Study on the Impact of Data Breaches on Firm Value and Information Security Risk Disclosures

The dissertation explores the financial and managerial impacts of data breaches on healthcare companies in the U.S., a sector increasingly significant given its growing proportion of the GDP and superior performance compared to the S&P 500. The study assesses the financial repercussions of data breaches and investigates the changes in disclosure practices over time. The research is divided into three parts. The first part employs an event study methodology to evaluate the stock price impacts of data breaches, revealing that while such incidents typically result in negative market reactions, these effects have diminished over time. This suggests an evolving investor sensitivity, influenced by historical market events and changes in the regulatory environment such as the implementation of breach notification laws. The second part of the thesis expands on these findings by analyzing the textual content of company disclosures following data breaches. It examines how management's response strategies affect investor reactions. The results indicate that companies can mitigate the losses in firm value typically associated with data breaches by disclosing information security risks in more detail. The third section delves deeper into the impacts of the 2018 SEC guidance on disclosure practices. It uses cosine similarity to assess changes in the textuality of disclosures post-guidance, highlighting a trend towards an industry specific convergence of information security risk disclosures following the guidance from the SEC

Thermodynamically consistent concurrent material and structure optimization of elastoplastic multiphase hierarchical systems

The concept of concurrent material and structure optimization aims at alleviating the computational discovery of optimum microstructure configurations in multiphase hierarchical systems, whose macroscale behavior is governed by their microstructure composition that can evolve over multiple length scales from a few micrometers to centimeters. It is based on the split of the multiscale optimization problem into two nested sub-problems, one at the macroscale (structure) and the other at the microscales (material). In this paper, we establish a novel formulation of concurrent material and structure optimization for multiphase hierarchical systems with elastoplastic constituents at the material scales. Exploiting the thermomechanical foundations of elastoplasticity, we reformulate the material optimization problem based on the maximum plastic dissipation principle such that it assumes the format of an elastoplastic constitutive law and can be efficiently solved via modified return mapping algorithms. We integrate continuum micromechanics based estimates of the stiffness and the yield criterion into the formulation, which opens the door to a computationally feasible treatment of the material optimization problem. To demonstrate the accuracy and robustness of our framework, we define new benchmark tests with several material scales that, for the first time, become computationally feasible. We argue that our formulation naturally extends to multiscale optimization under further path-dependent effects such as viscoplasticity or multiscale fracture and damage

The "C" in crowdfunding is for co-financing: exploring participative co-financing, a complement of novel and traditional bank financing

We explore the potentials of participative co-financing as a means for regional banks to integrate an innovative financing technique that enhances their strengths. Our goal is to interest platform operators, decision-makers of regional banks, and researchers in the potentials of participative co-financing. We define participative co-financing as capital provision, where professional financing sources provide one part, and the other is supplied via participative crowdfunding. We claim that crowdfunding and regional banks are compatible by common interests. We explore potentials emanating at the intersection of both fields by drawing on entrepreneurship and finance literature. Eventually, we bridge the gap between both fields of research. To guide our research, we develop a framework featuring the intersection of crowdfunding and regional banks. We ask: Which potentials affect the intentions of decision-makers in regional banks to offer participative co-financing? The technology acceptance model (TAM) provides a theoretical foundation for our analysis. We conduct a twofold analysis by looking at the direct effects of potentials first and acceptance according to the TAM second. Thereby we consider the intention to offer lending- and equity-based co-financing. We surveyed decision-makers from an association of German savings banks and derived 108 answers. We show that regional banks generally accept participative co-financing as an innovative financing technique. The most likely model is lending-based co-financing, with individual persons, startups, and SMEs as target groups. Decision-makers hope to profit from cross-selling and being perceived as innovative. Nevertheless, further research and trials are necessary to advance participative co-financing

On the robustness of potential-based flow networks

Potential-based flows provide a simple yet realistic mathematical model of transport in many real-world infrastructure networks such as, e.g., gas or water networks, where the flow along each edge depends on the difference of the potentials at its end nodes. We call a network topology robust if the maximal node potential needed to satisfy a set of demands never increases when demands are decreased. This notion of robustness is motivated by infrastructure networks where users first make reservations for certain demands that may be larger than the actual flows sent later on. In these networks, node potentials correspond to physical quantities such as pressures or hydraulic heads and must be guaranteed to lie within a fixed range, even if the actual amounts are smaller than the previously reserved demands. Our main results are a precise characterization of robust network topologies for the case of point-to-point demands via forbidden node-labeled graph minors, as well as an efficient algorithm for testing robustness

Moment-Based Variational Inference for Markov Jump Processes

We propose moment-based variational inference as a flexible framework for approximate smoothing of latent Markov jump processes. The main ingredient of our approach is to partition the set of all transitions of the latent process into classes. This allows to express the Kullback-Leibler divergence from the approximate to the posterior process in terms of a set of moment functions that arise naturally from the chosen partition. To illustrate possible choices of the partition, we consider special classes of jump processes that frequently occur in applications. We then extend the results to latent parameter inference and demonstrate the method on several examples

Learning Generalized Nash Equilibria in a Class of Convex Games

We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel distributed payoff-based algorithm, where each agent uses information only about its cost value and the constraint value with its associated dual multiplier. We prove convergence of this algorithm to a Nash equilibrium, under the assumption that the game admits a strictly convex potential function. In the absence of coupling constraints, we prove convergence to Nash equilibria under significantly weaker assumptions, not requiring a potential function. Namely, strict monotonicity of the game mapping is sufficient for convergence. We also derive the convergence rate of the algorithm for strongly monotone game maps

Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains

Hidden semi-Markov Models (HSMM's) - while broadly in use - are restricted to a discrete and uniform time grid. They are thus not well suited to explain often irregularly spaced discrete event data from continuous-time phenomena. We show that non-sampling-based latent state inference used in HSMM's can be generalized to latent Continuous-Time semi-Markov Chains (CTSMC's). We formulate integro-differential forward and backward equations adjusted to the observation likelihood and introduce an exact integral equation for the Bayesian posterior marginals and a scalable Viterbi-type algorithm for posterior path estimates. The presented equations can be efficiently solved using well-known numerical methods. As a practical tool, variable-step HSMM's are introduced. We evaluate our approaches in latent state inference scenarios in comparison to classical HSMM's

Advancing Machine Learning Algorithms for Object Localization in Data-Limited Scenarios : Techniques for 6DoF Pose Estimation and 2D Localization with limited Data

Recent successes of Machine Learning (ML) algorithms have profoundly influenced many fields, particularly Computer Vision (CV). One longstanding problem in CV is the task of determining the position and orientation of an object as depicted in an image in 3D space, relative to the recording camera sensor. Accurate pose estimation is essential for domains, such as robotics, augmented reality, autonomous driving, quality inspection in manufacturing, and many more. Current state-of-the-art pose estimation algorithms are dominated by Deep Learning-based approaches. However, adoption of these best in class algorithms to real-world tasks is often constrained by data limitations, such as not enough training data being available, existing data being of insufficient quality, data missing annotations, data having noisy annotations, or no directly suitable training data being available at all. This thesis presents contributions on both 6D object pose estimation, as well as on alleviating the restrictions of data limitations, for pose estimation, and for related CV problems such as classification, segmentation, and 2D object detection. It offers a range of solutions to enhance quality and efficiency of these tasks under different kinds of data limitations. The first contribution enhances a state-of-the-art pose estimation algorithm to predict a probability distribution of poses, instead of a single pose estimate. This approach allows to sample multiple, plausible poses for further refinement and outperforms the baseline algorithm even when sampling only the most likely pose. In our second contribution, we drastically improve runtime and reduce resource requirements to bring state-of-the-art pose estimation to low power edge devices, such as modern augmented and extended reality devices. Finally, we extend a pose estimator based on dense-feature prediction to incorporate additional views and illustrate its performance benefits in the stereo use case. The second set of two contributions focuses on data generation for ML-based CV tasks. High quality training data is a crucial component for best performance. We introduce a novel yet simple setup to record physical objects and generate all necessary annotations in a fully automated way. Evaluated on the 2D object detection use case, training on our data performs favourably with more complex data generation processes, such as real-world recordings and physically-based rendering. In a follow-up paper, we further improve upon the results by introducing a novel postprocessing step based on denoising diffusion probabilistic models (DDPM). At the intersection of 6D pose estimation and data generation methods, a final group of three contributions focuses on solving or circumventing the data problem with a range of different approaches. First, we demonstrate the use of physically-based, photorealistic, and non-photorealistic rendering to localize objects on Microsoft HoloLens 2, without needing any real-world images for training. Second, we extend a zero-shot pose estimation method by predicting geometric features, thereby improving estimation quality with almost no additional runtime. Third, we demonstrate pose estimation of objects with unseen appearances based on a 3D scene representation, allowing robust mesh-free pose estimation. In summary, this thesis advances the fields of 6D object pose estimation and alleviates some common data limitations for pose estimation and similar Machine Learning algorithms in Computer Vision problems, such as 2D detection and segmentation. The solutions proposed include several extensions to state-of-the-art 6D pose estimators and address the challenges of limited or poor quality training data, paving the way for more accurate, efficient, and accessible pose estimation technologies across various industries and fields

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes

Development of an FMCW Lidar Signal Processing Model

This work is concerned with the simulation of a frequency modulated continuous wave (FMCW) lidar (light detection and ranging) sensor in the context of automated vehicles. In order to save time and resources, automated driving functions are increasingly safeguarded in virtual environments, which requires respective simulation models of the vehicle’s perception sensors. In the following, the simulation of the FMCW lidar sensor is split into the three components: environment simulation, signal propagation model and signal processing model. The latter represents the main focus of this work and includes all processing steps that are executed within the sensor housing. An externally provided ray tracer realizes the signal propagation model. At the beginning, it is necessary to define the requirements for the FMCW lidar signal processing model. For this purpose, an existing approach is adapted and further developed. As a result, it is specified that the model must be able to accurately reproduce the beam pattern, the range measuring and the direct radial velocity measuring. Next up, a model development methodology is introduced which envisages an iterative step-by-step implementation of the three mentioned requirements with continuous verification and validation. Accordingly, within each iteration the current model is verified and validated after the implementation step. Subsequently, the proposed model development methodology is utilized to realize the signal processing model. The beam pattern, the radial range measuring and the radial velocity measuring are implemented one after the other. For each of the three, experimental reference measurements are conducted to obtain real sensor data. After that, the signal processing model is used within the sensor simulation to re-simulate the real measurements in a virtual environment. To enable this, additional reference sensors such as a laser range finder are employed during the real measurements to precisely determine the range to the target. For the velocity reference measurements, an Automotive Dynamic Motion Analyzer (ADMA) captures the position and velocity of the moving target vehicle. The uncertainties of these reference sensors are also taken into account by the simulation. The resulting simulated data is directly compared to the real reference data to validate the model after each implementation. At the end, a partly validated model is presented that fulfills the basic functions of an FMCW lidar sensor. The findings of this work show that the developed methodologies and approaches are suited for the development of a less complex perception sensor model. However, further improvements are necessary to enable more sophisticated and fully validated models