Controller design for continuous dynamical systems is a core algorithmic problem in the design of cyber-physical systems (CPS). When the CPS application is safety critical, additionally we require the controller to have strong correctness guarantees. One approach for this design problem is to use simpler discrete abstraction of the original continuous system, on which known reactive synthesis methods can be used to design the controller. This approach is known as the abstraction-based controller design (ABCD) paradigm. In this thesis, we build ABCD procedures which are faster and more modular compared to the state-of-the-art, and can handle problems which were beyond the scope of the existing techniques. Usually, existing ABCD approaches use state space discretization for computing the abstractions, for which the procedures do not scale well for larger systems. Our first contribution is a multi-layered ABCD algorithm, where we combine coarse abstractions and lazily computed fine abstractions to improve scalability. So far, we only address reach-avoid and safety specifications, for which our prototype tool (called Mascot) showed up to an order of magnitude speedup on standard benchmark examples. Second, we consider the problem of modular design of sound local controllers for a network of local discrete abstractions communicating via discrete/boolean variables and having local specifications. We propose a sound algorithm, where the systems negotiate a pair of local assume-guarantee contracts, in order to synchronize on a set of non-conflicting and correct behaviors. As a by-product, we also obtain a set of local controllers for the systems which ensure simultaneous satisfaction of the local specifications. We show the effectiveness of the our algorithm using a prototype tool (called Agnes) on a set of discrete benchmark examples. Our third contribution is a novel ABCD algorithm for a more expressive model of nonlinear dynamical systems with stochastic disturbances and ω-regular specifications. This part has two subparts, which are of significant merits on their own rights. First, we present an abstraction algorithm for nonlinear stochastic systems using 2.5-player games (turn-based stochastic graph games). We show that an almost sure winning strategy in this abstract 2.5-player game gives us a sound controller for the original system for satisfying the specification with probability one. Second, we present symbolic algorithms for a seemingly different class of 2-player games with certain environmental fairness assumptions, which can also be used to efficiently compute winning strategies in the aforementioned abstract 2.5-player game. Using our prototype tool (Mascot-SDS), we show that our algorithm significantly outperforms the state-of-the-art implementation on standard benchmark examples from the literature.

Robotic systems are entering the stage. Enabled by advances in both hardware components and software techniques, robots are increasingly able to operate outside of factories, assist humans, and work alongside them. The limiting factor of robots’ expansion remains the programming of robotic systems. Due to the many diverse skills necessary to build a multi-robot system, only the biggest organizations are able to innovate in the space of services provided by robots. To make developing new robotic services easier, in this dissertation I propose a program- ming model in which users (programmers) give a declarative specification of what needs to be accomplished, and then a backend system makes sure that the specification is safely and reliably executed. I present Antlab, one such backend system. Antlab accepts Linear Temporal Logic (LTL) specifications from multiple users and executes them using a set of robots of different capabilities. Building on the experience acquired implementing Antlab, I identify problems arising from the proposed programming model. These problems fall into two broad categories, specification and planning. In the category of specification problems, I solve the problem of inferring an LTL formula from sets of positive and negative example traces, as well as from a set of positive examples only. Building on top of these solutions, I develop a method to help users transfer their intent into a formal specification. The approach taken in this dissertation is combining the intent signals from a single demonstration and a natural language description given by a user. A set of candidate specifications is inferred by encoding the problem as a satisfiability problem for propositional logic. This set is narrowed down to a single specification through interaction with the user; the user approves or declines generated simulations of the robot’s behavior in different situations. In the category of planning problems, I first solve the problem of planning for robots that are currently executing their tasks. In such a situation, it is unclear what to take as the initial state for planning. I solve the problem by considering multiple, speculative initial states. The paths from those states are explored based on a quality function that repeatedly estimates the planning time. The second problem is a problem of reinforcement learning when the reward function is non-Markovian. The proposed solution consists of iteratively learning an automaton representing the reward function and using it to guide the exploration.

In the pre-seed phase before entering a market, new ventures face the complex, multi-faceted, and uncertain task of designing a business model. Founders accomplish this task within the framework of an innovation process, the so-called business model innovation process. However, because a set of feasible opportunities to design a viable business model is often not predictable in this early phase (Alvarez & Barney, 2007), business model ideas have to be revised multiple times, which corresponds to experimenting with alternative business models (Chesbrough, 2010). This also brings scholars to the relevant, but seldom noticed field of research on experimentation as a cognitive schema (Felin et al., 2015; Gavetti & Levinthal, 2000). The few scholars that discussed the importance of such thought experimentation did not elaborate on the manifestations of this phenomenon. Thus, building on qualitative interviews with entrepreneurs, the current state of the research has a gap that offers this dissertation the ability to clearly conceptualise the manifestation of experimentation as a cognitive schema in business model innovation. The results extend previous conceptualisations of experimentation by illustrating the interplay of three different forms of thought experimentation, namely purposeful interactions, incidental interactions, and theorising. In addition, the role of individuals in business model innovation has recently been recognised by scholars (Amit & Zott, 2015; Snihur & Zott, 2020). It is noticed that not only the founders themselves but also many other actors play a central role in this process to support a new venture on its way to designing a viable business model, such as accelerators or public institutions. It thus stands to reason that in addition to understanding how new ventures design their business model, it is also important to study how different actors are involved in this process. Building on qualitative interviews with entrepreneurs, this gap offers this dissertation the ability to study how different actors are involved in business model innovation and conceptualise actor engagement behaviours in this context. The results reveal six different actor engagement behaviours, including teaching, supporting, mobilising, co-developing, sharing, and signalling behaviour. Furthermore, it stands to reason, that entrepreneurs and external actors each play a certain role in business model innovation. Certain behavioural patterns and types of resource contributions may be characteristic for a group of actors, leading to the emergence of distinct actor roles. Thus, in this dissertation a role concept is established to illustrate how actors are involved in designing a new business model, including 13 actor roles. These actor roles are divided into task-oriented and network-oriented roles. Building on this, a variety of role dynamics are unveiled. Moreover, special attention is given to role temporality. Building on two case studies and a quantitative survey, the results reveal how actor roles are played at a certain point in time, thereby concretising them in relation to certain stages of the pre-seed phase.

Every organism contains a characteristic number of chromosomes that have to be segregated equally into two daughter cells during mitosis. Any error during chromosome segregation results in daughter cells that lost or gained a chromosome, a condition known as aneuploidy. Several studies from our laboratory and across the world have previously shown that aneuploidy per se strongly affects cellular physiology. However, these studies were limited mainly to the chromosomal gains due to the availability of several model systems. Strikingly, no systemic study to evaluate the impact of chromosome loss in the human cells has been performed so far. This is mainly due to the lack of model systems, as chromosome loss is incompatible with survival and drastically reduces cellular fitness. During my PhD thesis, for the first time, I used diverse omics and biochemical approaches to investigate the consequences of chromosome losses in human somatic cells. Using isogenic monosomic cells derived from the human cell line RPE1 lacking functional p53, we showed that, similar to the cells with chromosome gains, monosomic cells proliferate slower than the parental cells and exhibit genomic instability. Transcriptome and proteome analysis revealed that the expression of genes encoded on the monosomic chromosomes was reduced, as expected, but the abundance was partially compensated towards diploid levels by both transcriptional and post transcriptional mechanisms. Furthermore, we showed that monosomy induces global gene expression changes that are distinct to changes in response to chromosome gains. The most consistently deregulated pathways among the monosomies were ribosomes and translation, which we validated using polysome profiling and analysis of translation with puromycin incorporation experiments. We showed that these defects could be attributed to the haploinsufficiency of ribosomal protein genes (RPGs) encoded on monosomic chromosomes. Reintroduction of p53 into the monosomic cells uncovered that monosomy is incompatible with p53 expression and that the monosomic cells expressing p53 are either eliminated or outgrown by the p53 negative population. Given the RPG haploinsufficiency and ribosome biogenesis defects caused by monosomy, we show an evidence that the p53 activation in monosomies could be caused by the defects in ribosomes. These findings were further supported by computational analysis of cancer genomes revealing those cancers with monosomic karyotype accumulated frequently p53 pathway mutations and show reduced ribosomal functions. Together, our findings provide a rationale as to why monosomy is embryonically lethal, but frequently occurs in p53 deficient cancers.

Today’s digital world would be unthinkable without complex data sets. Whether in private, business or industrial environments, complex data provide the basis for important and critical decisions and determine many processes, some of which are automated. This is often associated with Big Data. However, often only one aspect of the usual Big Data definitions is sufficient and a human observer can no longer capture the data completely and correctly. In this thesis, different approaches are presented in order to master selected challenges in a more effective, efficient and userfriendly way. The approaches range from easier pre-processing of data sets for later analysis and the identification of design guidelines of such assistants, new visualization techniques for presenting uncertainty, extensions of existing visualizations for categorical data, concepts for time-saving selection methods for subsets of data points and faster navigation and zoom interaction–especially in the web-based area with enormous amounts of data–to new and innovative orientation-based interaction metaphors for mobile devices as well as stationary working environments. Evaluations and appropriate use case of the individual approaches show the usability also in comparison with state-of-the-art techniques.