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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.

Many loads acting on a vehicle depend on the condition and quality of roads
traveled as well as on the driving style of the motorist. Thus, during vehicle development,
good knowledge on these further operations conditions is advantageous.
For that purpose, usage models for different kinds of vehicles are considered. Based
on these mathematical descriptions, representative routes for multiple user
types can be simulated in a predefined geographical region. The obtained individual
driving schedules consist of coordinates of starting and target points and can
thus be routed on the true road network. Additionally, different factors, like the
topography, can be evaluated along the track.
Available statistics resulting from travel survey are integrated to guarantee reasonable
trip length. Population figures are used to estimate the number of vehicles in
contained administrative units. The creation of thousands of those geo-referenced
trips then allows the determination of realistic measures of the durability loads.
Private as well as commercial use of vehicles is modeled. For the former, commuters
are modeled as the main user group conducting daily drives to work and
additional leisure time a shopping trip during workweek. For the latter, taxis as
example for users of passenger cars are considered. The model of light-duty commercial
vehicles is split into two types of driving patterns, stars and tours, and in
the common traffic classes of long-distance, local and city traffic.
Algorithms to simulate reasonable target points based on geographical and statistical
data are presented in detail. Examples for the evaluation of routes based
on topographical factors and speed profiles comparing the influence of the driving
style are included.

In computer graphics, realistic rendering of virtual scenes is a computationally complex problem. State-of-the-art rendering technology must become more scalable to
meet the performance requirements for demanding real-time applications.
This dissertation is concerned with core algorithms for rendering, focusing on the
ray tracing method in particular, to support and saturate recent massively parallel computer systems, i.e., to distribute the complex computations very efficiently
among a large number of processing elements. More specifically, the three targeted
main contributions are:
1. Collaboration framework for large-scale distributed memory computers
The purpose of the collaboration framework is to enable scalable rendering
in real-time on a distributed memory computer. As an infrastructure layer it
manages the explicit communication within a network of distributed memory
nodes transparently for the rendering application. The research is focused on
designing a communication protocol resilient against delays and negligible in
overhead, relying exclusively on one-sided and asynchronous data transfers.
The hypothesis is that a loosely coupled system like this is able to scale linearly
with the number of nodes, which is tested by directly measuring all possible
communication-induced delays as well as the overall rendering throughput.
2. Ray tracing algorithms designed for vector processing
Vector processors are to be efficiently utilized for improved ray tracing performance. This requires the basic, scalar traversal algorithm to be reformulated
in order to expose a high degree of fine-grained data parallelism. Two approaches are investigated: traversing multiple rays simultaneously, and performing
multiple traversal steps at once. Efficiently establishing coherence in a group
of rays as well as avoiding sorting of the nodes in a multi-traversal step are the
defining research goals.
3. Multi-threaded schedule and memory management for the ray tracing acceleration structure
Construction times of high-quality acceleration structures are to be reduced by
improvements to multi-threaded scalability and utilization of vector processors. Research is directed at eliminating the following scalability bottlenecks:
dynamic memory growth caused by the primitive splits required for high-
quality structures, and top-level hierarchy construction where simple task par-
allelism is not readily available. Additional research addresses how to expose
scatter/gather-free data-parallelism for efficient vector processing.
Together, these contributions form a scalable, high-performance basis for real-time,
ray tracing-based rendering, and a prototype path tracing application implemented
on top of this basis serves as a demonstration.
The key insight driving this dissertation is that the computational power necessary
for realistic light transport for real-time rendering applications demands massively
parallel computers, which in turn require highly scalable algorithms. Therefore this
dissertation provides important research along the path towards virtual reality.

While the design step should be free from computational related constraints and operations due to its artistic aspect, the modeling phase has to prepare the model for the later stages of the pipeline.
This dissertation is concerned with the design and implementation of a framework for local remeshing and optimization. Based on the experience gathered, a full study about mesh quality criteria is also part of this work.
The contributions can be highlighted as: (1) a local meshing technique based on a completely novel approach constrained to the preservation of the mesh of non interesting areas. With this concept, designers can work on the design details of specific regions of the model without introducing more polygons elsewhere; (2) a tool capable of recovering the shape of a refined area to its decimated version, enabling details on optimized meshes of detailed models; (3) the integration of novel techniques into a single framework for meshing and smoothing which is constrained to surface structure; (4) the development of a mesh quality criteria priority structure, being able to classify and prioritize according to the application of the mesh.
Although efficient meshing techniques have been proposed along the years, most of them lack the possibility to mesh smaller regions of the base mesh, preserving the mesh quality and density of outer areas.
Considering this limitation, this dissertation seeks answers to the following research questions:
1. Given that mesh quality is relative to the application it is intended for, is it possible to design a general mesh evaluation plan?
2. How to prioritize specific mesh criteria over others?
3. Given an optimized mesh and its original design, how to improve the representation of single regions of the first, without degrading the mesh quality elsewhere?
Four main achievements came from the respective answers:
1. The Application Driven Mesh Quality Criteria Structure: Due to high variation in mesh standards because of various computer aided operations performed for different applications, e.g. animation or stress simulation, a structure for better visualization of mesh quality criteria is proposed. The criteria can be used to guide the mesh optimization, making the task consistent and reliable. This dissertation also proposes a methodology to optimize the criteria values, which is adaptable to the needs of a specific application.
2. Curvature Driven Meshing Algorithm: A novel approach, a local meshing technique, which works on a desired area of the mesh while preserving its boundaries as well as the rest of the topology. It causes a slow growth in the overall amount of polygons by making only small regions denser. The method can also be used to recover the details of a reference mesh to its decimated version while refining it. Moreover, it employs a geometric fast and easy to implement approach representing surface features as simple circles, being used to guide the meshing. It also generates quad-dominant meshes, with triangle count directly dependent on the size of the boundary.
3. Curvature-based Method for Anisotropic Mesh Smoothing: A geometric-based method is extended to 3D space to be able to produce anisotropic elements where needed. It is made possible by mapping the original space to another which embeds the surface curvature. This methodology is used to enhance the smoothing algorithm by making the nearly regularized elements follow the surface features, preserving the original design. The mesh optimization method also preserves mesh topology, while resizing elements according to the local mesh resolution, effectively enhancing the design aspects intended.
4. Framework for Local Restructure of Meshed Surfaces: The combination of both methods creates a complete tool for recovering surface details through mesh refinement and curvature aware mesh smoothing.

In this thesis we consider the directional analysis of stationary point processes. We focus on three non-parametric methods based on second order analysis which we have defined as Integral method, Ellipsoid method, and Projection method. We present the methods in a general setting and then focus on their application in the 2D and 3D case of a particular type of anisotropy mechanism called geometric anisotropy. We mainly consider regular point patterns motivated by our application to real 3D data coming from glaciology. Note that directional analysis of 3D data is not so prominent in the literature.
We compare the performance of the methods, which depends on the relative parameters, in a simulation study both in 2D and 3D. Based on the results we give recommendations on how to choose the methods´ parameters in practice.
We apply the directional analysis to the 3D data coming from glaciology, which consist in the locations of air-bubbles in polar ice cores. The aim of this study is to provide information about the deformation rate in the ice and the corresponding thinning of ice layers at different depths. This information is substantial for the glaciologists in order to build ice dating models and consequently to give a correct interpretation of the climate information which can be found by analyzing ice cores. In this thesis we consider data coming from three different ice cores: the Talos Dome core, the EDML core and the Renland core.
Motivated by the ice application, we study how isotropic and stationary noise influences the directional analysis. In fact, due to the relaxation of the ice after drilling, noise bubbles can form within the ice samples. In this context we take two classification algorithms into consideration, which aim to classify points in a superposition of a regular isotropic and stationary point process with Poisson noise.
We introduce two methods to visualize anisotropy, which are particularly useful in 3D and apply them to the ice data. Finally, we consider the problem of testing anisotropy and the limiting behavior of the geometric anisotropy transform.

In this thesis, we consider the problem of processing similarity queries over a dataset of top-k rankings and class constrained objects. Top-k rankings are the most natural and widely used technique to compress a large amount of information into a concise form. Spearman’s Footrule distance is used to compute the similarity between rankings, considering how well rankings agree on the positions (ranks) of ranked items. This setup allows the application of metric distance-based pruning strategies, and, alternatively, enables the use of traditional inverted indices for retrieving rankings that overlap in items. Although both techniques can be individually applied, we hypothesize that blending these two would lead to better performance. First, we formulate theoretical bounds over the rankings, based on Spearman's Footrule distance, which are essential for adapting existing, inverted index based techniques to the setting of top-k rankings. Further, we propose a hybrid indexing strategy, designed for efficiently processing similarity range queries, which incorporates inverted indices and metric space indices, such as M- or BK-trees, resulting in a structure that resembles both indexing methods with tunable emphasis on one or the other. Moreover, optimizations to the inverted index component are presented, for early termination and minimizing bookkeeping. As vast amounts of data are being generated on a daily bases, we further present a distributed, highly tunable, approach, implemented in Apache Spark, for efficiently processing similarity join queries over top-k rankings. To combine distance-based filtering with inverted indices, the algorithm works in several phases. The partial results are joined for the computation of the final result set. As the last contribution of the thesis, we consider processing k-nearest-neighbor (k-NN) queries over class-constrained objects, with the additional requirement that the result objects are of a specific type. We introduce the MISP index, which first indexes the objects by their (combination of) class belonging, followed by a similarity search sub index for each subset of objects. The number of such subsets can combinatorially explode, thus, we provide a cost model that analyzes the performance of the MISP index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.

Visualization is vital to the scientific discovery process.
An interactive high-fidelity rendering provides accelerated insight into complex structures, models and relationships.
However, the efficient mapping of visualization tasks to high performance architectures is often difficult, being subject to a challenging mixture of hardware and software architectural complexities in combination with domain-specific hurdles.
These difficulties are often exacerbated on heterogeneous architectures.
In this thesis, a variety of ray casting-based techniques are developed and investigated with respect to a more efficient usage of heterogeneous HPC systems for distributed visualization, addressing challenges in mesh-free rendering, in-situ compression, task-based workload formulation, and remote visualization at large scale.
A novel direct raytracing scheme for on-the-fly free surface reconstruction of particle-based simulations using an extended anisoptropic kernel model is investigated on different state-of-the-art cluster setups.
The versatile system renders up to 170 million particles on 32 distributed compute nodes at close to interactive frame rates at 4K resolution with ambient occlusion.
To address the widening gap between high computational throughput and prohibitively slow I/O subsystems, in situ topological contour tree analysis is combined with a compact image-based data representation to provide an effective and easy-to-control trade-off between storage overhead and visualization fidelity.
Experiments show significant reductions in storage requirements, while preserving flexibility for exploration and analysis.
Driven by an increasingly heterogeneous system landscape, a flexible distributed direct volume rendering and hybrid compositing framework is presented.
Based on a task-based dynamic runtime environment, it enables adaptable performance-oriented deployment on various platform configurations.
Comprehensive benchmarks with respect to task granularity and scaling are conducted to verify the characteristics and potential of the novel task-based system design.
A core challenge of HPC visualization is the physical separation of visualization resources and end-users.
Using more tiles than previously thought reasonable, a distributed, low-latency multi-tile streaming system is demonstrated, being able to sustain a stable 80 Hz when streaming up to 256 synchronized 3840x2160 tiles and achieve 365 Hz at 3840x2160 for sort-first compositing over the internet, thereby enabling lightweight visualization clients and leaving all the heavy lifting to the remote supercomputer.

Topology-Based Characterization and Visual Analysis of Feature Evolution in Large-Scale Simulations
(2019)

This manuscript presents a topology-based analysis and visualization framework that enables the effective exploration of feature evolution in large-scale simulations. Such simulations pose additional challenges to the already complex task of feature tracking and visualization, since the vast number of features and the size of the simulation data make it infeasible to naively identify, track, analyze, render, store, and interact with data. The presented methodology addresses these issues via three core contributions. First, the manuscript defines a novel topological abstraction, called the Nested Tracking Graph (NTG), that records the temporal evolution of features that exhibit a nesting hierarchy, such as superlevel set components for multiple levels, or filtered features across multiple thresholds. In contrast to common tracking graphs that are only capable of describing feature evolution at one hierarchy level, NTGs effectively summarize their evolution across all hierarchy levels in one compact visualization. The second core contribution is a view-approximation oriented image database generation approach (VOIDGA) that stores, at simulation runtime, a reduced set of feature images. Instead of storing the features themselves---which is often infeasable due to bandwidth constraints---the images of these databases can be used to approximate the depicted features from any view angle within an acceptable visual error, which requires far less disk space and only introduces a neglectable overhead. The final core contribution combines these approaches into a methodology that stores in situ the least amount of information necessary to support flexible post hoc analysis utilizing NTGs and view approximation techniques.

Various physical phenomenons with sudden transients that results into structrual changes can be modeled via
switched nonlinear differential algebraic equations (DAEs) of the type
\[
E_{\sigma}\dot{x}=A_{\sigma}x+f_{\sigma}+g_{\sigma}(x). \tag{DAE}
\]
where \(E_p,A_p \in \mathbb{R}^{n\times n}, x\mapsto g_p(x),\) is a mapping, \(p \in \{1,\cdots,P\}, P\in \mathbb{N}
f \in \mathbb{R} \rightarrow \mathbb{R}^n , \sigma: \mathbb{R} \rightarrow \{1,\cdots, P\}\).
Two related common tasks are:
Task 1: Investigate if above (DAE) has a solution and if it is unique.
Task 2: Find a connection among a solution of above (DAE) and solutions of related
partial differential equations.
In the linear case \(g(x) \equiv 0\) the task 1 has been tackeled already in a
distributional solution framework.
A main goal of the dissertation is to give contribution to task 1 for the
nonlinear case \(g(x) \not \equiv 0\) ; also contributions to the task 2 are given for
switched nonlinear DAEs arising while modeling sudden transients in water
distribution networks. In addition, this thesis contains the following further
contributions:
The notion of structured switched nonlinear DAEs has been introduced,
allowing also non regular distributions as solutions. This extend a previous
framework that allowed only piecewise smooth functions as solutions. Further six mild conditions were given to ensure existence and uniqueness of the solution within the space of piecewise smooth distribution. The main
condition, namely the regularity of the matrix pair \((E,A)\), is interpreted geometrically for those switched nonlinear DAEs arising from water network graphs.
Another contribution is the introduction of these switched nonlinear DAEs
as a simplication of the PDE model used classically for modeling water networks. Finally, with the support of numerical simulations of the PDE model it has been illustrated that this switched nonlinear DAE model is a good approximation for the PDE model in case of a small compressibility coefficient.

Shared memory concurrency is the pervasive programming model for multicore architectures
such as x86, Power, and ARM. Depending on the memory organization, each architecture follows
a somewhat different shared memory model. All these models, however, have one common
feature: they allow certain outcomes for concurrent programs that cannot be explained
by interleaving execution. In addition to the complexity due to architectures, compilers like
GCC and LLVM perform various program transformations, which also affect the outcomes of
concurrent programs.
To be able to program these systems correctly and effectively, it is important to define a
formal language-level concurrency model. For efficiency, it is important that the model is
weak enough to allow various compiler optimizations on shared memory accesses as well
as efficient mappings to the architectures. For programmability, the model should be strong
enough to disallow bogus “out-of-thin-air” executions and provide strong guarantees for well-synchronized
programs. Because of these conflicting requirements, defining such a formal
model is very difficult. This is why, despite years of research, major programming languages
such as C/C++ and Java do not yet have completely adequate formal models defining their
concurrency semantics.
In this thesis, we address this challenge and develop a formal concurrency model that is very
good both in terms of compilation efficiency and of programmability. Unlike most previous
approaches, which were defined either operationally or axiomatically on single executions,
our formal model is based on event structures, which represents multiple program executions,
and thus gives us more structure to define the semantics of concurrency.
In more detail, our formalization has two variants: the weaker version, WEAKEST, and the
stronger version, WEAKESTMO. The WEAKEST model simulates the promising semantics proposed
by Kang et al., while WEAKESTMO is incomparable to the promising semantics. Moreover,
WEAKESTMO discards certain questionable behaviors allowed by the promising semantics.
We show that the proposed WEAKESTMO model resolve out-of-thin-air problem, provide
standard data-race-freedom (DRF) guarantees, allow the desirable optimizations, and can be
mapped to the architectures like x86, PowerPC, and ARMv7. Additionally, our models are
flexible enough to leverage existing results from the literature to establish data-race-freedom
(DRF) guarantees and correctness of compilation.
In addition, in order to ensure the correctness of compilation by a major compiler, we developed
a translation validator targeting LLVM’s “opt” transformations of concurrent C/C++
programs. Using the validator, we identified a few subtle compilation bugs, which were reported
and were fixed. Additionally, we observe that LLVM concurrency semantics differs
from that of C11; there are transformations which are justified in C11 but not in LLVM and
vice versa. Considering the subtle aspects of LLVM concurrency, we formalized a fragment
of LLVM’s concurrency semantics and integrated it into our WEAKESTMO model.

The systems in industrial automation management (IAM) are information systems. The management parts of such systems are software components that support the manufacturing processes. The operational parts control highly plug-compatible devices, such as controllers, sensors and motors. Process variability and topology variability are the two main characteristics of software families in this domain. Furthermore, three roles of stakeholders -- requirement engineers, hardware-oriented engineers, and software developers -- participate in different derivation stages and have different variability concerns. In current practice, the development and reuse of such systems is costly and time-consuming, due to the complexity of topology and process variability. To overcome these challenges, the goal of this thesis is to develop an approach to improve the software product derivation process for systems in industrial automation management, where different variability types are concerned in different derivation stages. Current state-of-the-art approaches commonly use general-purpose variability modeling languages to represent variability, which is not sufficient for IAM systems. The process and topology variability requires more user-centered modeling and representation. The insufficiency of variability modeling leads to low efficiency during the staged derivation process involving different stakeholders. Up to now, product line approaches for systematic variability modeling and realization have not been well established for such complex domains. The model-based derivation approach presented in this thesis integrates feature modeling with domain-specific models for expressing processes and topology. The multi-variability modeling framework includes the meta-models of the three variability types and their associations. The realization and implementation of the multi-variability involves the mapping and the tracing of variants to their corresponding software product line assets. Based on the foundation of multi-variability modeling and realization, a derivation infrastructure is developed, which enables a semi-automated software derivation approach. It supports the configuration of different variability types to be integrated into the staged derivation process of the involved stakeholders. The derivation approach is evaluated in an industry-grade case study of a complex software system. The feasibility is demonstrated by applying the approach in the case study. By using the approach, both the size of the reusable core assets and the automation level of derivation are significantly improved. Furthermore, semi-structured interviews with engineers in practice have evaluated the usefulness and ease-of-use of the proposed approach. The results show a positive attitude towards applying the approach in practice, and high potential to generalize it to other related domains.

The usage of sensors in modern technical systems and consumer products is in a rapid increase. This advancement can be characterized by two major factors, namely, the mass introduction of consumer oriented sensing devices to the market and the sheer amount of sensor data being generated. These characteristics raise subsequent challenges regarding both the consumer sensing devices' reliability and the management and utilization of the generated sensor data. This thesis addresses these challenges through two main contributions. It presents a novel framework that leverages sentiment analysis techniques in order to assess the quality of consumer sensing devices. It also couples semantic technologies with big data technologies to present a new optimized approach for realization and management of semantic sensor data, hence providing a robust means of integration, analysis, and reuse of the generated data. The thesis also presents several applications that show the potential of the contributions in real-life scenarios.
Due to the broad range, growing feature set and fast release pace of new sensor-based products, evaluating these products is very challenging as standard product testing is not practical. As an alternative, an end-to-end aspect-based sentiment summarizer pipeline for evaluation of consumer sensing devices is presented. The pipeline uses product reviews to extract the sentiment at the aspect level and includes several components namely, product name extractor, aspects extractor and a lexicon-based sentiment extractor which handles multiple sentiment analysis challenges such as sentiment shifters, negations, and comparative sentences among others. The proposed summarizer's components generally outperform the state-of-the-art approaches. As a use case, features of the market leading fitness trackers are evaluated and a dynamic visual summarizer is presented to display the evaluation results and to provide personalized product recommendations for potential customers.
The increased usage of sensing devices in the consumer market is accompanied with increased deployment of sensors in various other fields such as industry, agriculture, and energy production systems. This necessitates using efficient and scalable methods for storing and processing of sensor data. Coupling big data technologies with semantic techniques not only helps to achieve the desired storage and processing goals, but also facilitates data integration, data analysis, and the utilization of data in unforeseen future applications through preserving the data generation context. This thesis proposes an efficient and scalable solution for semantification, storage and processing of raw sensor data through ontological modelling of sensor data and a novel encoding scheme that harnesses the split between the statements of the conceptual model of an ontology (TBox) and the individual facts (ABox) along with in-memory processing capabilities of modern big data systems. A sample use case is further introduced where a smartphone is deployed in a transportation bus to collect various sensor data which is then utilized in detecting street anomalies.
In addition to the aforementioned contributions, and to highlight the potential use cases of sensor data publicly available, a recommender system is developed using running route data, used for proximity-based retrieval, to provide personalized suggestions for new routes considering the runner's performance, visual and nature of route preferences.
This thesis aims at enhancing the integration of sensing devices in daily life applications through facilitating the public acquisition of consumer sensing devices. It also aims at achieving better integration and processing of sensor data in order to enable new potential usage scenarios of the raw generated data.

In modern algebraic geometry solutions of polynomial equations are studied from a qualitative point of view using highly sophisticated tools such as cohomology, \(D\)-modules and Hodge structures. The latter have been unified in Saito’s far-reaching theory of mixed Hodge modules, that has shown striking applications including vanishing theorems for cohomology. A mixed Hodge module can be seen as a special type of filtered \(D\)-module, which is an algebraic counterpart of a system of linear differential equations. We present the first algorithmic approach to Saito’s theory. To this end, we develop a Gröbner basis theory for a new class of algebras generalizing PBW-algebras.
The category of mixed Hodge modules satisfies Grothendieck’s six-functor formalism. In part these functors rely on an additional natural filtration, the so-called \(V\)-filtration. A key result of this thesis is an algorithm to compute the \(V\)-filtration in the filtered setting. We derive from this algorithm methods for the computation of (extraordinary) direct image functors under open embeddings of complements of pure codimension one subvarieties. As side results we show how to compute vanishing and nearby cycle functors and a quasi-inverse of Kashiwara’s equivalence for mixed Hodge modules.
Describing these functors in terms of local coordinates and taking local sections, we reduce the corresponding computations to algorithms over certain bifiltered algebras. It leads us to introduce the class of so-called PBW-reduction-algebras, a generalization of the class of PBW-algebras. We establish a comprehensive Gröbner basis framework for this generalization representing the involved filtrations by weight vectors.

Linking protistan community shifts along salinity gradients with cellular haloadaptation strategies
(2019)

Salinity is one of the most structuring environmental factors for microeukaryotic communities. Using eDNA barcoding, I detected significant shifts in microeukaryotic community compositions occurring at distinct salinities between brackish and marine conditions in the Baltic Sea. I, furthermore, conducted a metadata analysis including my and other marine and hypersaline community sequence data to confirm the existence of salinity-related transition boundaries and significant changes in alpha diversity patterns along a brackish to hypersaline gradient. One hypothesis for the formation of salinity-dependent transition boundaries between brackish to hypersaline conditions is the use of different cellular haloadaptation strategies. To test this hypothesis, I conducted metatranscriptome analyses of microeukaryotic communities along a pronounced salinity gradient (40 – 380 ‰). Clustering of functional transcripts revealed differences in metabolic properties and metabolic capacities between microeukaryotic communities at specific salinities, corresponding to the transition boundaries already observed in the taxonomic eDNA barcoding approach. In specific, microeukaryotic communities thriving at mid-hypersaline conditions (≤ 150 ‰) seem to predominantly apply the ‘low-salt – organic-solutes-in’ strategy by accumulating compatible solutes to counteract osmotic stress. Indications were found for both the intracellular synthesis of compatible solutes as well as for cellular transport systems. In contrast, communities of extreme-hypersaline habitats (≥ 200 ‰) may preferentially use the ‘high-salt-in’ strategy, i. e. the intracellular accumulation of inorganic ions in high concentrations, which is implied by the increased expression of Mg2+, K+, Cl- transporters and channels.
In order to characterize the ‘low-salt – organic-solutes-in’ strategy applied by protists in more detail, I conducted a time-resolved transcriptome analysis of the heterotrophic ciliate Schmidingerothrix salinarum serving as model organism. S. salinarum was thus subjected to a salt-up shock to investigate the intracellular response to osmotic stress by shifts of gene expression. After increasing the external salinity, an increased expression of two-component signal transduction systems and MAPK cascades was observed. In an early reaction, the expression of transport mechanisms for K+, Cl- and Ca2+ increased, which may enhance the capacity of K+, Cl- and Ca2+ in the cytoplasm to compensate possibly harmful Na+ influx. Expression of enzymes for the synthesis of possible compatible solutes, starting with glycine betaine, followed by ectoine and later proline, could imply that the inorganic ions K+, Cl- and Ca2+ are gradually replaced by the synthesized compatible solutes. Additionally, expressed transporters for choline (precursor of glycine betaine) and proline could indicate an intracellular accumulation of compatible solutes to balance the external salinity. During this accumulation, the up-regulated ion export mechanisms may increase the capacity for Na+ expulsion from the cytoplasm and ion compartmentalization between cell organelles seem to happen.
The results of my PhD project revealed first evidence at molecular level for the salinity-dependent use of different haloadaptation strategies in microeukaryotes and significantly extend existing knowledge about haloadaptation processes in ciliates. The results provide ground for future research, such as (comparative) transcriptome analysis of ciliates thriving in extreme-hypersaline habitats or experiments like qRT-PCR to validate transcriptome results.

On the Effect of Nanofillers on the Environmental Stress Cracking Resistance of Glassy Polymers
(2019)

It is well known that reinforcing polymers with small amounts of nano-sized fillers is one of the most effective methods for simultaneously improving their mechanical and thermal properties. However, only a small number of studies have focused on environ-mental stress cracking (ESC), which is a major issue for premature failures of plastic products in service. Therefore, the contribution of this work focused on the influence of nano-SiO2 particles on the morphological, optical, mechanical, thermal, as well as envi-ronmental stress cracking properties of amorphous-based nanocomposites.
Polycarbonate (PC), polystyrene (PS) and poly(methyl methacrylate) (PMMA) nanocom-posites containing different amounts and sizes of nano-SiO2 particles were prepared using a twin-screw extruder followed by injection molding. Adding a small amount of nano-SiO2 caused a reduction in optical properties but improved the tensile, toughness, and thermal properties of the polymer nanocomposites. The significant enhancement in mechanical and thermal properties was attributed to the adequate level of dispersion and interfacial interaction of the SiO2 nanoparticles in the polymer matrix. This situation possibly increased the efficiency of stress transfer across the nanocomposite compo-nents. Moreover, the data revealed a clear dependency on the filler size. The polymer nanocomposites filled with smaller nanofillers exhibited an outstanding enhancement in both mechanical properties and transparency compared with nanocomposites filled with larger particles. The best compromise of strength, toughness, and thermal proper-ties was achieved in PC-based nanocomposites. Therefore, special attention to the influ-ence of nanofiller on the ESC resistance was given to PC.
The ESC resistance of the materials was investigated under static loading with and without the presence of stress-cracking agents. Interestingly, the incorporation of nano-SiO2 greatly enhanced the ESC resistance of PC in all investigated fluids. This result was particularly evident with the smaller quantities and sizes of nano-SiO2. The enhancement in ESC resistance was more effective in mild agents and air, where the quality of the deformation process was vastly altered with the presence of nano-SiO2. This finding confirmed that the new structural arrangements on the molecular scale in-duced by nanoparticles dominate over the ESC agent absorption effect and result in greatly improving the ESC resistance of the materials. This effect was more pronounced with increasing molecular weight of PC due to an increase in craze stability and fibril density. The most important and new finding is that the ESC behavior of polymer-based nanocomposites/ stress-cracking agent combinations can be scaled using the Hansen solubility parameter. Thus allowed us to predict the risk of ESC as a function of the filler content for different stress-cracking agents without performing extensive tests. For a comparison of different amorphous polymer-based nanocomposites at a given nano-SiO2 particle content, the ESC resistance of materials improved in the following order: PMMA/SiO2 < PS/SiO2 < low molecular weight PC/SiO2 < high molecular weight PC/SiO2. In most cases, nanocomposites with 1 vol.% of nano-SiO2 particles exhibited the largest improvement in ESC resistance.
However, the remarkable improvement in the ESC resistance—particularly in PC-based nanocomposites—created some challenges related to material characterization because testing times (failure time) significantly increased. Accordingly, the superposition ap-proach has been applied to construct a master curve of crack propagation model from the available short-term tests at different temperatures. Good agreement of the master curves with the experimental data revealed that the superposition approach is a suitable comparative method for predicting slow crack growth behavior, particularly for long-duration cracking tests as in mild agents. This methodology made it possible to mini-mize testing time.
Additionally, modeling and simulations using the finite element method revealed that multi-field modeling could provide reasonable predictions for diffusion processes and their impact on fracture behavior in different stress cracking agents. This finding sug-gests that the implemented model may be a useful tool for quick screening and mitigat-ing the risk of ESC failures in plastic products.

Most modern multiprocessors offer weak memory behavior to improve their performance in terms of throughput. They allow the order of memory operations to be observed differently by each processor. This is opposite to the concept of sequential consistency (SC) which enforces a unique sequential view on all operations for all processors. Because most software has been and still is developed with SC in mind, we face a gap between the expected behavior and the actual behavior on modern architectures. The issues described only affect multithreaded software and therefore most programmers might never face them. However, multi-threaded bare metal software like operating systems, embedded software, and real-time software have to consider memory consistency and ensure that the order of memory operations does not yield unexpected results. This software is more critical as general consumer software in terms of consequences, and therefore new methods are needed to ensure their correct behavior.
In general, a memory system is considered weak if it allows behavior that is not possible in a sequential system. For example, in the SPARC processor with total store ordering (TSO) consistency, all writes might be delayed by store buffers before they eventually are processed by the main memory. This allows the issuing process to work with its own written values before other processes observed them (i.e., reading its own value before it leaves the store buffer). Because this behavior is not possible with sequential consistency, TSO is considered to be weaker than SC. Programming in the context of weak memory architectures requires a proper comprehension of how the model deviates from expected sequential behavior. For verification of these programs formal representations are required that cover the weak behavior in order to utilize formal verification tools.
This thesis explores different verification approaches and respectively fitting representations of a multitude of memory models. In a joint effort, we started with the concept of testing memory operation traces in regard of their consistency with different memory consistency models. A memory operation trace is directly derived from a program trace and consists of a sequence of read and write operations for each process. Analyzing the testing problem, we are able to prove that the problem is NP-complete for most memory models. In that process, a satisfiability (SAT) encoding for given problem instances was developed, that can be used in reachability and robustness analysis.
In order to cover all program executions instead of just a single program trace, additional representations are introduced and explored throughout this thesis. One of the representations introduced is a novel approach to specify a weak memory system using temporal logics. A set of linear temporal logic (LTL) formulas is developed that describes all properties required to restrict possible traces to those consistent to the given memory model. The resulting LTL specifications can directly be used in model checking, e.g., to check safety conditions. Unfortunately, the derived LTL specifications suffer from the state explosion problem: Even small examples, like the Peterson mutual exclusion algorithm, tend to generate huge formulas and require vast amounts of memory for verification. For this reason, it is concluded that using the proposed verification approach these specifications are not well suited for verification of real world software. Nonetheless, they provide comprehensive and formally correct descriptions that might be used elsewhere, e.g., programming or teaching.
Another approach to represent these models are operational semantics. In this thesis, operational semantics of weak memory models are provided in the form of reference machines that are both correct and complete regarding the memory model specification. Operational semantics allow to simulate systems with weak memory models step by step. This provides an elegant way to study the effects that lead to weak consistent behavior, while still providing a basis for formal verification. The operational models are then incorporated in verification tools for multithreaded software. These state space exploration tools proved suitable for verification of multithreaded software in a weak consistent memory environment. However, because not only the memory system but also the processor are expressed as operational semantics, some verification approach will not be feasible due to the large size of the state space.
Finally, to tackle the beforementioned issue, a state transition system for parallel programs is proposed. The transition system is defined by a set of structural operational semantics (SOS) rules and a suitable memory structure that can cover multiple memory models. This allows to influence the state space by use of smart representations and approximation approaches in future work.

In der vorliegenden Arbeit wird das Verhalten von thermoplastischen
Verbundwerkstoffen mittels experimentellen und numerischen Untersuchungen
betrachtet. Das Ziel dieser Untersuchungen ist die Identifikation und Quantifikation
des Versagensverhaltens und der Energieabsorptionsmechanismen von geschichteten,
quasi-isotropen thermoplastischen Faser-Kunststoff-Verbunden und die Umsetzung
der gewonnenen Einsichten in Eigenschaften und Verhalten eines Materialmodells zur
Vorhersage des Crash-Verhaltens dieser Werkstoffe in transienten Analysen.
Vertreter der untersuchten Klassen sind un- und mittel-vertreckte Rundgestricke und
glasfaserverstärkte Thermoplaste (GMT). Die Untersuchungen an rundgestrickten
glasfaser-(GF)-verstärktem Polyethylentherephthalat (PET) waren Teil eines
Forschungsprojektes zur Charakterisierung sowohl der Verarbeitbarkeit als auch des
mechanischen Verhaltens. Experimente an GMT und Schnittfaser-GMT wurden
ebenfalls zum Vergleich mit dem Gestrick durchgeführt und dienen als Bestätigung
des beobachteten Verhaltens des Gestrickes.
Besonderer Aufmerksamkeit wird der Einfluß der Probengeometrie auf die Resultate
gewidmet, weil die Crash-Charakteristiken wesentlich von der Geometrie des
getesteten Probekörpers abhängen. Hierzu wurde ein Rundhutprofil zur Untersuchung
dieses Einflußes definiert. Diese spezielle Geometrie hat insbesondere Vorteile
hinsichtlich Energieabsorptionsvermögen sowie Herstellbarkeit von thermoplastischen
Verbundwerkstoffen (TPCs). Es wurden Impakt- und Perforationsversuche zur
Untersuchung der Schädigungsausbreitung und zur Charakterisierung der Zähigkeit
der untersuchten Materialien durchgeführt.
Geschichtete TPCs versagen hauptsächlich in einem Laminat-Biegemodus mit
kombiniertem intra- und interlaminaren Schub (transversaler Schub zwischen Lagen und teilweise mit transversalen Schubbrüchen in einzelnen Lagen). Durch eine
Kopplung der aktuellen Versagensmodi und Crash-Kennwerten wie der mittleren
Crash-Spannung, konnten Indikationen über die Relation zwischen Materialparameter
und absoluter Energieabsorption gewonnen werden.
Numerische Untersuchungen wurden mit einem expliziten Finiten Elemente-
Programm zur Simulation von dreidimensionalen, großen Verformungen durchgeführt.
Das Modell besteht bezüglich des Querschnittaufbaus aus einer mesoskopischen
Darstellung, die zwischen Matrix-zwischenlagen und mesoskopischen Verbundwerkstofflagen unterscheidet. Die Modellgeometrie stellt einen vereinfachten
Längsquerschnitt durch den Probekörper dar. Dabei wurden Einflüsse der Reibung
zwischen Impaktor und Material sowie zwischen einzelnen Lagen berücksichtigt.
Auch die lokal herrschende Dehnrate, Energie und Spannungs-Dehnungsverteilung
über die mesoskopischen Phasen konnten beobachtet werden. Dieses Modell zeigt
deutlich die verschiedenen Effekte, die durch den heterogenen Charakter des Laminats
entstehen, und gibt auch Hinweise für einige Erklärungen dieser Effekte.
Basierend auf den Resultaten der obengenannten Untersuchungen wurde ein
phänomenologisches Modell mit a-priori Information des inherenten
Materialverhaltens vorgeschlagen. Daher, daß das Crashverhalten vom heterogenen
Charakter des Werkstoffes dominiert wird, werden im Modell die Phasen separat
betrachtet. Eine einfache Methode zur Bestimmung der mesoskopischen Eigenschaften
wird diskutiert.
Zur Beschreibung des Verhaltens vom thermoplastischen Matrixsystem während
„Crushing“ würde ein dehnraten- und temperaturabhängiges Plastizitätsgesetz
ausreichen. Für die Beschreibung des Verhaltens der Verbundwerkstoffschichten wird
eine gekoppelte Plastizitäts- und Schädigungsformulierung vorgeschlagen. Ein solches
Modell kann sowohl den plastischen Anteil des Matrixsystems als auch das
„Softening“ - verursacht durch Faser-Matrix-Grenzflächenversagen und Faserbrüche -
beschreiben. Das vorgeschlagene Modell unterscheidet zwischen Belastungsfällen für
axiales „Crushing“ und Versagen ohne „Crushing“. Diese Unterteilung ermöglicht
eine explizite Modellierung des Werkstoffes unter Berücksichtigung des spezifischen
Materialzustandes und der Geometrie für den außerordentlichen Belastungsfall, der
zum progressiven Versagen führt.

Cell migration is essential for embryogenesis, wound healing, immune surveillance, and
progression of diseases, such as cancer metastasis. For the migration to occur, cellular
structures such as actomyosin cables and cell-substrate adhesion clusters must interact.
As cell trajectories exhibit a random character, so must such interactions. Furthermore,
migration often occurs in a crowded environment, where the collision outcome is deter-
mined by altered regulation of the aforementioned structures. In this work, guided by a
few fundamental attributes of cell motility, we construct a minimal stochastic cell migration
model from ground-up. The resulting model couples a deterministic actomyosin contrac-
tility mechanism with stochastic cell-substrate adhesion kinetics, and yields a well-defined
piecewise deterministic process. The signaling pathways regulating the contractility and
adhesion are considered as well. The model is extended to include cell collectives. Numer-
ical simulations of single cell migration reproduce several experimentally observed results,
including anomalous diffusion, tactic migration, and contact guidance. The simulations
of colliding cells explain the observed outcomes in terms of contact induced modification
of contractility and adhesion dynamics. These explained outcomes include modulation
of collision response and group behavior in the presence of an external signal, as well as
invasive and dispersive migration. Moreover, from the single cell model we deduce a pop-
ulation scale formulation for the migration of non-interacting cells. In this formulation,
the relationships concerning actomyosin contractility and adhesion clusters are maintained.
Thus, we construct a multiscale description of cell migration, whereby single, collective,
and population scale formulations are deduced from the relationships on the subcellular
level in a mathematically consistent way.