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Synapses are the fundamental structures that regulate the functionality of the neural circuit. The ability of the synapse to modulate its structure and function at a fast rate due to various sensory inputs provides the strength to the nervous system to incorporate new adaptations and behaviors in the animal. The synapses are very dynamic throughout the life of the animal starting from early development. Continuous events of formation and elimination of synapse, activation and inhibition of synaptic function are observed in almost all synapses. These processes occur at a high speed and require controlled cellular mechanisms. Imbalance in these processes results in defective nervous system and has been reported in many neurological disorders. Thus, it is important to understand the mechanisms that regulate process of synapse development maintenance and function.
Kinases and phosphatases are the key regulators of cellular mechanisms. Understanding the function of these molecules in the neuron will shed light on the molecular mechanisms of synaptic plasticity. Using Drosophila melanogaster larval neuromuscular junction as a model, Bulat et al. (2014) performed a large RNAi based screen targeting kinome and phosphatome of Drosophila to identify the essential kinases and phosphatases and found Myeloid leukemia factor-1 adaptor molecule (Madm) and Protein phosphatase 4 (PP4) as novel regulators of synapse development and maintenance. The function of these molecules in the nervous system has not been reported and hence I investigated on the role of Madm and PP4 in the regulation of synapse development, maintenance and function.
Myeloid leukemia factor-1 adaptor molecule (Madm), a ubiquitously expressing psuedokinase essentially functions to regulate synaptic growth, stability and function. Using a combination of genetic and high throughput imaging, I could demonstrate that Madm functions to regulate the synaptic growth and stability from the presynapse and synaptic organization form the postsynapse. Also, I could demonstrate that Madm functions in association with mTOR pathway to regulate synapse growth acting downstream of 4E-BP. In addition, using electrophysiology, we could demonstrate that Madm is essential for the basic synaptic transmission with an additive function of retrograde synaptic potentiation. In summary, I could demonstrate that Madm is a novel regulator of synaptic development, maintenance and function.
Protein phosphatase 4 (PP4), a ubiquitously expressing protein phosphatase is involved in the regulation of multiple aspects of the nervous system. I could demonstrate that PP4 is essential for the development of nervous system and the metamorphosis. Using genetics and imaging analysis, I could demonstrate that loss of PP4 results in the abnormal morphology of cell organelles. In addition, I could show that loss of PP4 results in defective brain development with poorly developed structures.
Altogether, in this study, I could demonstrate the importance of novel molecules, a pesudokinase Madm and protein phosphatases PP4 in the nervous system to regulate distinct aspects of the neuron.
Wetted contacts play an important role in many fields. A prominent example in engineering is the lubricated contact between tool and workpiece in machining processes with cutting liquids. In such contacts, highly dynamic processes occur in the fluid at small length scales under extreme conditions regarding temperature, pressure, and shear. Experimental studies of these phenomena are generally not feasible. Thus, only little information on the actual processes in the contact zone is available. A tractable route for obtaining such information is molecular dynamics (MD) simulation. As input for these simulations, only a potential model that describes the interactions on the atomistic scale, is needed. On that basis also complex processes can be predicted. In the present work, a simple model potential was used, i.e. the Lennard-Jones truncated and shifted potential (LJTS), which was parameterized to describe the solids, the fluid, and their interactions. A novel method for determining fluid properties with non-equilibrium MD simulations was developed, which yields thermal, caloric and transport properties in a single simulation run. It can also be used for studying the influence of shear on these properties. With the new method, a comprehensive study of properties of the LJTS fluid was carried out. Furthermore, it was investigated how these fluid properties change near the solid-fluid interface and how these changes affect the conductive heat transfer between the solid and the fluid. Finally, a nanotribological process was studied, in which all these phenomena occur simultaneously.
Glycine constitutes the major neurotransmitter at inhibitory synapses of lower brain regions.
A rapid removal of glycine from the synaptic cleft and consequent recycling is crucial for
synaptic transmission in systems with high effort on temporal precision. This is mainly
achieved by glycine translocation via two glycine transporters (GlyTs), namely GlyT1 and
GlyT2. At inhibitory synapses, GlyT2 was found to be specifically expressed by neurons,
supplying the presynapse with glycine needed for vesicle filling. In contrast, GlyT1 is attributed
to astrocytes and primarily mediates the termination of synaptic transmission by glycine
removal from the synaptic cleft. Employing patch-clamp recordings from principal neurons of
the lateral superior olive (LSO) in acute brainstem slices of GlyT1b/c knockout (KO) mice and
wildtype (WT) littermates at postnatal day 20, I analyzed how postsynaptic responses are
changed in a GlyT1-depleted environment. During spontaneous vesicle release I found no
change of postsynaptic responses, contradicting my initial hypothesis of prolonged decay
times. Electrical stimulation of fibers of the medial nucleus of the trapezoid body (MNTB),
which are known to form fast, reliable and highly precise synapses with LSO principal neurons,
revealed that GlyT1 is involved in proper synaptic function during sustained, high frequent
synaptic transmission. Stimulation with 50 Hz led to a stronger decay time and latency
prolongation in GlyT1b/c KO, accelerating to 60% longer decay times and 30% longer latencies.
Additionally, a more pronounced frequency-dependent depression and fidelity decrease was
observed during stimulation with 200 Hz in GlyT1b/c KO, resulting in 67% smaller amplitudes
and only 25% of WT fidelity at the end of the challenge. Basic properties like readily releasable
pool, release probability, and quantal size (q) were not altered in GlyT1b/c KO, but
interestingly q decreased during 50 Hz and 100 Hz challenges to about 84%, which was not
observed in WT. I conclude that stronger accumulation of extracellular glycine due to GlyT1
loss leads to prolonged activation of postsynaptic glycine receptors (GlyRs). As a further
consequence, activation of presynaptic GlyRs in the vicinity of the synaptic cleft might be
enhanced, accompanied by a stronger occurrence of shunting inhibition. Furthermore, I
assume a GlyT1-dependent glycine shuttle, which is absent at GlyT1b/c KO synapses. This
could result in a diminished glycine supply to GlyT2 located at more distant sites, causing a
disturbed replenishment during periods with excess release of glycine. Conclusively, my study
reveals a contribution of astrocytes in fast and reliable synaptic transmission at the MNTB-LSO
synapse, which in turn is crucial for proper sound source localization.
Private data analytics systems preferably provide required analytic accuracy to analysts and specified privacy to individuals whose data is analyzed. Devising a general system that works for a broad range of datasets and analytic scenarios has proven to be difficult.
Despite the advent of differentially private systems with proven formal privacy guarantees, industry still uses inferior ad-hoc mechanisms that provide better analytic accuracy. Differentially private mechanisms often need to add large amounts of noise to statistical results, which impairs their usability.
In my thesis I follow two approaches to improve the usability of private data analytics systems in general and differentially private systems in particular. First, I revisit ad-hoc mechanisms and explore the possibilities of systems that do not provide Differential Privacy or only a weak version thereof. Based on an attack analysis I devise a set of new protection mechanisms including Query Based Bookkeeping (QBB). In contrast to previous systems QBB only requires the history of analysts’ queries in order to provide privacy protection. In particular, QBB does not require knowledge about the protected individuals’ data.
In my second approach I use the insights gained with QBB to propose UniTraX, the first differentially private analytics system that allows to analyze part of a protected dataset without affecting the other parts and without giving up on accuracy. I show UniTraX’s usability by way of multiple case studies on real-world datasets across different domains. UniTraX allows more queries than previous differentially private data analytics systems at moderate runtime overheads.
Model uncertainty is a challenge that is inherent in many applications of mathematical models in various areas, for instance in mathematical finance and stochastic control. Optimization procedures in general take place under a particular model. This model, however, might be misspecified due to statistical estimation errors and incomplete information. In that sense, any specified model must be understood as an approximation of the unknown "true" model. Difficulties arise since a strategy which is optimal under the approximating model might perform rather bad in the true model. A natural way to deal with model uncertainty is to consider worst-case optimization.
The optimization problems that we are interested in are utility maximization problems in continuous-time financial markets. It is well known that drift parameters in such markets are notoriously difficult to estimate. To obtain strategies that are robust with respect to a possible misspecification of the drift we consider a worst-case utility maximization problem with ellipsoidal uncertainty sets for the drift parameter and with a constraint on the strategies that prevents a pure bond investment.
By a dual approach we derive an explicit representation of the optimal strategy and prove a minimax theorem. This enables us to show that the optimal strategy converges to a generalized uniform diversification strategy as uncertainty increases.
To come up with a reasonable uncertainty set, investors can use filtering techniques to estimate the drift of asset returns based on return observations as well as external sources of information, so-called expert opinions. In a Black-Scholes type financial market with a Gaussian drift process we investigate the asymptotic behavior of the filter as the frequency of expert opinions tends to infinity. We derive limit theorems stating that the information obtained from observing the discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process which can be interpreted as a continuous-time expert. Our convergence results carry over to convergence of the value function in a portfolio optimization problem with logarithmic utility.
Lastly, we use our observations about how expert opinions improve drift estimates for our robust utility maximization problem. We show that our duality approach carries over to a financial market with non-constant drift and time-dependence in the uncertainty set. A time-dependent uncertainty set can then be defined based on a generic filter. We apply this to various investor filtrations and investigate which effect expert opinions have on the robust strategies.
In this dissertation we apply financial mathematical modelling to electricity markets. Electricity is different from any other underlying of financial contracts: it is not storable. This means that electrical energy in one time point cannot be transferred to another. As a consequence, power contracts with disjoint delivery time spans basically have a different underlying. The main idea throughout this thesis is exactly this two-dimensionality of time: every electricity contract is not only characterized by its trading time but also by its delivery time.
The basis of this dissertation are four scientific papers corresponding to the Chapters 3 to 6, two of which have already been published in peer-reviewed journals. Throughout this thesis two model classes play a significant role: factor models and structural models. All ideas are applied to or supported by these two model classes. All empirical studies in this dissertation are conducted on electricity price data from the German market and Chapter 4 in particular studies an intraday derivative unique to the German market. Therefore, electricity market design is introduced by the example of Germany in Chapter 1. Subsequently, Chapter 2 introduces the general mathematical theory necessary for modelling electricity prices, such as Lévy processes and the Esscher transform. This chapter is the mathematical basis of the Chapters 3 to 6.
Chapter 3 studies factor models applied to the German day-ahead spot prices. We introduce a qualitative measure for seasonality functions based on three requirements. Furthermore, we introduce a relation of factor models to ARMA processes, which induces a new method to estimate the mean reversion speed.
Chapter 4 conducts a theoretical and empirical study of a pricing method for a new electricity derivative: the German intraday cap and floor futures. We introduce the general theory of derivative pricing and propose a method based on the Hull-White model of interest rate modelling, which is a one-factor model. We include week futures prices to generate a price forward curve (PFC), which is then used instead of a fixed deterministic seasonality function. The idea that we can combine all market prices, and in particular futures prices, to improve the model quality also plays the major role in Chapter 5 and Chapter 6.
In Chapter 5 we develop a Heath-Jarrow-Morton (HJM) framework that models intraday, day-ahead, and futures prices. This approach is based on two stochastic processes motivated by economic interpretations and separates the stochastic dynamics in trading and delivery time. Furthermore, this framework allows for the use of classical day-ahead spot price models such as the ones of Schwartz and Smith (2000), Lucia and Schwartz (2002) and includes many model classes such as structural models and factor models.
Chapter 6 unifies the classical theory of storage and the concept of a risk premium through the introduction of an unobservable intrinsic electricity price. Since all tradable electricity contracts are derivatives of this actual intrinsic price, their prices should all be derived as conditional expectation under the risk-neutral measure. Through the intrinsic electricity price we develop a framework, which also includes many existing modelling approaches, such as the HJM framework of Chapter 5.
The main focus of the research lies in the interpretation and application of results and correlations of soil properties from in situ testing and subsequent use in terramechanical applications. The empirical correlations and current procedures were mainly developed for medium to large depths, and therefore they were re-evaluated and adjusted herein to reflect the current state of knowledge for the assessment of near-surface soil. For testing technologies, a field investigation to a moon analogue site was carried out. Focus was placed in the assessment of the near surface soil properties. Samples were collected for subsequent analysis in laboratory conditions. Further laboratory experiments in extraterrestrial soil simulants and other terrestrial soils were conducted and correlations with relative density and shear strength parameters were attempted. The correlations from the small scale laboratory experiments, and the new re-evaluated correlation for relative density were checked against the data from the field investigation. Additionally, single tire-soil tests were carried out, which enable the investigation of the localized soil response in order to advance current wheel designs and subsequently the vehicle’s mobility. Furthermore, numerical simulations were done to aid the investigation of the tire-soil interaction. Summing up, current relationships for estimating relative density of near surface soil were re-evaluated, and subsequently correlated to shear strength parameters that are the main input to model soil in numerical analyses. Single tire-soil tests were carried out and were used as a reference to calibrate the interaction of the tire and the soil and subsequently were utilized to model rolling scenarios which enable the assessment of soil trafficability and vehicle’s mobility.
Planar force or pressure is a fundamental physical aspect during any people-vs-people and people-vs-environment activities and interactions. It is as significant as the more established linear and angular acceleration (usually acquired by inertial measurement units). There have been several studies involving planar pressure in the discipline of activity recognition, as reviewed in the first chapter. These studies have shown that planar pressure is a promising sensing modality for activity recognition. However, they still take a niche part in the entire discipline, using ad hoc systems and data analysis methods. Mostly these studies were not followed by further elaborative works. The situation calls for a general framework that can help push planar pressure sensing into the mainstream.
This dissertation systematically investigates using planar pressure distribution sensing technology for ubiquitous and wearable activity recognition purposes. We propose a generic Textile Pressure Mapping (TPM) Framework, which encapsulates (1) design knowledge and guidelines, (2) a multi-layered tool including hardware, software and algorithms, and (3) an ensemble of empirical study examples. Through validation with various empirical studies, the unified TPM framework covers the full scope of application recognition, including the ambient, object, and wearable subspaces.
The hardware part constructs a general architecture and implementations in the large-scale and mobile directions separately. The software toolkit consists of four heterogeneous tiers: driver, data processing, machine learning, visualization/feedback. The algorithm chapter describes generic data processing techniques and a unified TPM feature set. The TPM framework offers a universal solution for other researchers and developers to evaluate TPM sensing modality in their application scenarios.
The significant findings from the empirical studies have shown that TPM is a versatile sensing modality. Specifically, in the ambient subspace, a sports mat or carpet with TPM sensors embedded underneath can distinguish different sports activities or different people's gait based on the dynamic change of body-print; a pressure sensitive tablecloth can detect various dining actions by the force propagated from the cutlery through the plates to the tabletop. In the object subspace, swirl office chairs with TPM sensors under the cover can be used to detect the seater's real-time posture; TPM can be used to detect emotion-related touch interactions for smart objects, toys or robots. In the wearable subspace, TPM sensors can be used to perform pressure-based mechanomyography to detect muscle and body movement; it can also be tailored to cover the surface of a soccer shoe to distinguish different kicking angles and intensities.
All the empirical evaluations have resulted in accuracies well-above the chance level of the corresponding number of classes, e.g., the `swirl chair' study has classification accuracy of 79.5% out of 10 posture classes and in the `soccer shoe' study the accuracy is 98.8% among 17 combinations of angle and intensity.
Topological insulators (TI) are a fascinating new state of matter. Like usual insulators, their band structure possesses a band gap, such that they cannot conduct current in their bulk. However, they are able to conduct current along their edges and surfaces, due to edge states that cross the band gap. What makes TIs so interesting and potentially useful are these robust unidirectional edge currents. They are immune to significant defects and disorder, which means that they provide scattering-free transport.
In photonics, using topological protection has a huge potential for applications, e.g. for robust optical data transfer [1-3] – even on the quantum level [4, 5] – or to make devices more stable and robust [6, 7]. Therefore, the field of topological insulators has spread to optics to create the new and active research field of topological photonics [8-10].
Well-defined and controllable model systems can help to provide deeper insight into the mechanisms of topologically protected transport. These model systems provide a vast control over parameters. For example, arbitrary lattice types without defects can be examined, and single lattice sites can be manipulated. Furthermore, they allow for the observation of effects that usually happen at extremely short time-scales in solids. Model systems based on photonic waveguides are ideal candidates for this.
They consist of optical waveguides arranged on a lattice. Due to evanescent coupling, light that is inserted into one waveguide spreads along the lattice. This coupling of light between waveguides can be seen as an analogue to electrons hopping/tunneling between atomic lattice sites in a solid.
The theoretical basis for this analogy is given by the mathematical equivalence between Schrödinger and paraxial Helmholtz equation. This means that in these waveguide systems, the role of time is assigned to a spatial axis. The field evolution along the waveguides' propagation axis z thus models the temporal evolution of an electron's wave-function in solid states. Electric and magnetic fields acting on electrons in solids need to be incorporated into the photonic platform by introducing artificial fields. These artificial gauge fields need to act on photons in the same way that their electro-magnetic counterparts act on electrons. E.g., to create a photonic analogue of a topological insulator the waveguides are bent helically along their propagation axis to model the effect of a magnetic field [3]. This means that the fabrication of these waveguide arrays needs to be done in 3D.
In this thesis, a new method to 3D micro-print waveguides is introduced. The inverse structure is fabricated via direct laser writing, and subsequently infiltrated with a material with higher refractive index contrast. We will use these model systems of evanescently coupled waveguides to look at different effects in topological systems, in particular at Floquet topological systems.
We will start with a topologically trivial system, consisting of two waveguide arrays with different artificial gauge fields. There, we observe that an interface between these trivial gauge fields has a profound impact on the wave vector of the light traveling across it. We deduce an analog to Snell's law and verify it experimentally.
Then we will move on to Floquet topological systems, consisting of helical waveguides. At the interface between two Floquet topological insulators with opposite helicity of the waveguides, we find additional trivial interface modes that trap the light. This allows to investigate the interaction between trivial and topological modes in the lattice.
Furthermore, we address the question if topological edge states are robust under the influence of time-dependent defects. In a one-dimensional topological model (the Su-Schrieffer-Heeger model [11]) we apply periodic temporal modulations to an edge wave-guide. We find Floquet copies of the edge state, that couple to the bulk in a certain frequency window and thus depopulate the edge state.
In the two-dimensional Floquet topological insulator, we introduce single defects at the edge. When these defects share the temporal periodicity of the helical bulk waveguides, they have no influence on a topological edge mode. Then, the light moves around/through the defect without being scattered into the bulk. Defects with different periodicity, however, can – likewise to the defects in the SSH model – induce scattering of the edge state into the bulk.
In the end we will briefly highlight a newly emerging method for the fabrication of waveguides with low refractive index contrast. Moreover, we will introduce new ways to create artificial gauge fields by the use of orbital angular momentum states in waveguides.
Under the notion of Cyber-Physical Systems an increasingly important research area has
evolved with the aim of improving the connectivity and interoperability of previously
separate system functions. Today, the advanced networking and processing capabilities
of embedded systems make it possible to establish strongly distributed, heterogeneous
systems of systems. In such configurations, the system boundary does not necessarily
end with the hardware, but can also take into account the wider context such as people
and environmental factors. In addition to being open and adaptive to other networked
systems at integration time, such systems need to be able to adapt themselves in accordance
with dynamic changes in their application environments. Considering that many
of the potential application domains are inherently safety-critical, it has to be ensured
that the necessary modifications in the individual system behavior are safe. However,
currently available state-of-the-practice and state-of-the-art approaches for safety assurance
and certification are not applicable to this context.
To provide a feasible solution approach, this thesis introduces a framework that allows
“just-in-time” safety certification for the dynamic adaptation behavior of networked
systems. Dynamic safety contracts (DSCs) are presented as the core solution concept
for monitoring and synthesis of decentralized safety knowledge. Ultimately, this opens
up a path towards standardized service provision concepts as a set of safety-related runtime
evidences. DSCs enable the modular specification of relevant safety features in
networked applications as a series of formalized demand-guarantee dependencies. The
specified safety features can be hierarchically integrated and linked to an interpretation
level for accessing the scope of possible safe behavioral adaptations. In this way, the networked
adaptation behavior can be conditionally certified with respect to the fulfilled
DSC safety features during operation. As long as the continuous evaluation process
provides safe adaptation behavior for a networked application context, safety can be
guaranteed for a networked system mode at runtime. Significant safety-related changes
in the application context, however, can lead to situations in which no safe adaptation
behavior is available for the current system state. In such cases, the remaining DSC
guarantees can be utilized to determine optimal degradation concepts for the dynamic
applications.
For the operationalization of the DSCs approach, suitable specification elements and
mechanisms have been defined. Based on a dedicated GUI-engineering framework it is
shown how DSCs can be systematically developed and transformed into appropriate runtime
representations. Furthermore, a safety engineering backbone is outlined to support
the DSC modeling process in concrete application scenarios. The conducted validation
activities show the feasibility and adequacy of the proposed DSCs approach. In parallel,
limitations and areas of future improvement are pointed out.