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Deep learning enables enormous progress in many computer vision-related tasks. Artificial Intel- ligence (AI) steadily yields new state-of-the-art results in the field of detection and classification. Thereby AI performance equals or exceeds human performance. Those achievements impacted many domains, including medical applications.
One particular field of medical applications is gastroenterology. In gastroenterology, machine learning algorithms are used to assist examiners during interventions. One of the most critical concerns for gastroenterologists is the development of Colorectal Cancer (CRC), which is one of the leading causes of cancer-related deaths worldwide. Detecting polyps in screening colonoscopies is the essential procedure to prevent CRC. Thereby, the gastroenterologist uses an endoscope to screen the whole colon to find polyps during a colonoscopy. Polyps are mucosal growths that can vary in severity.
This thesis supports gastroenterologists in their examinations with automated detection and clas- sification systems for polyps. The main contribution is a real-time polyp detection system. This system is ready to be installed in any gastroenterology practice worldwide using open-source soft- ware. The system achieves state-of-the-art detection results and is currently evaluated in a clinical trial in four different centers in Germany.
The thesis presents two additional key contributions: One is a polyp detection system with ex- tended vision tested in an animal trial. Polyps often hide behind folds or in uninvestigated areas. Therefore, the polyp detection system with extended vision uses an endoscope assisted by two additional cameras to see behind those folds. If a polyp is detected, the endoscopist receives a vi- sual signal. While the detection system handles the additional two camera inputs, the endoscopist focuses on the main camera as usual.
The second one are two polyp classification models, one for the classification based on shape (Paris) and the other on surface and texture (NBI International Colorectal Endoscopic (NICE) classification). Both classifications help the endoscopist with the treatment of and the decisions about the detected polyp.
The key algorithms of the thesis achieve state-of-the-art performance. Outstandingly, the polyp detection system tested on a highly demanding video data set shows an F1 score of 90.25 % while working in real-time. The results exceed all real-time systems in the literature. Furthermore, the first preliminary results of the clinical trial of the polyp detection system suggest a high Adenoma Detection Rate (ADR). In the preliminary study, all polyps were detected by the polyp detection system, and the system achieved a high usability score of 96.3 (max 100). The Paris classification model achieved an F1 score of 89.35 % which is state-of-the-art. The NICE classification model achieved an F1 score of 81.13 %.
Furthermore, a large data set for polyp detection and classification was created during this thesis. Therefore a fast and robust annotation system called Fast Colonoscopy Annotation Tool (FastCAT) was developed. The system simplifies the annotation process for gastroenterologists. Thereby the
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gastroenterologists only annotate key parts of the endoscopic video. Afterward, those video parts are pre-labeled by a polyp detection AI to speed up the process. After the AI has pre-labeled the frames, non-experts correct and finish the annotation. This annotation process is fast and ensures high quality. FastCAT reduces the overall workload of the gastroenterologist on average by a factor of 20 compared to an open-source state-of-art annotation tool.
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.
Complexity and Partitions
(2001)
Computational complexity theory usually investigates the complexity of sets, i.e., the complexity of partitions into two parts. But often it is more appropriate to represent natural problems by partitions into more than two parts. A particularly interesting class of such problems consists of classification problems for relations. For instance, a binary relation R typically defines a partitioning of the set of all pairs (x,y) into four parts, classifiable according to the cases where R(x,y) and R(y,x) hold, only R(x,y) or only R(y,x) holds or even neither R(x,y) nor R(y,x) is true. By means of concrete classification problems such as Graph Embedding or Entailment (for propositional logic), this thesis systematically develops tools, in shape of the boolean hierarchy of NP-partitions and its refinements, for the qualitative analysis of the complexity of partitions generated by NP-relations. The Boolean hierarchy of NP-partitions is introduced as a generalization of the well-known and well-studied Boolean hierarchy (of sets) over NP. Whereas the latter hierarchy has a very simple structure, the situation is much more complicated for the case of partitions into at least three parts. To get an idea of this hierarchy, alternative descriptions of the partition classes are given in terms of finite, labeled lattices. Based on these characterizations the Embedding Conjecture is established providing the complete information on the structure of the hierarchy. This conjecture is supported by several results. A natural extension of the Boolean hierarchy of NP-partitions emerges from the lattice-characterization of its classes by considering partition classes generated by finite, labeled posets. It turns out that all significant ideas translate from the case of lattices. The induced refined Boolean hierarchy of NP-partitions enables us more accuratly capturing the complexity of certain relations (such as Graph Embedding) and a description of projectively closed partition classes.
Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research
(2021)
Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
Aktivitätsbasierte Verhaltensmodellierung und ihre Unterstützung bei Multiagentensimulationen
(2000)
Durch Zusammenführung traditioneller Methoden zur individuenbasierten Simulation und dem Konzept der Multiagentensysteme steht mit der Multiagentensimulation eine Methodik zur Verfügung, die es ermöglicht, sowohl technisch als auch konzeptionell eine neue Ebene an Detaillierung bei Modellbildung und Simulation zu erreichen. Ein Modell beruht dabei auf dem Konzept einer Gesellschaft: Es besteht aus einer Menge interagierender, aber in ihren Entscheidungen autonomen Einheiten, den Agenten. Diese ändern durch ihre Aktionen ihre Umwelt und reagieren ebenso auf die für sie wahrnehmbaren Änderungen in der Umwelt. Durch die Simulation jedes Agenten zusammen mit der Umwelt, in der er "lebt", wird die Dynamik im Gesamtsystem beobachtbar. In der vorliegenden Dissertation wurde ein Repräsentationsschema für Multiagentensimulationen entwickelt werden, das es Fachexperten, wie zum Beispiel Biologen, ermöglicht, selbständig ohne traditionelles Programmieren Multiagentenmodelle zu implementieren und mit diesen Experimente durchzuführen. Dieses deklarative Schema beruht auf zwei Basiskonzepten: Der Körper eines Agenten besteht aus Zustandsvariablen. Das Verhalten des Agenten kann mit Regeln beschrieben werden. Ausgehend davon werden verschiedene Strukturierungsansätze behandelt. Das wichtigste Konzept ist das der "Aktivität", einer Art "Verhaltenszustand": Während der Agent in einer Aktivität A verweilt, führt er die zugehörigen Aktionen aus und dies solange, bis eine Regel feuert, die diese Aktivität beendet und eine neue Aktivität auswählt. Durch Indizierung dieser Regeln bei den zugehörigen Aktivitäten und Einführung von abstrakten Aktivitäten entsteht ein Schema für eine vielfältig strukturierbare Verhaltensbeschreibung. Zu diesem Schema wurde ein Interpreter entwickelt, der ein derartig repräsentiertes Modell ausführt und so Simulationsexperimente mit dem Multiagentenmodell erlaubt. Auf dieser Basis wurde die Modellierungs- und Experimentierumgebung SeSAm ("Shell für Simulierte Agentensysteme") entwickelt. Sie verwendet vorhandene Konzepte aus dem visuellen Programmieren. Mit dieser Umgebung wurden Anwendungsmodelle aus verschiedenen Domänen realisiert: Neben abstrakten Spielbeispielen waren dies vor allem Fragestellungen zu sozialen Insekten, z.B. zum Verhalten von Ameisen, Bienen oder der Interaktion zwischen Bienenvölkern und Milbenpopulationen.
Die Realisierung einer koordinierten und effektiven Fortbewegung für einen mobilen Roboter in natürlichen, sich kontinuierlich verändernden Umgebungen unter sich ebenso bewegenden Hindernissen ist eine komplexe Aufgabe, die die Lösung einer Reihe von Unterproblemen voraussetzt. Die vorliegende Arbeit beschäftigt sich sowohl mit den Themen der Wahrnehmung und Fortbewegung in veränderlichen Umgebungen, als auch mit Methoden zur Analyse der Hindernisbewegungen in Zusammenhang mit der Roboterbewegung selbst. Die Wahrnehmung wird in erster Linie anhand von Laserscannern betrachtet, und ein entsprechendes Verfahren zur Hindernisdetektion und -verfolung wird vorgestellt. Dabei werden Verfahren der globalen Netzwerkoptimierung eingesetzt, um Korrespondenzen zwischen Objekten aus den Einzelbildern herzustellen, was sich positiv auf die Robustheit gegenüber Störungen durch sporadische kleine Objekte auswirkt. Die Navigation basiert auf einer Adaption des sog. "Velocity Obstacle" Ansatzes auf die vorhandene Fahrzeugkinematik, und eine kooperative Bewegungskoordination (Roboter begleitet Mensch) wird durch eine geeignete Auswahlregel für kollisionsfreie Geschwindigkeiten realisiert. Anschließend werden verschiedene Distanzmaße eingeführt, anhand derer sich etwa der Pfad des Roboters mit dem Pfad seiner Begleitperson vergleichen lässt. Weiter wird eine Klassifizierung von Situationen vorgenommen, in die der Roboter potentiell involviert sein kann, und nach einer Übersicht über existierende Ansätze zur automatischen Intentionserkennung wird ein praktikabler Ansatz zur Erkennung gezielter Behinderungen eines mobilen Roboters vorgestellt. Die Arbeit schließt mit einem neuen Ansatz der Bewegungsplanung in dynamischen Umgebungen, der auf rekursiven Modellen des Roboters von seinem Gegenüber basiert, d.h. der Roboter berechnet zunächst, wie er sich in der Situation des (intelligenten, beweglichen) Hindernisses fortbewegen würde, und bezieht dies in die Entscheidung über die eigene Fortbewegung mit ein. Je nach Rekursionstiefe entstehen hierdurch Verhaltensweisen unterschiedlichen Charakters für den Roboter.
A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandstädt et al. in Theor. Comput. Sci. 381(1–3):260–265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before.
Today’s Internet architecture was not designed from scratch but was driven by new services that emerged during its development. Hence, it is often described as patchwork where additional patches are applied in case new services require modifications to the existing architecture. This process however is rather slow and hinders the development of innovative network services with certain architecture or network requirements. Currently discussed technologies like Software-Defined Networking (SDN) or Network Virtualization (NV) are seen as key enabling technologies to overcome this rigid best effort legacy of the Internet. Both technologies offer the possibility to create virtual networks that accommodate the specific needs of certain services. These logical networks are operated on top of a physical substrate and facilitate flexible network resource allocation as physical resources can be added and removed depending on the current network and load situation. In addition, the clear separation and isolation of networks foster the development of application-aware networks that fulfill the special requirements of emerging applications. A prominent use case that benefits from these extended capabilities of the network is denoted with service component mobility. Services hosted on Virtual Machines (VMs) follow their consuming mobile endpoints, so that access latency as well as consumed network resources are reduced. Especially for applications like video streaming, which consume a large fraction of the available resources, is this an important means to relieve the resource constraints and eventually provide better service quality. Service and endpoint mobility both allow an adaptation of the used paths between an offered service, i.e., video streaming and the consuming users in case the service quality drops due to network problems. To make evidence-based adaptations in case of quality drops, a scalable monitoring component is required that is able to monitor the service quality for video streaming applications with reliable accuracy. This monograph details challenges that arise when deploying a certain service, i.e., video streaming, in a future virtualized network architecture and discusses possible solutions. In particular, this work evaluates the performance of mechanisms enabling service mobility and presents an optimized architecture for service mobility. Concerning endpoint mobility, improvements are developed that reduce the latency between endpoints and consumed services and ensure connectivity regardless of the used mobile access network. In the last part, a network-based video quality monitoring solution is developed and its accuracy is evaluated.
The focus of this work lies on the communication issues of Medium Access Control (MAC) and routing protocols in the context of WSNs. The communication challenges in these networks mainly result from high node density, low bandwidth, low energy constraints and the hardware limitations in terms of memory, computational power and sensing capabilities of low-power transceivers. For this reason, the structure of WSNs is always kept as simple as possible to minimize the impact of communication issues. Thus, the majority of WSNs apply a simple one hop star topology since multi-hop communication has high demands on the routing protocol since it increases the bandwidth requirements of the network. Moreover, medium access becomes a challenging problem due to the fact that low-power transceivers are very limited in their sensing capabilities. The first contribution is represented by the Backoff Preamble-based MAC Protocol with Sequential Contention Resolution (BPS-MAC) which is designed to overcome the limitations of low-power transceivers. Two communication issues, namely the Clear Channel Assessment (CCA) delay and the turnaround time, are directly addressed by the protocol. The CCA delay represents the period of time which is required by the transceiver to detect a busy radio channel while the turnaround time specifies the period of time which is required to switch between receive and transmit mode. Standard Carrier Sense Multiple Access (CSMA) protocols do not achieve high performance in terms of packet loss if the traffic is highly correlated due to the fact that the transceiver is not able to sense the medium during the switching phase. Therefore, a node may start to transmit data while another node is already transmitting since it has sensed an idle medium right before it started to switch its transceiver from receive to transmit mode. The BPS-MAC protocol uses a new sequential preamble-based medium access strategy which can be adapted to the hardware capabilities of the transceivers. The protocol achieves a very low packet loss rate even in wireless networks with high node density and event-driven traffic without the need of synchronization. This makes the protocol attractive to applications such as structural health monitoring, where event suppression is not an option. Moreover, acknowledgments or complex retransmission strategies become almost unnecessary since the sequential preamble-based contention resolution mechanism minimizes the collision probability. However, packets can still be lost as a consequence of interference or other issues which affect signal propagation. The second contribution consists of a new routing protocol which is able to quickly detect topology changes without generating a large amount of overhead. The key characteristics of the Statistic-Based Routing (SBR) protocol are high end-to-end reliability (in fixed and mobile networks), load balancing capabilities, a smooth continuous routing metric, quick adaptation to changing network conditions, low processing and memory requirements, low overhead, support of unidirectional links and simplicity. The protocol can establish routes in a hybrid or a proactive mode and uses an adaptive continuous routing metric which makes it very flexible in terms of scalability while maintaining stable routes. The hybrid mode is optimized for low-power WSNs since routes are only established on demand. The difference of the hybrid mode to reactive routing strategies is that routing messages are periodically transmitted to maintain already established routes. However, the protocol stops the transmission of routing messages if no data packets are transmitted for a certain time period in order to minimize the routing overhead and the energy consumption. The proactive mode is designed for high data rate networks which have less energy constraints. In this mode, the protocol periodically transmits routing messages to establish routes in a proactive way even in the absence of data traffic. Thus, nodes in the network can immediately transmit data since the route to the destination is already established in advance. In addition, a new delay-based routing message forwarding strategy is introduced. The forwarding strategy is part of SBR but can also be applied to many routing protocols in order to modify the established topology. The strategy can be used, e.g. in mobile networks, to decrease the packet loss by deferring routing messages with respect to the neighbor change rate. Thus, nodes with a stable neighborhood forward messages faster than nodes within a fast changing neighborhood. As a result, routes are established through nodes with correlated movement which results in fewer topology changes due to higher link durations.
The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes.
Graphs are a frequently used tool to model relationships among entities. A graph is a binary relation between objects, that is, it consists of a set of objects (vertices) and a set of pairs of objects (edges).
Networks are common examples of modeling data as a graph. For example, relationships between persons in a social network, or network links between computers in a telecommunication network can be represented by a graph.
The clearest way to illustrate the modeled data is to visualize the graphs. The field of Graph Drawing deals with the problem of finding algorithms to automatically generate graph visualizations. The task is to find a "good" drawing, which can be measured by different criteria such as number of crossings between edges or the used area. In this thesis, we study Angular Schematization in Graph Drawing. By this, we mean drawings
with large angles (for example, between the edges at common vertices or at crossing points).
The thesis consists of three parts. First, we deal with the placement of boxes. Boxes are axis-parallel rectangles that can, for example, contain text.
They can be placed on a map to label important sites, or can be used to describe semantic relationships between words in a word network. In the second part of the thesis, we consider graph drawings visually guide the
viewer. These drawings generally induce large angles between edges that meet at a vertex. Furthermore, the edges are drawn crossing-free and in a way that
makes them easy to follow for the human eye. The third and final part is devoted to crossings with large angles. In drawings with crossings, it is important to have large angles between edges at their crossing point, preferably right angles.
This article introduces the Off-The-Shelf Stylus (OTSS), a framework for 2D interaction (in 3D) as well as for handwriting and sketching with digital pen, ink, and paper on physically aligned virtual surfaces in Virtual, Augmented, and Mixed Reality (VR, AR, MR: XR for short). OTSS supports self-made XR styluses based on consumer-grade six-degrees-of-freedom XR controllers and commercially available styluses. The framework provides separate modules for three basic but vital features: 1) The stylus module provides stylus construction and calibration features. 2) The surface module provides surface calibration and visual feedback features for virtual-physical 2D surface alignment using our so-called 3ViSuAl procedure, and surface interaction features. 3) The evaluation suite provides a comprehensive test bed combining technical measurements for precision, accuracy, and latency with extensive usability evaluations including handwriting and sketching tasks based on established visuomotor, graphomotor, and handwriting research. The framework’s development is accompanied by an extensive open source reference implementation targeting the Unity game engine using an Oculus Rift S headset and Oculus Touch controllers. The development compares three low-cost and low-tech options to equip controllers with a tip and includes a web browser-based surface providing support for interacting, handwriting, and sketching. The evaluation of the reference implementation based on the OTSS framework identified an average stylus precision of 0.98 mm (SD = 0.54 mm) and an average surface accuracy of 0.60 mm (SD = 0.32 mm) in a seated VR environment. The time for displaying the stylus movement as digital ink on the web browser surface in VR was 79.40 ms on average (SD = 23.26 ms), including the physical controller’s motion-to-photon latency visualized by its virtual representation (M = 42.57 ms, SD = 15.70 ms). The usability evaluation (N = 10) revealed a low task load, high usability, and high user experience. Participants successfully reproduced given shapes and created legible handwriting, indicating that the OTSS and it’s reference implementation is ready for everyday use. We provide source code access to our implementation, including stylus and surface calibration and surface interaction features, making it easy to reuse, extend, adapt and/or replicate previous results (https://go.uniwue.de/hci-otss).
An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
In der Arbeit wird ein neues Konzept für Fahrsimulator-Datenbasen vorgestellt. Der Anwender entwirft eine auf seine Fragestellung zugeschnittene Datenbasis mithilfe einer einfachen Skriptsprache. Das Straßennetzwerk wird auf einer topologischen Ebene repäsentiert. In jedem Simulationsschritt wird hieraus im Sichtbarkeitsbereich des Fahrers die geometrische Repäsentation berechnet. Die für den Fahrer unsichtbaren Teile des Straßenetzwerks können während der Simulation verändert werden. Diese Veränderungen können von der Route des Fahrers oder von den in der Simulation erhobenen Messerten abhängen. Zudem kann der Anwender das Straßennetzwerk interaktiv verändern. Das vorgestellte Konzept bietet zahlreiche Möglichkeiten zur Erzeugung reproduzierbarer Szenarien für Experimente in Fahrsimulatoren.
Background: Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed.
Results: (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones).
Conclusions: Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.
Nowadays, robotics plays an important role in increasing fields of application. There exist many environments or situations where mobile robots instead of human beings are used, since the tasks are too hazardous, uncomfortable, repetitive, or costly for humans to perform. The autonomy and the mobility of the robot are often essential for a good solution of these problems. Thus, such a robot should at least be able to answer the question "Where am I?". This thesis investigates the problem of self-localizing a robot in an indoor environment using range measurements. That is, a robot equipped with a range sensor wakes up inside a building and has to determine its position using only its sensor data and a map of its environment. We examine this problem from an idealizing point of view (reducing it into a pure geometric one) and further investigate a method of Guibas, Motwani, and Raghavan from the field of computational geometry to solving it. Here, so-called visibility skeletons, which can be seen as coarsened representations of visibility polygons, play a decisive role. In the major part of this thesis we analyze the structures and the occurring complexities in the framework of this scheme. It turns out that the main source of complication are so-called overlapping embeddings of skeletons into the map polygon, for which we derive some restrictive visibility constraints. Based on these results we are able to improve one of the occurring complexity bounds in the sense that we can formulate it with respect to the number of reflex vertices instead of the total number of map vertices. This also affects the worst-case bound on the preprocessing complexity of the method. The second part of this thesis compares the previous idealizing assumptions with the properties of real-world environments and discusses the occurring problems. In order to circumvent these problems, we use the concept of distance functions, which model the resemblance between the sensor data and the map, and appropriately adapt the above method to the needs of realistic scenarios. In particular, we introduce a distance function, namely the polar coordinate metric, which seems to be well suited to the localization problem. Finally, we present the RoLoPro software where most of the discussed algorithms are implemented (including the polar coordinate metric).
For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses
(2021)
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.