Inflation modeling is a very important tool for conducting an efficient monetary policy. This doctoral thesis reviewed inflation models, in particular the Phillips curve models of inflation dynamics. We focused on a well known and widely used model, the so-called three equation new Keynesian model which is a system of equations consisting of a new Keynesian Phillips curve (NKPC), an investment and saving (IS) curve and an interest rate rule.
We gave a detailed derivation of these equations. The interest rate rule used in this model is normally determined by using a Lagrangian method to solve an optimal control problem constrained by a standard discrete time NKPC which describes the inflation dynamics and an IS curve that represents the output gaps dynamics. In contrast to the real world, this method assumes that the policy makers intervene continuously. This means that the costs resulting from the change in the interest rates are ignored. We showed also that there are approximation errors made, when one log-linearizes non linear equations, by doing the derivation of the standard discrete time NKPC.
We agreed with other researchers as mentioned in this thesis, that errors which result from ignoring such log-linear approximation errors and the costs of altering interest rates by determining interest rate rule, can lead to a suboptimal interest rate rule and hence to non-optimal paths of output gaps and inflation rate.
To overcome such a problem, we proposed a stochastic optimal impulse control method. We formulated the problem as a stochastic optimal impulse control problem by considering the costs of change in interest rates and the approximation error terms. In order to formulate this problem, we first transform the standard discrete time NKPC and the IS curve into their high-frequency versions and hence into their continuous time versions where error terms are described by a zero mean Gaussian white noise with a finite and constant variance. After formulating this problem, we use the quasi-variational inequality approach to solve analytically a special case of the central bank problem, where an inflation rate is supposed to be on target and a central bank has to optimally control output gap dynamics. This method gives an optimal control band in which output gap process has to be maintained and an optimal control strategy, which includes the optimal size of intervention and optimal intervention time, that can be used to keep the process into the optimal control band.
Finally, using a numerical example, we examined the impact of some model parameters on optimal control strategy. The results show that an increase in the output gap volatility as well as in the fixed and proportional costs of the change in interest rate lead to an increase in the width of the optimal control band. In this case, the optimal intervention requires the central bank to wait longer before undertaking another control action.
In this thesis, mathematical research questions related to recursive utility and stochastic differential utility (SDU) are explored.
First, a class of backward equations under nonlinear expectations is investigated: Existence and uniqueness of solutions are established, and the issues of stability and discrete-time approximation are addressed. It is then shown that backward equations of this class naturally appear as a continuous-time limit in the context of recursive utility with nonlinear expectations.
Then, the Epstein-Zin parametrization of SDU is studied. The focus is on specifications with both relative risk aversion and elasitcity of intertemporal substitution greater that one. A concave utility functional is constructed and a utility gradient inequality is established.
Finally, consumption-portfolio problems with recursive preferences and unspanned risk are investigated. The investor's optimal strategies are characterized by a specific semilinear partial differential equation. The solution of this equation is constructed by a fixed point argument, and a corresponding efficient and accurate method to calculate optimal strategies numerically is given.
This thesis deals with risk measures based on utility functions and time consistency of dynamic risk measures. It is therefore aimed at readers interested in both, the theory of static and dynamic financial risk measures in the sense of Artzner, Delbaen, Eber and Heath ,  and the theory of preferences in the tradition of von Neumann and Morgenstern .
A main contribution of this thesis is the introduction of optimal expected utility (OEU) risk measures as a new class of utility-based risk measures. We introduce OEU, investigate its main properties, and its applicability to risk measurement and put it in perspective to alternative risk measures and notions of certainty equivalents. To the best of our knowledge, OEU is the only existing utility-based risk measure that is (non-trivial and) coherent if the utility function u has constant relative risk aversion. We present several different risk measures that can be derived with special choices of u and illustrate that OEU reacts in a more sensitive way to slight changes of the probability of a financial loss than value at risk (V@R) and average value at risk.
Further, we propose implied risk aversion as a coherent rating methodology for retail structured products (RSPs). Implied risk aversion is based on optimal expected utility risk measures and, in contrast to standard V@R-based ratings, takes into account both the upside potential and the downside risks of such products. In addition, implied risk aversion is easily interpreted in terms of an individual investor's risk aversion: A product is attractive (unattractive) for an investor if its implied risk aversion is higher (lower) than his individual risk aversion. We illustrate this approach in a case study with more than 15,000 warrants on DAX ® and find that implied risk aversion is able to identify favorable products; in particular, implied risk aversion is not necessarily increasing with respect to the strikes of call warrants.
Another main focus of this thesis is on consistency of dynamic risk measures. To this end, we study risk measures on the space of distributions, discuss concavity on the level of distributions and slightly generalize Weber's  findings on the relation of time consistent dynamic risk measures to static risk measures to the case of dynamic risk measures with time-dependent parameters. Finally, this thesis investigates how recursively composed dynamic risk measures in discrete time, which are time consistent by construction, can be related to corresponding dynamic risk measures in continuous time. We present different approaches to establish this link and outline the theoretical basis and the practical benefits of this relation. The thesis concludes with a numerical implementation of this theory.
Today, many publishers (e.g., websites, mobile application developers) commonly use third-party analytics services and social widgets. Unfortunately, this scheme allows these third parties to track individual users across the web, creating privacy concerns and leading to reactions to prevent tracking via blocking, legislation and standards. While improving user privacy, these efforts do not consider the functionality third-party tracking enables publishers to use: to obtain aggregate statistics about their users and increase their exposure to other users via online social networks. Simply preventing third-party tracking without replacing the functionality it provides cannot be a viable solution; leaving publishers without essential services will hurt the sustainability of the entire ecosystem.
Membrane proteins are generally soluble only in the presence of detergent micelles or other membrane-mimetic systems, which renders the determination of the protein’s molar mass or oligomeric state difficult. Moreover, the amount of bound detergent varies drastically among different proteins and detergents. However, the type of detergent and its concentration have a great influence on the protein’s structure, stability, and functionality and the success of structural and functional investigations and crystallographic trials. Size-exclusion chromatography, which is commonly used to determine the molar mass of water-soluble proteins, is not suitable for detergent-solubilised proteins because
the protein–detergent complex has a different conformation and, thus, commonly exhibits
a different migration behaviour than globular standard proteins. Thus, calibration curves obtained with standard proteins are not useful for membrane-protein analysis. However,
the combination of size-exclusion chromatography with ultraviolet absorbance, static light scattering, and refractive index detection provides a tool to determine the molar mass of protein–detergent complexes in an absolute manner and allows for distinguishing the contributions of detergent and protein to the complex.
The goal of this thesis was to refine the standard triple-detection size-exclusion chromatography measurement and data analysis procedure for challenging membrane-protein samples, non-standard detergents, and difficult solvents such as concentrated denaturant solutions that were thought to elude routine approaches. To this end, the influence of urea on the performance of the method beyond direct influences on detergents and proteins was investigated with the help of the water-soluble bovine serum albumin. On the basis of
the obtained results, measurement and data analysis procedures were refined for different detergents and protein–detergent complexes comprising the membrane proteins OmpLA and Mistic from Escherichia coli and Bacillus subtilis, respectively.
The investigations on mass and shape of different detergent micelles and the compositions of protein–detergent complexes in aqueous buffer and concentrated urea solutions
showed that triple-detection size-exclusion chromatography provides valuable information
about micelle masses and shapes under various conditions. Moreover, it is perfectly suited for the straightforward analysis of detergent-suspended proteins in terms of composition and oligomeric state not only under native but, more importantly, also under denaturing conditions.
Asynchronous programs are challenging to implement correctly: the loose coupling between asynchronously executed tasks makes the control and data dependencies difficult to follow. Even subtle design and programming mistakes on the programs have the capability to introduce erroneous or divergent behaviors. As asynchronous programs are typically written to provide a reliable, high-performance infrastructure, there is a critical need for analysis techniques to guarantee their correctness.
In this dissertation, I provide scalable verification and testing tools to make asyn- chronous programs more reliable. I show that the combination of counter abstraction and partial order reduction is an effective approach for the verification of asynchronous systems by presenting PROVKEEPER and KUAI, two scalable verifiers for two types of asynchronous systems. I also provide a theoretical result that proves a counter-abstraction based algorithm called expand-enlarge-check, is an asymptotically optimal algorithm for the coverability problem of branching vector addition systems as which many asynchronous programs can be modeled. In addition, I present BBS and LLSPLAT, two testing tools for asynchronous programs that efficiently uncover many subtle memory violation bugs.
The task of printed Optical Character Recognition (OCR), though considered ``solved'' by many, still poses several challenges. The complex grapheme structure of many scripts, such as Devanagari and Urdu Nastaleeq, greatly lowers the performance of state-of-the-art OCR systems.
Moreover, the digitization of historical and multilingual documents still require much probing. Lack of benchmark datasets further complicates the development of reliable OCR systems. This thesis aims to find the answers to some of these challenges using contemporary machine learning technologies. Specifically, the Long Short-Term Memory (LSTM) networks, have been employed to OCR modern as well historical monolingual documents. The excellent OCR results obtained on these have led us to extend their application for multilingual documents.
The first major contribution of this thesis is to demonstrate the usability of LSTM networks for monolingual documents. The LSTM networks yield very good OCR results on various modern and historical scripts, without using sophisticated features and post-processing techniques. The set of modern scripts include modern English, Urdu Nastaleeq and Devanagari. To address the challenge of OCR of historical documents, this thesis focuses on Old German Fraktur script, medieval Latin script of the 15th century, and Polytonic Greek script. LSTM-based systems outperform the contemporary OCR systems on all of these scripts. To cater for the lack of ground-truth data, this thesis proposes a new methodology, combining segmentation-based and segmentation-free OCR approaches, to OCR scripts for which no transcribed training data is available.
Another major contribution of this thesis is the development of a novel multilingual OCR system. A unified framework for dealing with different types of multilingual documents has been proposed. The core motivation behind this generalized framework is the human reading ability to process multilingual documents, where no script identification takes place.
In this design, the LSTM networks recognize multiple scripts simultaneously without the need to identify different scripts. The first step in building this framework is the realization of a language-independent OCR system which recognizes multilingual text in a single step. This language-independent approach is then extended to script-independent OCR that can recognize multiscript documents using a single OCR model. The proposed generalized approach yields low error rate (1.2%) on a test corpus of English-Greek bilingual documents.
In summary, this thesis aims to extend the research in document recognition, from modern Latin scripts to Old Latin, to Greek and to other ``under-privilaged'' scripts such as Devanagari and Urdu Nastaleeq.
It also attempts to add a different perspective in dealing with multilingual documents.
Cells and organelles are enclosed by membranes that consist of a lipid bilayer harboring highly
diverse membrane proteins (MPs). These carry out vital functions, and α-helical MPs, in
particular, are of outstanding pharmacological importance, as they comprise more than half of
all drug targets. However, knowledge from MP research is limited, as MPs require membranemimetic
environments to retain their native structures and functions and, thus, are not readily
amenable to in vitro studies. To gain insight into vectorial functions, as in the case of channels
and transporters, and into topology, which describes MP conformation and orientation in the
context of a membrane, purified MPs need to be reconstituted, that is, transferred from detergent
micelles into a lipid-bilayer system.
The ultimate goal of this thesis was to elucidate the membrane topology of Mistic, which is
an essential regulator of biofilm formation in Bacillus subtilis consisting of four α-helices. The
conformational stability of Mistic has been shown to depend on the presence of a hydrophobic
environment. However, Mistic is characterized by an uncommonly hydrophilic surface, and
its helices are significantly shorter than transmembrane helices of canonical integral MPs.
Therefore, the means by which its association with the hydrophobic interior of a lipid bilayer
is accomplished is a subject of much debate. To tackle this issue, Mistic was produced and
purified, reconstituted, and subjected to topological studies.
Reconstitution of Mistic in the presence of lipids was performed by lowering the detergent
concentration to subsolubilizing concentrations via addition of cyclodextrin. To fully exploit
the advantages offered by cyclodextrin-mediated detergent removal, a quantitative model was
established that describes the supramolecular state of the reconstitution mixture and allows
for the prediction of reconstitution trajectories and their cross points with phase boundaries.
Automated titrations enabled spectroscopic monitoring of Mistic reconstitutions in real time.
On the basis of the established reconstitution protocol, the membrane topology of Mistic was
investigated with the aid of fluorescence quenching experiments and oriented circular dichroism
spectroscopy. The results of these experiments reveal that Mistic appears to be an exception
from the commonly observed transmembrane orientation of α-helical MPs, since it exhibits
a highly unusual in-plane topology, which goes in line with recent coarse-grained molecular
Computer Vision (CV) problems, such as image classification and segmentation, have traditionally been solved by manual construction of feature hierarchies or incorporation of other prior knowledge. However, noisy images, varying viewpoints and lighting conditions of images, and clutters in real-world images make the problem challenging. Such tasks cannot be efficiently solved without learning from data. Therefore, many Deep Learning (DL) approaches have recently been successful for various CV tasks, for instance, image classification, object recognition and detection, action recognition, video classification, and scene labeling. The main focus of this thesis is to investigate a purely learning-based approach, particularly, Multi-Dimensional LSTM (MD-LSTM) recurrent neural networks to tackle the challenging CV tasks, classification and segmentation on 2D and 3D image data. Due to the structural nature of MD-LSTM, the network learns directly from raw pixel values and takes the complex spatial dependencies of each pixel into account. This thesis provides several key contributions in the field of CV and DL.
Several MD-LSTM network architectural options are suggested based on the type of input and output, as well as the requiring tasks. Including the main layers, which are an input layer, a hidden layer, and an output layer, several additional layers can be added such as a collapse layer and a fully connected layer. First, a single Two Dimensional LSTM (2D-LSTM) is directly applied on texture images for segmentation and show improvement over other texture segmentation methods. Besides, a 2D-LSTM layer with a collapse layer is applied for image classification on texture and scene images and have provided an accurate classification results. In addition, a deeper model with a fully connected layer is introduced to deal with more complex images for scene labeling and outperforms the other state-of-the-art methods including the deep Convolutional Neural Networks (CNN). Here, several input and output representation techniques are introduced to achieve the robust classification. Randomly sampled windows as input are transformed in scaling and rotation, which are integrated to get the final classification. To achieve multi-class image classification on scene images, several pruning techniques are introduced. This framework provides a good results in automatic web-image tagging. The next contribution is an investigation of 3D data with MD-LSTM. The traditional cuboid order of computations in Multi-Dimensional LSTM (MD-LSTM) is re-arranged in pyramidal fashion. The resulting Pyramidal Multi-Dimensional LSTM (PyraMiD-LSTM) is easy to parallelize, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM was tested on 3D biomedical volumetric images and achieved best known pixel-wise brain image segmentation results and competitive results on Electron Microscopy (EM) data for membrane segmentation.
To validate the framework, several challenging databases for classification and segmentation are proposed to overcome the limitations of current databases. First, scene images are randomly collected from the web and used for scene understanding, i.e., the web-scene image dataset for multi-class image classification. To achieve multi-class image classification, the training and testing images are generated in a different setting. For training, images belong to a single pre-defined category which are trained as a regular single-class image classification. However, for testing, images containing multi-classes are randomly collected by web-image search engine by querying the categories. All scene images include noise, background clutter, unrelated contents, and also diverse in quality and resolution. This setting can make the database possible to evaluate for real-world applications. Secondly, an automated blob-mosaics texture dataset generator is introduced for segmentation. Random 2D Gaussian blobs are generated and filled with random material textures. These textures contain diverse changes in illumination, scale, rotation, and viewpoint. The generated images are very challenging since they are even visually hard to separate the related regions.
Overall, the contributions in this thesis are major advancements in the direction of solving image analysis problems with Long Short-Term Memory (LSTM) without the need of any extra processing or manually designed steps. We aim at improving the presented framework to achieve the ultimate goal of accurate fine-grained image analysis and human-like understanding of images by machines.
Most of today’s wireless communication devices operate on unlicensed bands with uncoordinated spectrum access, with the consequence that RF interference and collisions are impairing the overall performance of wireless networks. In the classical design of network protocols, both packets in a collision are considered lost, such that channel access mechanisms attempt to avoid collisions proactively. However, with the current proliferation of wireless applications, e.g., WLANs, car-to-car networks, or the Internet of Things, this conservative approach is increasingly limiting the achievable network performance in practice. Instead of shunning interference, this thesis questions the notion of „harmful“ interference and argues that interference can, when generated in a controlled manner, be used to increase the performance and security of wireless systems. Using results from information theory and communications engineering, we identify the causes for reception or loss of packets and apply these insights to design system architectures that benefit from interference. Because the effect of signal propagation and channel fading, receiver design and implementation, and higher layer interactions on reception performance is complex and hard to reproduce by simulations, we design and implement an experimental platform for controlled interference generation to strengthen our theoretical findings with experimental results. Following this philosophy, we introduce and evaluate a system architecture that leverage interference.
First, we identify the conditions for successful reception of concurrent transmissions in wireless networks. We focus on the inherent ability of angular modulation receivers to reject interference when the power difference of the colliding signals is sufficiently large, the so-called capture effect. Because signal power fades over distance, the capture effect enables two or more sender–receiver pairs to transmit concurrently if they are positioned appropriately, in turn boosting network performance. Second, we show how to increase the security of wireless networks with a centralized network access control system (called WiFire) that selectively interferes with packets that violate a local security policy, thus effectively protecting legitimate devices from receiving such packets. WiFire’s working principle is as follows: a small number of specialized infrastructure devices, the guardians, are distributed alongside a network and continuously monitor all packet transmissions in the proximity, demodulating them iteratively. This enables the guardians to access the packet’s content before the packet fully arrives at the receiver. Using this knowledge the guardians classify the packet according to a programmable security policy. If a packet is deemed malicious, e.g., because its header fields indicate an unknown client, one or more guardians emit a limited burst of interference targeting the end of the packet, with the objective to introduce bit errors into it. Established communication standards use frame check sequences to ensure that packets are received correctly; WiFire leverages this built-in behavior to prevent a receiver from processing a harmful packet at all. This paradigm of „over-the-air“ protection without requiring any prior modification of client devices enables novel security services such as the protection of devices that cannot defend themselves because their performance limitations prohibit the use of complex cryptographic protocols, or of devices that cannot be altered after deployment.
This thesis makes several contributions. We introduce the first software-defined radio based experimental platform that is able to generate selective interference with the timing precision needed to evaluate the novel architectures developed in this thesis. It implements a real-time receiver for IEEE 802.15.4, giving it the ability to react to packets in a channel-aware way. Extending this system design and implementation, we introduce a security architecture that enables a remote protection of wireless clients, the wireless firewall. We augment our system with a rule checker (similar in design to Netfilter) to enable rule-based selective interference. We analyze the security properties of this architecture using physical layer modeling and validate our analysis with experiments in diverse environmental settings. Finally, we perform an analysis of concurrent transmissions. We introduce a new model that captures the physical properties correctly and show its validity with experiments, improving the state of the art in the design and analysis of cross-layer protocols for wireless networks.