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Institut
- Institut für Biochemie und Biologie (43)
- Extern (27)
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- Institut für Geowissenschaften (19)
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- Institut für Ernährungswissenschaft (10)
- Institut für Umweltwissenschaften und Geographie (9)
- Wirtschaftswissenschaften (8)
The main goal of this dissertation is to experimentally investigate how focus is realised, perceived, and processed by native Turkish speakers, independent of preconceived notions of positional restrictions. Crucially, there are various issues and scientific debates surrounding focus in the Turkish language in the existing literature (chapter 1). It is argued in this dissertation that two factors led to the stagnant literature on focus in Turkish: the lack of clearly defined, modern understandings of information structure and its fundamental notion of focus, and the ongoing and ill-defined debate surrounding the question of whether there is an immediately preverbal focus position in Turkish. These issues gave rise to specific research questions addressed across this dissertation. Specifically, we were interested in how the focus dimensions such as focus size (comparing narrow constituent and broad sentence focus), focus target (comparing narrow subject and narrow object focus), and focus type (comparing new-information and contrastive focus) affect Turkish focus realisation and, in turn, focus comprehension when speakers are provided syntactic freedom to position focus as they see fit.
To provide data on these core goals, we presented three behavioural experiments based on a systematic framework of information structure and its notions (chapter 2): (i) a production task with trigger wh-questions and contextual animations manipulated to elicit the focus dimensions of interest (chapter 3), (ii) a timed acceptability judgment task in listening to the recorded answers in our production task (chapter 4), and (iii) a self-paced reading task to gather on-line processing data (chapter 5).
Based on the results of the conducted experiments, multiple conclusions are made in this dissertation (chapter 6). Firstly, this dissertation demonstrated empirically that there is no focus position in Turkish, neither in the sense of a strict focus position language nor as a focally loaded position facilitating focus perception and/or processing. While focus is, in fact, syntactically variable in the Turkish preverbal area, this is a consequence of movement triggered by other IS aspects like topicalisation and backgrounding, and the observational markedness of narrow subject focus compared to narrow object focus. As for focus type in Turkish, this dimension is not associated with word order in production, perception, or processing. Significant acoustic correlates of focus size (broad sentence focus vs narrow constituent focus) and focus target (narrow subject focus vs narrow object focus) were observed in fundamental frequency and intensity, representing focal boost, (postfocal) deaccentuation, and the presence or absence of a phrase-final rise in the prenucleus, while the perceivability of these effects remains to be investigated. In contrast, no acoustic correlates of focus type in simple, three-word transitive structures were observed, with focus types being interchangeable in mismatched question-answer pairs. Overall, the findings of this dissertation highlight the need for experimental investigations regarding focus in Turkish, as theoretical predictions do not necessarily align with experimental data. As such, the fallacy of implying causation from correlation should be strictly kept in mind, especially when constructions coincide with canonical structures, such as the immediately preverbal position in narrow object foci. Finally, numerous open questions remain to be explored, especially as focus and word order in Turkish are multifaceted. As shown, givenness is a confounding factor when investigating focus types, while thematic role assignment potentially confounds word order preferences. Further research based on established, modern information structure frameworks is needed, with chapter 5 concluding with specific recommendations for such future research.
Das kolonisierte Heiligtum
(2022)
Während der Zeit des historischen Kolonialismus wurden in Völkerkundemuseen komplexe Formen rassistischer und religiöser Diskriminierung institutionalisiert, z.B. in den dort gültigen Ästhetik- und Kunstbegriffen. Viele der heutigen Museumsangestellten erklären sich deswegen zu Reformen bereit. Doch können sie sich tatsächlich vom Kolonialismus trennen? Ist eine Dekolonisation ethnologischer Museen mit kolonialer Beute je abschließend möglich? Am Beispiel umstrittener Heiligtümer lebender Kulturen untersucht Christoph Balzar das Verfahren der Musealisierung durch die Linse der Diskriminierungskritik. Im Fokus stehen dabei die Sammlungen der »Staatlichen Museen zu Berlin«.
Over the past decades, there has been a growing interest in ‘extreme events’ owing to the increasing threats that climate-related extremes such as floods, heatwaves, droughts, etc., pose to society. While extreme events have diverse definitions across various disciplines, ranging from earth science to neuroscience, they are characterized mainly as dynamic occurrences within a limited time frame that impedes the normal functioning of a system. Although extreme events are rare in occurrence, it has been found in various hydro-meteorological and physiological time series (e.g., river flows, temperatures, heartbeat intervals) that they may exhibit recurrent behavior, i.e., do not end the lifetime of the system. The aim of this thesis to develop some
sophisticated methods to study various properties of extreme events.
One of the main challenges in analyzing such extreme event-like time series is that they have large temporal gaps due to the paucity of the number of observations of extreme events. As a result, existing time series analysis tools are usually not helpful to decode the underlying
information. I use the edit distance (ED) method to analyze extreme event-like time series in their unaltered form. ED is a specific distance metric, mainly designed to measure the similarity/dissimilarity between point process-like data. I combine ED with recurrence plot techniques to identify the recurrence property of flood events in the Mississippi River in the United States. I also use recurrence quantification analysis to show the deterministic properties
and serial dependency in flood events.
After that, I use this non-linear similarity measure (ED) to compute the pairwise dependency in extreme precipitation event series. I incorporate the similarity measure within the framework of complex network theory to study the collective behavior of climate extremes. Under this architecture, the nodes are defined by the spatial grid points of the given spatio-temporal climate dataset. Each node is associated with a time series corresponding to the temporal evolution
of the climate observation at that grid point. Finally, the network links are functions of the pairwise statistical interdependence between the nodes. Various network measures, such as degree, betweenness centrality, clustering coefficient, etc., can be used to quantify the network’s topology. We apply the methodology mentioned above to study the spatio-temporal coherence pattern of extreme rainfall events in the United States and the Ganga River basin, which reveals its relation to various climate processes and the orography of the region.
The identification of precursors associated with the occurrence of extreme events in the near future is extremely important to prepare the masses for an upcoming disaster and mitigate the potential risks associated with such events. Under this motivation, I propose an in-data prediction recipe for predicting the data structures that typically occur prior to extreme events using the Echo state network, a type of Recurrent Neural Network which is a part of the reservoir
computing framework. However, unlike previous works that identify precursory structures in the same variable in which extreme events are manifested (active variable), I try to predict these structures by using data from another dynamic variable (passive variable) which does not show large excursions from the nominal condition but carries imprints of these extreme events. Furthermore, my results demonstrate that the quality of prediction depends on the magnitude
of events, i.e., the higher the magnitude of the extreme, the better is its predictability skill. I show quantitatively that this is because the input signals collectively form a more coherent pattern for an extreme event of higher magnitude, which enhances the efficiency of the machine to predict the forthcoming extreme events.
Text is a ubiquitous entity in our world and daily life. We encounter it nearly everywhere in shops, on the street, or in our flats. Nowadays, more and more text is contained in digital images. These images are either taken using cameras, e.g., smartphone cameras, or taken using scanning devices such as document scanners. The sheer amount of available data, e.g., millions of images taken by Google Streetview, prohibits manual analysis and metadata extraction. Although much progress was made in the area of optical character recognition (OCR) for printed text in documents, broad areas of OCR are still not fully explored and hold many research challenges. With the mainstream usage of machine learning and especially deep learning, one of the most pressing problems is the availability and acquisition of annotated ground truth for the training of machine learning models because obtaining annotated training data using manual annotation mechanisms is time-consuming and costly. In this thesis, we address of how we can reduce the costs of acquiring ground truth annotations for the application of state-of-the-art machine learning methods to optical character recognition pipelines. To this end, we investigate how we can reduce the annotation cost by using only a fraction of the typically required ground truth annotations, e.g., for scene text recognition systems. We also investigate how we can use synthetic data to reduce the need of manual annotation work, e.g., in the area of document analysis for archival material. In the area of scene text recognition, we have developed a novel end-to-end scene text recognition system that can be trained using inexact supervision and shows competitive/state-of-the-art performance on standard benchmark datasets for scene text recognition. Our method consists of two independent neural networks, combined using spatial transformer networks. Both networks learn together to perform text localization and text recognition at the same time while only using annotations for the recognition task. We apply our model to end-to-end scene text recognition (meaning localization and recognition of words) and pure scene text recognition without any changes in the network architecture.
In the second part of this thesis, we introduce novel approaches for using and generating synthetic data to analyze handwriting in archival data. First, we propose a novel preprocessing method to determine whether a given document page contains any handwriting. We propose a novel data synthesis strategy to train a classification model and show that our data synthesis strategy is viable by evaluating the trained model on real images from an archive. Second, we introduce the new analysis task of handwriting classification. Handwriting classification entails classifying a given handwritten word image into classes such as date, word, or number. Such an analysis step allows us to select the best fitting recognition model for subsequent text recognition; it also allows us to reason about the semantic content of a given document page without the need for fine-grained text recognition and further analysis steps, such as Named Entity Recognition. We show that our proposed approaches work well when trained on synthetic data. Further, we propose a flexible metric learning approach to allow zero-shot classification of classes unseen during the network’s training. Last, we propose a novel data synthesis algorithm to train off-the-shelf pixel-wise semantic segmentation networks for documents. Our data synthesis pipeline is based on the famous Style-GAN architecture and can synthesize realistic document images with their corresponding segmentation annotation without the need for any annotated data!
Core-shell upconversion nanoparticles - investigation of dopant intermixing and surface modification
(2022)
Frequency upconversion nanoparticles (UCNPs) are inorganic nanocrystals capable to up-convert incident photons of the near-infrared electromagnetic spectrum (NIR) into higher energy photons. These photons are re-emitted in the range of the visible (Vis) and even ultraviolet (UV) light. The frequency upconversion process (UC) is realized with nanocrystals doped with trivalent lanthanoid ions (Ln(III)). The Ln(III) ions provide the electronic (excited) states forming a ladder-like electronic structure for the Ln(III) electrons in the nanocrystals. The absorption of at least two low energy photons by the nanoparticle and the subsequent energy transfer to one Ln(III) ion leads to the promotion of one Ln(III) electron into higher excited electronic states. One high energy photon will be emitted during the radiative relaxation of the electron in the excited state back into the electronic ground state of the Ln(III) ion. The excited state electron is the result of the previous absorption of at least two low energy photons.
The UC process is very interesting in the biological/medical context. Biological samples (like organic tissue, blood, urine, and stool) absorb high-energy photons (UV and blue light) more strongly than low-energy photons (red and NIR light). Thanks to a naturally occurring optical window, NIR light can penetrate deeper than UV light into biological samples. Hence, UCNPs in bio-samples can be excited by NIR light. This possibility opens a pathway for in vitro as well as in vivo applications, like optical imaging by cell labeling or staining of specific organic tissue. Furthermore, early detection and diagnosis of diseases by predictive and diagnostic biomarkers can be realized with bio-recognition elements being labeled to the UCNPs. Additionally, "theranostic" becomes possible, in which the identification and the treatment of a disease are tackled simultaneously.
For this to succeed, certain parameters for the UCNPs must be met: high upconversion efficiency, high photoluminescence quantum yield, dispersibility, and dispersion stability in aqueous media, as well as availability of functional groups to introduce fast and easy bio-recognition elements. The UCNPs used in this work were prepared with a solvothermal decomposition synthesis yielding in particles with NaYF4 or NaGdF4 as host lattice. They have been doped with the Ln(III) ions Yb3+ and Er3+, which is only one possible upconversion pair. Their upconversion efficiency and photoluminescence quantum yield were improved by adding a passivating shell to reduce surface quenching.
However, the brightness of core-shell UCNPs stays behind the expectations compared to their bulk material (being at least μm-sized particles). The core-shell structures are not clearly separated from each other, which is a topic in literature. Instead, there is a transition layer between the core and the shell structure, which relates to the migration of the dopants within the host lattice during the synthesis. The ion migration has been examined by time-resolved laser spectroscopy and the interlanthanoid resonance energy transfer (LRET) in the two different host lattices from above. The results are
presented in two publications, which dealt with core-shell-shell structured nanoparticles. The core is doped with the LRET-acceptor (either Nd3+ or Pr3+). The intermediate shell serves as an insulation shell of pure host lattice material, whose shell thickness has been varied within one set of samples having the same composition, so that the spatial separation of LRET-acceptor and -donor changes. The outer shell with the same host lattice is doped with the LRET-donor (Eu3+). The effect of the increasing insulation shell thickness is significant, although the LRET cannot be suppressed completely.
Next to the Ln(III) migration within a host lattice, various phase transfer reactions were investigated in order to subsequently perform surface modifications for bioapplications. One result out of this research has been published using a promising ligand, that equips the UCNP with bio-modifiable groups and has good potential for bio-medical applications. This particular ligand mimics natural occurring mechanisms of mussel protein adhesion and of blood coagulation, which is why the UCNPs are encapsulated very effectively. At the same time, bio-functional groups are introduced. In a proof-of-concept, the encapsulated UCNP has been coupled successfully with a dye (which is representative for a biomarker) and the system’s photoluminescence properties have been investigated.
In der fortwährenden Diskussion zur Familienbesteuerung bewertet der Autor vor dem Hintergrund des deutschen Verfassungsrechts die geltenden Regelungen und mögliche Reformmodelle rechtlich, steuerpolitisch und ökonomisch. Unter dem besonderen Blickwinkel eines seit mehr als 20 Jahren tätigen Steuerberaters kann der Autor die Wirkung des Splittingtarifs und deren Alternativen ökonomisch gut durchdringen.
Die Ehe und Familie stehen unter dem besonderen Schutz der deutschen staatlichen Ordnung. Diese Schutzpflicht aus Art. 6 Abs. 1 GG geht deutlich über ein Abwehrrecht hinaus. Sie gebietet es, die Ehe zu schützen, ihr einen grundsätzlich unantastbaren Kernbereich privater Lebensführung zu gewähren und innerfamiliär staatlicherseits keine bestimmte innereheliche Arbeits- und Aufgabenverteilung vorzuschreiben.
Die Ehe wird häufig als „Keimzelle“ jeglicher menschlichen Gesellschaft bezeichnet. Das erscheint folgerichtig und historisch bedeutsam. Im heutigen Zeitalter jedoch ist sie mehr oder weniger eine Erwerbs-, Konsum- und Verantwortungsgemeinschaft. Insofern stellt sich vornehmlich die Frage nach einer genügenden Rechtfertigung für die Gewährung eines Splittingtarifs.
Ausgangspunkt der umfassenden systematischen Darstellung ist die historischen Entwicklung in Deutschland und den angrenzenden Staaten; insbesondere der heutigen Europäischen Union. Denn in anderen Staaten werden bereits unterschiedliche Modelle wie beispielsweise die Einzel- oder Familienbesteuerung angewendet.
Verfassungsrechtlich hat der Gesetzgeber Veränderungen der gesellschaftlichen Rahmenbedingungen in und für die Familie in seine gesetzgeberischen Entscheidungen zwingend einzubeziehen. Es stellt sich demnach die Frage, ob beispielsweise seit der Entscheidung des Bundesverfassungsgerichts zum Ehegattensplitting im Jahr 1957 relevante Veränderungen eingetreten sind, die eine Änderung der Familienbesteuerung rechtfertigen oder gar erfordern. Die zwischenzeitliche Anerkennung von Lebenspartnerschaften und die Ehe für alle sind Ausdruck einer solchen Veränderung. Sie implizieren die bereits eingetretenen Veränderungen der Lebensverhältnisse. Daher ist die politische Diskussion für eine Veränderung nachvollziehbar. Ob jedoch Alternativen vorzugswürdig sind, wird ausführlich dargestellt.
Pensionskassen
(2022)
Die Kapitalanlage von Pensionskassen in Investmentfonds wird dadurch erschwert, dass sich die sie betreffenden aufsichts- und steuerrechtlichen Normen widersprechen. Die den Pensionskassen aufsichtsrechtlich zugestandene Kaitalanlage, wird durch die aktuelle Lesart ihrer Steuerbefreiung beschränkt. Die Steuerbefreiung soll demnach entfallen, soweit die Pensionskasse durch die Kapitalanlage gewerbliche Einkünfte erzielt. Dann sei die dauernde Einkünfte- und Vermögensbindung für die Zwecke der Kasse nach § 5 Abs. 1 Nr. 3 lit. c KStG nicht gesichert. Das benachteiligt die Pensionskassen bei der Erwirtschaftung von Rentenleistungen. Zugleich findet diese Lesart bei der Auslegung der entsprechenden steuerrechtlichen Normen keinen Zuspruch.
Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users’ Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users’ private traits from what they share and act on different social networking platforms.
In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user’s personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users’ long-term personality development and further unlock the potential of cooperation between psychologists and data scientists.
Die Zeit um 1600 markiert eine Zäsur in der Ausbildung junger Adliger, da neben der etablierten humanistischen Ausbildung zunehmend moderne Fremdsprachen und die institutionalisierte Ausbildung standesspezifischer Inhalte wie Fechten, Tanzen und Reiten an Bedeutung gewannen. Im vorliegenden Band werden die Ausbildungsgänge der Söhne dreier südwestdeutscher Freiherren- und Grafenfamilien in der Zeit des Späthumanismus untersucht. Dabei werden standesspezifische Bildungsstrategien herausgearbeitet und familiäre sowie konfessionelle Unterschiede aufgezeigt. Die Familien der Reichsgrafen orientierten sich – soweit es ihr Budget zuließ – einerseits am Fürstenstand, andererseits waren sie bestrebt, eigene Akzente zu setzen, sei es aus standespolitischen Erwägungen oder, weil sie eigenen Traditionen verpflichtet waren. Die systematische Auswertung breiter Quellenbestände förderte dabei auch eine Fülle an Informationen zu weiteren Wissensgebieten wie etwa der Reise-, Medizin-, Musikgeschichte oder der Alltagskultur zutage.