeren Fokus auf den Mehrwert des Produktes beziehungsweise der Dienstleistung. Zudem kann gezeigt werden, dass Investoren mit mehr Investitionserfahrung die Erfahrung des Managementteams wesentlich wichtiger einschätzen als Investoren mit geringerer Investitionserfahrung. Diese Ergebnisse ermöglichen es Unternehmensgründern ihre Bewerbungen um eine Risikokapitalfinanzierung zielgenauer auszurichten, etwa durch eine Analyse des beruflichen Hintergrunds der potentiellen Investoren und eine damit einhergehende Anpassung der Bewerbungsunterlagen, zum Beispiel durch eine stärkere Schwerpunktsetzung besonders relevanter Kriterien.
Die in Kapitel 3 vorgestellte Studie bedient sich der Daten des gleichen Conjoint Experiments aus Kapitel 2, legt hierbei allerdings einen Fokus auf den Unterschied zwischen Investoren aus den USA und Investoren aus Kontinentaleuropa. Dazu wurden Subsamples kreiert, in denen 128 Experimentteilnehmer in den USA angesiedelt sind und 302 in Kontinentaleuropa. Die Analyse der Daten zeigt, dass US-amerikanische Investoren, im Vergleich zu Investoren in Kontinentaleuropa, einen signifikant stärkeren Fokus auf das Umsatzwachstum der Unternehmen legen. Zudem legen kontinentaleuropäische Investoren einen deutlich stärkeren Fokus auf die internationale Skalierbarkeit der Unternehmen. Um die Ergebnisse der Analyse besser interpretieren zu können, wurden diese im Anschluss mit vier amerikanischen und sieben europäischen Investoren diskutiert. Dabei bestätigen die europäischen Investoren die Wichtigkeit der hohen internationalen Skalierbarkeit aufgrund der teilweise geringen Größe europäischer Länder und dem damit zusammenhängenden Zwang, schnell international skalieren zu können, um so zufriedenstellende Wachstumsraten zu erreichen. Des Weiteren wurde der vergleichsweise geringere Fokus auf das Umsatzwachstum in Europa mit fehlenden Mitteln für eine schnelle Expansion begründet. Gleichzeitig wird der starke Fokus der US-amerikanischen Investoren auf Umsatzwachstum mit der höheren Tendenz zu einem Börsengang in den USA begründet, bei dem hohe Umsätze als Werttreiber dienen. Die Ergebnisse dieses Kapitels versetzen Unternehmensgründer in die Lage, ihre Bewerbung stärker an die wichtigsten Kriterien der potenziellen Investoren auszurichten, um so die Wahrscheinlichkeit einer erfolgreichen Investitionsentscheidung zu erhöhen. Des Weiteren bieten die Ergebnisse des Kapitels Investoren, die sich an grenzüberschreitenden syndizierten Investitionen beteiligen, die Möglichkeit, die Präferenzen der anderen Investoren besser zu verstehen und die Investitionskriterien besser auf potenzielle Partner abzustimmen.
Kapitel 4 untersucht ob bestimmte Charaktereigenschaften des sogenannten Schumpeterschen Entrepreneurs einen Einfluss auf die Wahrscheinlichkeit eines zweiten Risikokapitalinvestments haben. Dazu wurden von Gründern auf Twitter gepostete Nachrichten sowie Information von Investitionsrunden genutzt, die auf der Plattform Crunchbase zur Verfügung stehen. Insgesamt wurden mithilfe einer Textanalysesoftware mehr als zwei Millionen Tweets von 3313 Gründern analysiert. Die Ergebnisse der Studie deuten an, dass einige Eigenschaften, die typisch für Schumpetersche Gründer sind, die Chancen für eine weitere Investition erhöhen, während andere keine oder negative Auswirkungen haben. So erhöhen Gründer, die auf Twitter einen starken Optimismus sowie ihre unternehmerische Vision zur Schau stellen die Chancen auf eine zweite Risikokapitalfinanzierung, gleichzeitig werden diese aber durch ein zu starkes Streben nach Erfolg reduziert. Diese Ergebnisse haben eine hohe praktische Relevanz für Unternehmensgründer, die sich auf der Suche nach Risikokapital befinden. Diese können dadurch ihr virtuelles Auftreten („digital identity“) zielgerichteter steuern, um so die Wahrscheinlichkeit einer weiteren Investition zu erhöhen.
Abschließend wird in Kapitel 5 untersucht, wie sich die digitale Identität der Gründer verändert, nachdem diese eine erfolgreiche Risikokapitalinvestition erhalten haben. Dazu wurden sowohl Twitter-Daten als auch Crunchbase-Daten genutzt, die im Rahmen der Erstellung der Studie in Kapitel 4 erhoben wurden. Mithilfe von Textanalyse und Paneldatenregressionen wurden die Tweets von 2094 Gründern vor und nach Erhalt der Investition untersucht. Dabei kann gezeigt werden, dass der Erhalt einer Risikokapitalinvestition das Selbstvertrauen, die positiven Emotionen, die Professionalisierung und die Führungsqualitäten der Gründer erhöhen. Gleichzeitig verringert sich allerdings die Authentizität der von den Gründern verfassten Nachrichten. Durch die Verwendung von Interaktionseffekten kann zudem gezeigt werden, dass die Steigerung des Selbstvertrauens positiv durch die Reputation des Investors moderiert wird, während die Höhe der Investition die Authentizität negativ moderiert. Investoren haben durch diese Erkenntnisse die Möglichkeit, den Weiterentwicklungsprozess der Gründer nach einer erfolgreichen Investition besser nachvollziehen zu können, wodurch sie in die Lage versetzt werden, die Aktivitäten ihrer Gründer auf Social Media Plattformen besser zu kontrollieren und im Bedarfsfall bei ihrer Anpassung zu unterstützen.
Die in den Kapiteln 2 bis 5 vorgestellten Studien dieser Dissertation tragen damit zu einem besseren Verständnis der Entscheidungsfindung im Venture Capital Prozess bei. Der bisherige Stand der Forschung wird um Erkenntnisse erweitert, die sowohl den Einfluss der Eigenschaften der Investoren als auch der Gründer betreffen. Zudem wird auch gezeigt, wie sich die Investition auf den Gründer selbst auswirken kann. Die Implikationen der Ergebnisse, sowie Limitationen und Möglichkeiten künftiger Forschung werden in Kapitel 6 näher beschrieben. Da die in dieser Dissertation verwendeten Methoden und Daten erst seit wenigen Jahren im Kontext der Venture Capital Forschung genutzt werden, beziehungsweise überhaupt verfügbar sind, bietet sie sich als eine Grundlage für weitere Forschung an.
External capital plays an important role in financing entrepreneurial ventures, due to limited internal capital sources. An important external capital provider for entrepreneurial ventures are venture capitalists (VCs). VCs worldwide are often confronted with thousands of proposals of entrepreneurial ventures per year and must choose among all of these companies in which to invest. Not only do VCs finance companies at their early stages, but they also finance entrepreneurial companies in their later stages, when companies have secured their first market success. That is why this dissertation focuses on the decision-making behavior of VCs when investing in later-stage ventures. This dissertation uses both qualitative as well as quantitative research methods in order to provide answer to how the decision-making behavior of VCs that invest in later-stage ventures can be described.
Based on qualitative interviews with 19 investment professionals, the first insight gained is that for different stages of venture development, different decision criteria are applied. This is attributed to different risks and goals of ventures at different stages, as well as the different types of information available. These decision criteria in the context of later-stage ventures contrast with results from studies that focus on early-stage ventures. Later-stage ventures possess meaningful information on financials (revenue growth and profitability), the established business model, and existing external investors that is not available for early-stage ventures and therefore constitute new decision criteria for this specific context.
Following this identification of the most relevant decision criteria for investors in the context of later-stage ventures, a conjoint study with 749 participants was carried out to understand the relative importance of decision criteria. The results showed that investors attribute the highest importance to 1) revenue growth, (2) value-added of products/services for customers, and (3) management team track record, demonstrating differences when compared to decision-making studies in the context of early-stage ventures.
Not only do the characteristics of a venture influence the decision to invest, additional indirect factors, such as individual characteristics or characteristics of the investment firm, can influence individual decisions. Relying on cognitive theory, this study investigated the influence of various individual characteristics on screening decisions and found that both investment experience and entrepreneurial experience have an influence on individual decision-making behavior. This study also examined whether goals, incentive structures, resources, and governance of the investment firm influence decision making in the context of later-stage ventures. This study particularly investigated two distinct types of investment firms, family offices and corporate venture capital funds (CVC), which have unique structures, goals, and incentive systems. Additional quantitative analysis showed that family offices put less focus on high-growth firms and whether reputable investors are present. They tend to focus more on the profitability of a later-stage venture in the initial screening. The analysis showed that CVCs place greater importance on product and business model characteristics than other investors. CVCs also favor later-stage ventures with lower revenue growth rates, indicating a preference for less risky investments. The results provide various insights for theory and practice.
In order to investigate the psychobiological consequences of acute stress under laboratory conditions, a wide range of methods for socially evaluative stress induction have been developed. The present dissertation is concerned with evaluating a virtual reality (VR)-based adaptation of one of the most widely used of those methods, the Trier Social Stress Test (TSST). In the three empirical studies collected in this dissertation, we aimed to examine the efficacy and possible areas of application of the adaptation of this well-established psychosocial stressor in a virtual environment. We found that the TSST-VR reliably incites the activation of the major stress effector systems in the human body, albeit in a slightly less pronounced way than the original paradigm. Moreover, the experience of presence is discussed as one potential factor of influence in the origin of the psychophysiological stress response. Lastly, we present a use scenario for the TSST-VR in which we employed the method to investigate the effects of acute stress on emotion recognition performance. We conclude that, due to its advantages concerning versatility, standardization and economic administration, the paradigm harbors enormous potential not only for psychobiological research, but other applications such as clinical practice as well. Future studies should further explore the underlying effect mechanisms of stress in the virtual realm and the implementation of VR-based paradigms in different fields of application.
While humans find it easy to process visual information from the real world, machines struggle with this task due to the unstructured and complex nature of the information. Computer vision (CV) is the approach of artificial intelligence that attempts to automatically analyze, interpret, and extract such information. Recent CV approaches mainly use deep learning (DL) due to its very high accuracy. DL extracts useful features from unstructured images in a training dataset to use them for specific real-world tasks. However, DL requires a large number of parameters, computational power, and meaningful training data, which can be noisy, sparse, and incomplete for specific domains. Furthermore, DL tends to learn correlations from the training data that do not occur in reality, making DNNs poorly generalizable and error-prone.
Therefore, the field of visual transfer learning is seeking methods that are less dependent on training data and are thus more applicable in the constantly changing world. One idea is to enrich DL with prior knowledge. Knowledge graphs (KG) serve as a powerful tool for this purpose because they can formalize and organize prior knowledge based on an underlying ontological schema. They contain symbolic operations such as logic, rules, and reasoning, and can be created, adapted, and interpreted by domain experts. Due to the abstraction potential of symbols, KGs provide good prerequisites for generalizing their knowledge. To take advantage of the generalization properties of KG and the ability of DL to learn from large-scale unstructured data, attempts have long been made to combine explicit graph and implicit vector representations. However, with the recent development of knowledge graph embedding methods, where a graph is transferred into a vector space, new perspectives for a combination in vector space are opening up.
In this work, we attempt to combine prior knowledge from a KG with DL to improve visual transfer learning using the following steps: First, we explore the potential benefits of using prior knowledge encoded in a KG for DL-based visual transfer learning. Second, we investigate approaches that already combine KG and DL and create a categorization based on their general idea of knowledge integration. Third, we propose a novel method for the specific category of using the knowledge graph as a trainer, where a DNN is trained to adapt to a representation given by prior knowledge of a KG. Fourth, we extend the proposed method by extracting relevant context in the form of a subgraph of the KG to investigate the relationship between prior knowledge and performance on a specific CV task. In summary, this work provides deep insights into the combination of KG and DL, with the goal of making DL approaches more generalizable, more efficient, and more interpretable through prior knowledge.
The visualization of relational data is at the heart of information visualization. The prevalence of visual representations for this kind of data is based on many real world examples spread over many application domains: protein-protein interaction networks in the field of bioinformatics, hyperlinked documents in the World Wide Web, call graphs in software systems, or co-author networks are just four instances of a rich source of relational datasets. The most common visual metaphor for this kind of data is definitely the node-link approach, which typically suffers from visual clutter caused by many edge crossings. Many sophisticated algorithms have been developed to layout a graph efficiently and with respect to a list of aesthetic graph drawing criteria. Relations between objects normally change over time. Visualizing the dynamics means an additional challenge for graph visualization researchers. Applying the same layout algorithms for static graphs to intermediate states of dynamic graphs may also be a strategy to compute layouts for an animated graph sequence that shows the dynamics. The major drawback of this approach is the high cognitive effort for a viewer of the animation to preserve his mental map. To tackle this problem, a sophisticated layout algorithm has to inspect the whole graph sequence and compute a layout with as little changes as possible between subsequent graphs. The main contribution and ultimate goal of this thesis is the visualization of dynamic compound weighted multi directed graphs as a static image that targets at visual clutter reduction and at mental map preservation. To achieve this goal, we use a radial space-filling visual metaphor to represent the dynamics in relational data. As a side effect the obtained pictures are very aesthetically appealing. In this thesis we firstly describe static graph visualizations for rule sets obtained by extracting knowledge from software archives under version control. In a different work we apply animated node-link diagrams to code-developer relationships to show the dynamics in software systems. An underestimated visualization paradigm is the radial representation of data. Though this kind of data has a long history back to centuries-old statistical graphics, only little efforts have been done to fully explore the benefits of this paradigm. We evaluated a Cartesian and a radial counterpart of a visualization technique for visually encoding transaction sequences and dynamic compound digraphs with both an eyetracking and an online study. We found some interesting phenomena apart from the fact that also laymen in graph theory can understand the novel approach in a short time and apply it to datasets. The thesis is concluded by an aesthetic dimensions framework for dynamic graph drawing, future work, and currently open issues.
In her poems, Tawada constructs liminal speaking subjects – voices from the in-between – which disrupt entrenched binary thought processes. Synthesising relevant concepts from theories of such diverse fields as lyricology, performance studies, border studies, cultural and postcolonial studies, I develop ‘voice’ and ‘in-between space’ as the frameworks to approach Tawada’s multifaceted poetic output, from which I have chosen 29 poems and two verse novels for analysis. Based on the body speaking/writing, sensuality is central to Tawada’s use of voice, whereas the in-between space of cultures and languages serves as the basis for the liminal ‘exophonic’ voices in her work. In the context of cultural alterity, Tawada focuses on the function of language, both its effect on the body and its role in subject construction, while her feminist poetry follows the general development of feminist academia from emancipation to embodiment to queer representation. Her response to and transformation of écriture féminine in her verse novels transcends the concept of the body as the basis of identity, moving to literary and linguistic, plural self-construction instead. While few poems are overtly political, the speaker’s personal and contextual involvement in issues of social conflict reveal the poems’ potential to speak of, and to, the multiply identified citizens of a globalised world, who constantly negotiate physical as well as psychological borders.