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Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) can show variable histological growth patterns and present remarkable overlap with T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL). Previous studies suggest that NLPHL histological variants represent progression forms of NLPHL and THRLBCL transformation in aggressive disease. Since molecular studies of both lymphomas are limited due to the low number of tumor cells, the present study aimed to learn if a better understanding of these lymphomas is possible via detailed measurements of nuclear and cell size features in 2D and 3D sections. Whereas no significant differences were visible in 2D analyses, a slightly increased nuclear volume and a significantly enlarged cell size were noted in 3D measurements of the tumor cells of THRLBCL in comparison to typical NLPHL cases. Interestingly, not only was the size of the tumor cells increased in THRLBCL but also the nuclear volume of concomitant T cells in the reactive infiltrate when compared with typical NLPHL. Particularly CD8+ T cells had frequent contacts to tumor cells of THRLBCL. However, the nuclear volume of B cells was comparable in all cases. These results clearly demonstrate that 3D tissue analyses are superior to conventional 2D analyses of histological sections. Furthermore, the results point to a strong activation of T cells in THRLBCL, representing a cytotoxic response against the tumor cells with unclear effectiveness, resulting in enhanced swelling of the tumor cell bodies and limiting proliferative potential. Further molecular studies combining 3D tissue analyses and molecular data will help to gain profound insight into these ill-defined cellular processes.
We derive a shape derivative formula for the family of principal Dirichlet eigenvalues λs(Ω) of the fractional Laplacian (−Δ)s associated with bounded open sets Ω⊂RN of class C1,1. This extends, with a help of a new approach, a result in Dalibard and Gérard-Varet (Calc. Var. 19(4):976–1013, 2013) which was restricted to the case s=12. As an application, we consider the maximization problem for λs(Ω) among annular-shaped domains of fixed volume of the type B∖B¯¯¯¯′, where B is a fixed ball and B′ is ball whose position is varied within B. We prove that λs(B∖B¯¯¯¯′) is maximal when the two balls are concentric. Our approach also allows to derive similar results for the fractional torsional rigidity. More generally, we will characterize one-sided shape derivatives for best constants of a family of subcritical fractional Sobolev embeddings.
Die folgende Arbeit handelt von einem Human Computer Interaction Interface, welches es gestattet, mit Hilfe von Gesten zu schreiben. Das System ermöglicht seinen Nutzern, neue Gesten hinzuzufügen und zu verwenden. Da Gesten besser erkannt werden können, je genauer die Darstellung der Hände ist, wird diese durch Datenhandschuhe an den Computer übertragen. Die Hände werden einerseits in der Virtual Reality (VR) dargestellt, damit sie der Nutzer sieht. Andererseits werden die Daten, die die Gestenerkennung benötigt, an das Interface weitergeleitet. Die Erkennung der Gesten wird mit Hilfe eines Neuronales Netz (NN) implementiert. Dieses ist in der Lage, Gesten zu unterscheiden, sofern es genügend Trainingsdaten erhalten hat. Die genutzten Gesten sind entweder einhändig oder beidhändig auszuführen. Die Aussagen der Gesten beziehen sich in dieser Arbeit vor allem auf relationale Operatoren, die Beziehungen zwischen Objekten ausdrücken, wie beispielsweise „gleich“ oder „größer gleich“. Abschließend wird in dieser Arbeit ein System geschaffen, das es ermöglicht, mit Gesten Sätze auszudrücken. Dies betrifft das sogenannte gestische Schreiben nach Mehler, Lücking und Abrami 2014. Zu diesem Zweck befindet sich der Nutzer in einem virtuellen Raum mit Objekten, die er verknüpfen kann, wobei er Sätze in einem relationalen Kontext manifestiert.
We provide a Hopf boundary lemma for the regional fractional Laplacian (−Δ)sΩ, with Ω ⊂ RN a bounded open set. More precisely, given u a pointwise or weak super-solution of the equation (−Δ)s u = c(x)u in Ω, we show that the ratio u(x)∕(dist(x, 𝜕Ω))2s−1 is strictly Ω positive as x approaches the boundary 𝜕Ω of Ω. We also prove a strong maximum principle for distributional super-solutions.
The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.
We consider a linear ill-posed equation in the Hilbert space setting. Multiple independent unbiased measurements of the right-hand side are available. A natural approach is to take the average of the measurements as an approximation of the right-hand side and to estimate the data error as the inverse of the square root of the number of measurements. We calculate the optimal convergence rate (as the number of measurements tends to infinity) under classical source conditions and introduce a modified discrepancy principle, which asymptotically attains this rate.
We show how nonlocal boundary conditions of Robin type can be encoded in the pointwise expression of the fractional operator. Notably, the fractional Laplacian of functions satisfying homogeneous nonlocal Neumann conditions can be expressed as a regional operator with a kernel having logarithmic behaviour at the boundary.
In this survey paper, we present a multiscale post-processing method in exploration. Based on a physically relevant mollifier technique involving the elasto-oscillatory Cauchy–Navier equation, we mathematically describe the extractable information within 3D geological models obtained by migration as is commonly used for geophysical exploration purposes. More explicitly, the developed multiscale approach extracts and visualizes structural features inherently available in signature bands of certain geological formations such as aquifers, salt domes etc. by specifying suitable wavelet bands.
Adaptive, synchronous, and mobile online education: developing the ASYMPTOTE learning environment
(2022)
The COVID-19-induced distance education was perceived as highly challenging by teachers and students. A cross-national comparison of five European countries identified several challenges occurred during the distance learning period. On this basis, the article aims to develop a theoretical framework and design requirements for distance and online learning tools. As one example for online learning in mathematics education, the ASYMPTOTE system is introduced. It will be freely available by May 2022. ASYMPTOTE is aimed at the adaptive and synchronous delivery of online education by taking a mobile learning approach. Its core is the so-called digital classroom, which not only allows students to interact with each other or with the teacher but also enables teachers to monitor their students’ work progress in real time. With respect to the theoretical framework, this article analyses to what extent the ASYMPTOTE system meets the requirements of online learning. Overall, the digital classroom can be seen as a promising tool for teachers to carry out appropriate formative assessment and—partly—to maintain personal and content-related interaction at a distance. Moreover, we highlight the availability of this tool. Due to its mobile learning approach, almost all students will be able to participate in lessons conducted with ASYMPTOTE.
We show the existence of additive kinematic formulas for general flag area measures, which generalizes a recent result by Wannerer. Building on previous work by the second named author, we introduce an algebraic framework to compute these formulas explicitly. This is carried out in detail in the case of the incomplete flag manifold consisting of all (p+1)-planes containing a unit vector.
Biological ageing is a degenerative and irreversible process, ultimately leading to death of the organism. The process is complex and under the control of genetic, environmental and stochastic traits. Although many theories have been established during the last decades, none of these are able to fully describe the complex mechanisms, which lead to ageing. Generally, biological processes and environmental factors lead to molecular damage and an accumulation of impaired cellular components. In contrast, counteracting surveillance systems are effective, including repair, remodelling and degradation of damaged or impaired components, respectively. Nevertheless, at some point these systems are no longer effective, either because the increasing amount of molecular damages can not longer be removed efficiently or because the repairing and removing mechanisms themselves become affected by impairing effects. The organism finally declines and dies. To investigate and to understand these counteracting mechanisms and the complex interplay of decline and maintenance, holistic and systems biological investigations are required. Hence, the processes which lead to ageing in the fungal model organism Podospora anserina, had been analysed using different advanced bioinformatics methods. In contrast to many other ageing models, P. anserina exhibits a short lifespan, a less biochemical complexity and it provides a good accessibility for genetic manipulations.
To achieve a general overview on the different biochemical processes, which are affected during ageing in P. anserina, an initial comprehensive investigation was applied, which aimed to reveal genes significantly regulated and expressed in an age-dependent manner. This investigation was based on an age-dependent transcriptome analysis. Sophisticated and comprehensive analyses revealed different age-related pathways and indicated that especially autophagy may play a crucial role during ageing. For example, it was found that the expression of autophagy-associated genes increases in the course of ageing.
Subsequently, to investigate and to characterise the autophagy pathway, its associated single components and their interactions, Path2PPI, a new bioinformatics approach, was developed. Path2PPI enables the prediction of protein-protein interaction networks of particular pathways by means of a homology comparison approach and was applied to construct the protein-protein interaction network of autophagy in P. anserina.
The predicted network was extended by experimental data, comprising the transcriptome data as well as newly generated protein-protein interaction data achieved from a yeast two-hybrid analysis. Using different mathematical and statistical methods the topological properties of the constructed network had been compared with those of randomly generated networks to approve its biological significance. In addition, based on this topological and functional analysis, the most important proteins were determined and functional modules were identified, which correspond to the different sub-pathways of autophagy. Due to the integrated transcriptome data the autophagy network could be linked to the ageing process. For example, different proteins had been identified, which genes are continuously up- or down-regulated during ageing and it was shown for the first time that autophagy-associated genes are significantly often co-expressed during ageing.
The presented biological network provides a systems biological view on autophagy and enables further studies, which aim to analyse the relationship of autophagy and ageing. Furthermore, it allows the investigation of potential methods for intervention into the ageing process and to extend the healthy lifespan of P. anserina as well as of other eukaryotic organisms, in particular humans.
Die vorliegende Dissertation behandelt die Entwicklung eines Verkehrssimulationssystems, welches vollautomatisch aus Landkarten Simulationsgraphen erstellen kann. Der Fokus liegt bei urbanen Simulationsstudien in beliebigen Gemeinden und Städten. Das zweite fundamentale Standbein dieser Arbeit ist daher die Konstruktion von Verkehrsmodellen, die die wichtigsten Verkehrsteilnehmertypen im urbanen Bereich abbilden. Es wurden Modelle für Autos, Fahrräder und Fußgänger entwickelt.
Die Betrachtung des Stands der Forschung in diesem Bereich hat ergeben, dass die Verknüpfung von automatischer Grapherstellung und Modellen, die die Wechselwirkungen der verschiedenen Verkehrsteilnehmertypen abbilden, von keinem vorhandenen System geleistet wird. Es gibt grundlegend zwei Gruppen von Verkehrssimulationssystemen. Zum Einen existieren Systeme, die hohe Genauigkeiten an Simulationsergebnissen erzielen und dafür exakte (teil-)manuelle Modellierung der Gegebenheiten im zu simulierenden Bereich benötigen. Es werden in diesem Bereich meist Verkehrsmodelle simuliert, die die Verhaltensweisen der Verkehrsteilnehmer sehr gut abbilden und hierfür einen hohen Berechnungsaufwand benötigen. Auf der anderen Seiten existieren Simulationssysteme, die Straßengraphen automatisch erstellen können, darauf jedoch sehr vereinfachte Verkehrsmodelle simulieren. Es werden meist nur Autobewegungen simuliert. Der Nutzen dieser Herangehensweise ist die Möglichkeit, sehr große Szenarien simulieren zu können.
Im Rahmen dieser Arbeit wird ein System mit Eigenschaften beider grundlegenden Ansätze entwickelt, um multimodalen innerstädtischen Verkehr auf Basis automatisch erstellter Straßengraphen simulieren zu können. Die Entwicklung eines neuen Verkehrssimulationssystems erschien notwendig, da sich zum Zeitpunkt der Literaturbetrachtung kein anderes vorhandenes System für die Nutzung zur Erfüllung der genannten Zielstellung eignete. Das im Rahmen dieser Arbeit entwickelte System heißt MAINSIM (MultimodAle INnerstädtische VerkehrsSIMulation).
Die Simulationsgraphen werden aus Kartenmaterial von OpenStreetMap extrahiert. Kartenmaterial wird zuerst in verschiedene logische Layer separiert und anschließend zur Bestimmung eines Graphen des Straßennetzes genutzt. Eine Gruppe von Analyseschritten behebt Ungenauigkeiten im Kartenmaterial und ergänzt Informationen, die während der Simulation benötigt werden (z.B. die Verbindungsrichtung zwischen zwei Straßen). Das System verwendet Geoinformationssystemkomponenten zur Verarbeitung der Geodaten. Dies birgt den Vorteil der einfachen Erweiterbarkeit um weitere Datenquellen.
Die Verkehrssimulation verwendet mikroskopische Verhaltensmodelle. Jeder einzelne Verkehrsteilnehmer wird somit simuliert. Das Modell für Autos basiert auf dem in der Verkehrsforschung weit genutzten Nagel-Schreckenberg-Modell. Es verfügt jedoch über zahlreiche Modifikationen und Erweiterungen, um das Modell auch abseits von Autobahnen nutzen zu können und weitere Verhaltensweisen zu modellieren. Das Fahrradmodell entsteht durch geeignete Parametrisierung aus dem Automodell. Zur Entwicklung des Fußgängermodells wurde Literatur über das Verhalten von Fußgängern diskutiert, um daraus geeignete Eigenschaften (z.B. Geschwindigkeiten und Straßenüberquerungsverhaltensmuster) abzuleiten. MAINSIM ermöglicht folglich die Betrachtung des Verkehrsgeschehens auch aus der Sicht der Gruppe der Fußgänger oder Fahrradfahrer und kann deren Auswirkungen auf den Straßenverkehr einer ganzen Stadt bestimmen.
Das Automodell wurde auf Autobahnszenarien und innerstädtischen Straßengraphen evaluiert. Es konnte die gut verstandenen Zusammenhänge zwischen Verkehrsdichte, -fluss und -geschwindigkeit reproduzieren. Zur Evaluierung von Fahrradmodellen liegen nach dem besten Wissen des Autors keine Studien vor. Daher wurden an dieser Stelle der Einfluss der Fahrradfahrer auf den Straßenverkehr und die von Fahrrädern gefahrenen Geschwindigkeiten untersucht. Das Fußgängermodell konnte die aus der Literaturbetrachtung ermittelten Verhaltensweisen abbilden.
Nachdem die wichtigsten Komponenten von MAINSIM untersucht wurden, begannen Fallstudien, die verschiedene Gebiete abdecken. Die wichtigsten Ergebnisse aus diesem Teil der Arbeit sind:
- Es ist möglich, mit Hilfe maschineller Lernverfahren Staus innerhalb Frankfurts vorherzusagen.
- Nonkonformismus bezüglich der Verkehrsregeln kann je nach Verhalten den Verkehrsfluss empfindlich beeinflussen, kann aber auch ohne Effekt bleiben.
- Mit Hilfe von Kommunikationstechniken könnte in der Zukunft die Routenplanung von Autos verbessert werden. Ein Verfahren auf Basis von Pheromonspuren wurde im Rahmen dieser Arbeit untersucht.
- MAINSIM eignet sich zur Simulation großer Szenarien. In der letzten Fallstudie dieser Arbeit wurde der Autoverkehr eines Simulationsgebietes um Frankfurt am Main herum mit ca. 1,6 Mio. Trips pro Tag simuliert. Da MAINSIM über ein Kraftstoffverbrauchs- und CO2-Emissionsmodell verfügt, konnten die CO2-Emissionen innerhalb von Frankfurt ermittelt werden. Eine angekoppelte Simulation des Wetters mit Hilfe einer atmosphärischen Simulation zeigte, wie sich die Gase innerhalb Frankfurts verteilen.
Für den professionellen Einsatz in der Verkehrsforschung muss das entwickelte Simulationssystem um eine Methode zur Kalibrierung auf Sensordaten im Simulationsgebiet erweitert werden. Die vorhandenen Ampelschaltungen bilden nicht reale Ampeln ab. Eine Erweiterung des Systems um die automatische Integrierung maschinell lesbarer Schaltpläne von Ampeln im Bereich des Simulationsgebietes würde die Ergebnisgüte weiter erhöhen.
MAINSIM hat mehrere Anwendungsgebiete. Es können sehr schnell Simulationsgebiete modelliert werden. Daher bietet sich die Nutzung für Vorabstudien an. Wenn große Szenarien simuliert werden müssen, um z.B. die Verteilung der CO2-Emissionen innerhalb einer Stadt zu ermitteln, kann MAINSIM genutzt werden. Es hat sich im Rahmen dieser Arbeit gezeigt, dass Fahrräder und Fußgänger einen Effekt auf die Mengen des Kraftstoffverbrauchs von Autos haben können. Es sollte bei derartigen Szenarien folglich ein Simulationssysytem genutzt werden, welches die relevanten Verkehrsteilnehmertypen abbilden kann. Zur Untersuchung weiterer wissenschaftlicher Fragestellungen kann MAINSIM beliebig erweitert werden.
Wir betrachten Algorithmen für strategische Kommunikation mit Commitment Power zwischen zwei rationalen Parteien mit eigenen Interessen. Wenn eine Partei Commitment Power hat, so legt sie sich auf eine Handlungsstrategie fest und veröffentlicht diese und kann nicht mehr davon abweichen.
Beide Parteien haben Grundinformation über den Zustand der Welt. Die erste Partei (S) hat die Möglichkeit, diesen direkt zu beobachten. Die zweite Partei (R) trifft jedoch eine Entscheidung durch die Wahl einer von n Aktionen mit für sie unbekanntem Typ. Dieser Typ bestimmt die möglicherweise verschiedenen, nicht-negativen Nutzwerte für S und R. Durch das Senden von Signalen versucht S, die Wahl von R zu beeinflussen. Wir betrachten zwei Grundszenarien: Bayesian Persuasion und Delegated Search.
In Bayesian Persuasion besitzt S Commitment Power. Hier legt sich S sich auf ein Signalschema φ fest und teilt dieses R mit. Es beschreibt, welches Signal S in welcher Situation sendet. Erst danach erfährt S den wahren Zustand der Welt. Nach Erhalt der durch φ bestimmten Signale wählt R eine der Aktionen. Das Wissen um φ erlaubt R die Annahmen über den Zustand der Welt in Abhängigkeit von den empfangenen Signalen zu aktualisieren. Dies muss S für das Design von φ berücksichtigen, denn R wird Empfehlungen nicht folgen, die S auf Kosten von R übervorteilen. Wir betrachten das Problem aus der Sicht von S und beschreiben Signalschemata, die S einen möglichst großen Nutzen garantieren.
Zuerst betrachten wir den Offline-Fall. Hier erfährt S den kompletten Zustand der Welt und schickt daraufhin ein Signal an R. Wir betrachten ein Szenario mit einer beschränkten Anzahl k ≤ n Signale. Mit nur k Signalen kann S höchstens k verschiedene Aktionen empfehlen. Für verschiedene symmetrische Instanzen beschreiben wir einen Polynomialzeitalgorithmus für die Berechnung eines optimalen Signalschemas mit k Signalen.
Weiterhin betrachten wir eine Teilmenge von Instanzen, in denen die Typen aus bekannten, unabhängigen Verteilungen gezogen werden. Wir beschreiben Polynomialzeitalgorithmen, die ein Signalschema mit k Signalen berechnen, das einen konstanten Approximationsfaktor im Verhältnis zum optimalen Signalschema mit k Signalen garantiert.
Im Online-Fall werden die Aktionstypen einzeln in Runden aufgedeckt. Nach Betrachtung der aktuellen Aktion sendet S ein Signal und R muss sofort durch Wahl oder Ablehnung der Aktion darauf reagieren. Der Prozess endet mit der Wahl einer Aktion. Andernfalls wird der nächste Aktionstyp aufgedeckt und vorherige Aktionen können nicht mehr gewählt werden. Als Richtwert für unsere Online-Signalschemata verwenden wir das beste Offline-Signalschema.
Zuerst betrachten wir ein Szenario mit unabhängigen Verteilungen. Wir zeigen, wie ein optimales Signalschema in Polynomialzeit bestimmt werden kann. Jedoch gibt es Beispiele, bei denen S – anders als im Offline-Fall – im Online-Fall keinen positiven Wert erzielen kann. Wir betrachten daraufhin eine Teilmenge der Instanzen, für die ein einfaches Signalschema einen konstanten Approximationsfaktor garantiert und zeigen dessen Optimalität.
Zusätzlich betrachten wir 16 verschiedene Szenarien mit unterschiedlichem Level an Information für S und R und unterschiedlichen Zielfunktionen für S und R unter der Annahme, dass die Aktionstypen a priori unbekannt sind, aber in uniform zufälliger Reihenfolge aufgedeckt werden. Für 14 Fälle beschreiben wir Signalschemata mit konstantem Approximationsfaktor. Solche Schemata existieren für die verbleibenden beiden Fälle nicht. Zusätzlich zeigen wir für die meistern Fälle, dass die beschriebenen Approximationsgarantien optimal sind.
Im zweiten Teil betrachten wir eine Online-Variante von Delegated Search. Hier besitzt nun R Commitment Power. Die Aktionstypen werden aus bekannten, unabhängigen Verteilungen gezogen. Bevor S die realisierten Typen beobachtet, legt R sich auf ein Akzeptanzschema φ fest. Für jeden Typen gibt φ an, mit welcher Wahrscheinlichkeit R diesen akzeptiert. Folglich versucht S, eine Aktion mit einem guten Typen für sich selbst zu finden, der von R akzeptiert wird. Da der Prozess online abläuft, muss S für jede Aktion einzeln entscheiden, diese vorzuschlagen oder zu verwerfen. Nur empfohlene Aktionen können von R ausgewählt werden.
Für den Offline-Fall sind für identisch verteilte Aktionstypen konstante Approximationsfaktoren im Vergleich zu einer Aktion mit optimalem Wert für R bekannt. Wir zeigen, dass R im Online-Fall im Allgemeinen nur eine Θ(1/n)-Approximation erzielen kann. Der Richtwert ist der erwartete Wert für eine eindimensionale Online-Suche von R.
Da für die Schranke eine exponentielle Diskrepanz in den Werten der Typen für S benötigt wird, betrachten wir parametrisierte Instanzen. Die Parameter beschränken die Werte für S bzw. das Verhältnis der Werte für R und S. Wir zeigen (beinahe) optimale logarithmische Approximationsfaktoren im Bezug auf diese Parameter, die von effizient berechenbaren Schemata garantiert werden.
An exploratory latent class analysis of student expectations towards learning analytics services
(2021)
For service implementations to be widely adopted, it is necessary for the expectations of the key stakeholders to be considered. Failure to do so may lead to services reflecting ideological gaps, which will inadvertently create dissatisfaction among its users. Learning analytics research has begun to recognise the importance of understanding the student perspective towards the services that could be potentially offered; however, student engagement remains low. Furthermore, there has been no attempt to explore whether students can be segmented into different groups based on their expectations towards learning analytics services. In doing so, it allows for a greater understanding of what is and is not expected from learning analytics services within a sample of students. The current exploratory work addresses this limitation by using the three-step approach to latent class analysis to understand whether student expectations of learning analytics services can clearly be segmented, using self-report data obtained from a sample of students at an Open University in the Netherlands. The findings show that student expectations regarding ethical and privacy elements of a learning analytics service are consistent across all groups; however, those expectations of service features are quite variable. These results are discussed in relation to previous work on student stakeholder perspectives, policy development, and the European General Data Protection Regulation (GDPR).
The ALICE High-Level-Trigger (HLT) is a large scale computing farm designed and constructed for the purpose of the realtime reconstruction of particle interactions (events) inside the ALICE detector. The reconstruction of such events is based on the raw data produced in collisions inside the ALICE at the Large Hadron Collider. The online reconstruction in the HLT allows the triggering on certain event topologies and a significant data reduction by applying compression algorithms. Moreover, it enables a real-time verification of the quality of the data.
To receive the raw data from the various sub-detectors of ALICE, the HLT is equipped with 226 custom built FPGA-based PCI-X cards, the H-RORCs. The H-RORC interfaces the detector readout electronics to the nodes of the HLT farm. In addition to the transfer of raw data, 108 H-RORCs host 216 Fast-Cluster-Finder (FCF) processors for the Time-Projection-Chamber (TPC). The TPC is the main tracking detector of ALICE and contributes with up to 16 GB/s to over 90% of the overall data volume. The FCF processor implements the first of two steps in the data reconstruction of the TPC. It calculates the space points and their properties from charge clouds (clusters) created by charged particles traversing the TPCs gas volume. Those space points are not only the base for the tracking algorithm, but also allow for a Huffman-based data compression, which reduces the data volume by a factor of 4 to 6.
The FCF processor is designed to cope with any incoming data rate up to the maximum bandwidth of the incoming optical link (160 MB/s) without creating back-pressure to the detectors readout electronics. A performance comparison with the software implementation of the algorithm shows a speedup factor of about 20 compared with one AMD Opteron 6172 Core @ 2.1 GHz, the CPU type used in the HLT during the LHC Run1 campaign. Comparison with an Intel E5-2690 Core @ 3.0 GHz, the CPU type used by the HLT for the LHC Run2 campaign, results in a speedup factor of 8.5. In total numbers, the 216 FCF processors provide the computing performance of 4255 AMD Opteron cores or 2203 Intel cores of the previously mentioned type. The performance of the reconstruction with respect to the physics analysis is equivalent or better than the official ALICE Offline clusterizer. Therefore, ALICE data taking was switched in 2011 to FCF cluster recording and compression only, discarding the raw data from the TPC. Due to the capability to compress the clusters, the recorded data volume could be increased by a factor of 4 to 6.
For the LHC Run3 campaign, starting in 2020, the FCF builds the foundation of the ALICE data taking and processing strategy. The raw data volume (before processing) of the upgraded TPC will exceed 3 TB/s. As a consequence, online processing of the raw data and compression of the results before it enters the online computing farms is an essential and crucial part of the computing model.
Within the scope of this thesis, the H-RORC card and the FCF processor were developed and built from scratch. It covers the conceptual design, the optimisation and implementation, as well as the verification. It is completed by performance benchmarks and experiences from real data taking.
Das Projekt anan ist ein Werkzeug zur Fehlersuche in verteilten Hochleistungsrechnern. Die Neuheit des Beitrags besteht darin, dass die bekannten Methoden, die bereits erfolgreich zum Debuggen von Soft- und Hardware eingesetzt werden, auf Hochleistungs-Rechnen übertragen worden sind. Im Rahmen der vorliegenden Arbeit wurde ein Werkzeug namens anan implementiert, das bei der Fehlersuche hilft. Außerdem kann es als dynamischeres Monitoring eingesetzt werden. Beide Einsatzzwecke sind
getestet worden.
Das Werkzeug besteht aus zwei Teilen:
1. aus einem Teil namens anan, der interaktiv vom Nutzer bedient wird
2. und aus einem Teil namens anand, der automatisiert die verlangten Messwerte erhebt und nötigenfalls Befehle ausführt.
Der Teil anan führt Sensoren aus — kleine mustergesteuerte Algorithmen —, deren Ergebnisse per anan zusammengeführt werden. In erster Näherung lässt anan sich als Monitoring beschreiben, welches (1) schnell umkonfiguriert werden (2) komplexere Werte messen kann, die über Korrelationen einfacher Zeitreihen hinausgehen.
Um Wissen in einer Form abzulegen, in der es automatisiert verarbeitet werden kann, werden unter anderem Ontologien verwendet. Ontologien erlauben über einen als Inferenz bezeichneten Prozess die Ableitung neuen Wissens. Bei inhaltlichen Überschneidungen werden Ontologien über Ontologie-Alignments miteinander verbunden, die Entitäten aus den verschiedenen Ontologien in Beziehung zueinander setzen. Üblicherweise werden diese Alignments als Mengen von Äquivalenzen formuliert, die beschreiben, welche Konzepte aus einer Ontologie Konzepten aus einer anderen Ontologie entsprechen. Ebenfalls verbreitet sind Ober- und Unterklassenbeziehungen in Alignments.
Diese Ontologie-Alignments werden zum Beispiel in der Biomedizin in Forschungsdatenbanken verwendet, da durch Alignments Informationen aus verschiedenen Bereichen zusammengeführt werden können. Der manuelle Aufwand, um große Ontologien und Alignments zu erstellen, ist sehr hoch. Dementsprechend wäre es wünschenswert, bei einer Veränderung von Ontologien nicht wieder von vorne beginnen und eine neue Ontologie erstellen zu müssen und möglichst viel aus der veränderten Ontologie und den die Ontologie betreffenden Alignments wiederverwenden zu können. Daher sollten möglichst automatisierte Verfahren verwendet werden. Diese Arbeit untersucht vier Ansätze, um die Anpassung von Alignments an Veränderungen in Ontologien zu automatisieren.
Der erste Ansatz bezieht Inferenzen in den Prozess zur Vorhersage von Alignment-Änderungen mit ein. Dazu werden die Inferenzen vor und nach der Änderung der Ontologien berechnet und auf Basis der Unterschiede mit einem regelbasierten Algorithmus bestimmt, wie sich das Alignment ändern soll. Der zweite Ansatz, wie auch die weiteren Ansätze, hat nicht zum Ziel das Alignment direkt anzupassen. Stattdessen soll vorhergesagt werden, welche Teile des Alignments angepasst werden müssen. Dazu werden die Ontologien und das Alignment als Wissensgraph-Embeddings repräsentiert. Diese Embeddings bilden Knoten aus den Ontologien in einen Raum mit 300-1000 Dimensionen so ab, dass in dem Raum auch die Beziehungen zwischen den Entitäten der Ontologien repräsentiert werden können. Diese Embeddings werden dann verwendet, um verschiedene Klassifikationsalgorithmen zu trainieren. Auf diese Weise wird vorhergesagt, welche Teile des Alignments sich verändern werden. Der dritte Ansatz verbindet Embeddings mit einem Veränderungsmodell. Das Veränderungsmodell kategorisiert die an den Ontologien vorgenommenen Veränderungen. Auf diese Kategorisierung und das Embedding werden dann Klassifikationsalgorithmen angewandt. Der vierte Ansatz verwendet eine speziell auf Wissensgraphen ausgerichtete Architektur für neuronale Netze, sogenannte Graph Convolutional Networks, um Veränderungen an Alignments vorher zu sagen.
Diese Ansätze werden auf ihre jeweiligen Vor- und Nachteile untersucht. Dazu werden die Verfahren an zwei Anwendungsfällen untersucht. Der Ansatz zur regelbasierten Einbeziehung von Inferenzen wird anhand eines Anwendungsbeispiels aus dem Bereich der Interweaving Systems betrachtet. In dem Beispiel wird eine allgemeine Methode für Interweaving Systems angewandt um das Selbstmanagement von Ampelsteuerungen zu ermöglichen. Die auf maschinellem Lernen aufbauenden Ansätze werden auf einem Auszug aus der biomedizinischen Forschungsdatenbank UMLS evaluiert.
Dabei konnte festgestellt werden, dass die betrachteten Ansätze grundsätzlich zur Anpassung von Alignments an Ontologie-Veränderungen eingesetzt werden können. Der Ansatz zur regelbasierten Einbeziehung von Inferenzen kann dabei vor allem auf sehr kleinen Datensätzen eingesetzt werden, bei denen alle Gesetzmäßigkeiten der Veränderungen grundsätzlich bekannt sind. Diese Anwendbarkeit ergibt sich aus dem Entwurf der Problemstellung für den ersten Ansatz. Die auf maschinellem Lernen aufbauenden Ansätze eignen sich besonders für große Datensätze und bieten den Vorteil, dass auch ohne ein vollständiges Verständnis des Veränderungsprozesses Vorhersagen getroffen werden können.
Unter den Ansätzen, die maschinelles Lernen einsetzen, zeigte die Einbeziehung von Veränderungsmodellen keine Vorteile gegenüber den anderen Ansätzen. Auf einem etwas
kleineren Datensatz waren die Ergebnisse des Embedding-basierten Ansatzes und der Relational Graph Convolutional Networks vergleichbar, während auf einem größeren Datensatz
die Graph Convolutional Networks etwas bessere Ergebnisse erreichen konnten.
Weitere Ergebnisse dieser Arbeit stellen eine Formalisierung der Problemstellung der Anpassung von Ontologie-Alignments an Veränderungen sowie eine formale Darstellung der Ansätze dar. Ein weiterer Beitrag der Arbeit ist die Vorstellung eines Anwendungsfalls aus dem Bereich der Interweaving Systems für Ontologie-Alignments. Außerdem wurde das Problem der Anpassung von Alignments an Veränderungen so formuliert, dass es mithilfe von
maschinellem Lernen betrachtet werden kann.
Antimicrobial resistance became a serious threat to the worldwide public health in this century. A better understanding of the mechanisms, by which bacteria infect host cells and how the host counteracts against the invading pathogens, is an important subject of current research. Intracellular bacteria of the Salmonella genus have been frequently used as a model system for bacterial infections. Salmonella are ingested by contaminated food or water and cause gastroenteritis and typhoid fever in animals and humans. Once inside the gastrointestinal tract, Salmonella can invade intestinal epithelial cells. The host cell can fight against intracellular pathogens by a process called xenophagy. For complex systems, such as processes involved in the bacterial infection of cells, computational systems biology provides approaches to describe mathematically how these intertwined mechanisms in the cell function. Computational systems biology allows the analysis of biological systems at different levels of abstraction. Functional dependencies as well as dynamic behavior can be studied. In this thesis, we used the Petri net formalism to gain a better insight into bacterial infections and host defense mechanisms and to predict cellular behavior that can be tested experimentally. We also focused on the development of new computational methods.
In this work, the first realization of a mathematical model of the xenophagic capturing of Salmonella enterica serovar Typhimurium in epithelial cells was developed. The mathematical model expressed in the Petri net formalism was constructed in an iterative way of modeling and analyses. For the model verification, we analyzed the Petri net, including a computational performance of knockout experiments named in silico knockouts, which was established in this work. The in silico knockouts of the proposed Petri net are consistent with the published experimental perturbation studies and, thus, ensures the biological credibility of the Petri net. In silico knockouts that have not been experimentally investigated yet provide hypotheses for future investigations of the pathway.
To study the dynamic behavior of an epithelial cell infected with Salmonella enterica serovar Typhimurium, a stochastic Petri net was constructed. In experimental research, a decision like "Which incubation time is needed to infect half of the epithelial cells with Salmonella?" is based on experience or practicability. A mathematical model can help to answer these questions and improve experimental design. The stochastic Petri net models the cell at different stages of the Salmonella infection. We parameterized the model by a set of experimental data derived from different literature sources. The kinetic parameters of the stochastic Petri net determine the time evolution of the bacterial infection of a cell. The model captures the stochastic variation and heterogeneity of the intracellular Salmonella population of a single cell over time. The stochastic Petri net is a valuable tool to examine the dynamics of Salmonella infections in epithelial cells and generate valuable information for experimental design.
In the last part of this thesis, a novel theoretical method was introduced to perform knockout experiments in silico. The new concept of in silico knockouts is based on the computation of signal flows at steady state and allows the determination of knockout behavior that is comparable to experimental perturbation behavior. In this context, we established the concept of Manatee invariants and demonstrated the suitability of their application for in silico knockouts by reflecting biological dependencies from the signal initiation to the response. As a proof of principle, we applied the proposed concept of in silico knockouts to the Petri net of the xenophagic recognition of Salmonella. To enable the application of in silico knockouts for the scientific community, we implemented the novel method in the software isiKnock. isiKnock allows the automatized performance and visualization of in silico knockouts in signaling pathways expressed in the Petri net formalism. In conclusion, the knockout analysis provides a valuable method to verify computational models of signaling pathways, to detect inconsistencies in the current knowledge of a pathway, and to predict unknown pathway behavior.
In summary, the main contributions of this thesis are the Petri net of the xenophagic capturing of Salmonella enterica serovar Typhimurium in epithelial cells to study the knockout behavior and the stochastic Petri net of an epithelial cell infected with Salmonella enterica serovar Typhimurium to analyze the infection dynamics. Moreover, we established a new method for in silico knockouts, including the concept of Manatee invariants and the software isiKnock. The results of these studies are useful to a better understanding of bacterial infections and provide valuable model analysis techniques for the field of computational systems biology.
In the first part of this thesis, we introduce the concept of prospective strict no-arbitrage for discrete-time financial market models with proportional transaction. The prospective strict no-arbitrage condition, which is a variant of strict no-arbitrage, is slightly weaker than the robust no-arbitrage condition. It still implies that the set of portfolios attainable from zero initial endowment is closed in probability. Consequently, prospective strict no-arbitrage implies the existence of consistent prices, which may lie on the boundary of the bid-ask spread. A weak version of prospective strict no-arbitrage turns out to be equivalent to the existence of a consistent price system.
In continuous-time financial market models with proportional transaction costs, efficient friction, i.e., nonvanishing transaction costs, is a standing assumption. Together with robust no free lunch with vanishing risk, it rules out strategies of infinite variation which usually appear in frictionless financial markets. In the second part of this thesis, we show how models with and without transaction costs can be unified. The bid and the ask price of a risky asset are given by cadlag processes which are locally bounded from below and may coincide at some points. In a first step, we show that if the bid-ask model satisfies no unbounded profit with bounded risk for simple long-only strategies, then there exists a semimartingale lying between the bid and the ask price process.
In a second step, under the additional assumption that the zeros of the bid-ask spread are either starting points of an excursion away from zero or inner points from the right, we show that for every bounded predictable strategy specifying the amount of risky assets, the semimartingale can be used to construct the corresponding self-financing risk-free position in a consistent way. Finally, the set of most general strategies is introduced, which also provides a new view on the frictionless case.
Chatbots are a promising technology with the potential to enhance workplaces and everyday life. In terms of scalability and accessibility, they also offer unique possibilities as communication and information tools for digital learning. In this paper, we present a systematic literature review investigating the areas of education where chatbots have already been applied, explore the pedagogical roles of chatbots, the use of chatbots for mentoring purposes, and their potential to personalize education. We conducted a preliminary analysis of 2,678 publications to perform this literature review, which allowed us to identify 74 relevant publications for chatbots’ application in education. Through this, we address five research questions that, together, allow us to explore the current state-of-the-art of this educational technology. We conclude our systematic review by pointing to three main research challenges: 1) Aligning chatbot evaluations with implementation objectives, 2) Exploring the potential of chatbots for mentoring students, and 3) Exploring and leveraging adaptation capabilities of chatbots. For all three challenges, we discuss opportunities for future research.
AttendAffectNet-emotion prediction of movie viewers using multimodal fusion with self-attention
(2021)
In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, these typically, while ignoring the correlation between multiple modality inputs, ignore the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture—namely AttendAffectNet (AAN)—uses the self-attention mechanism for predicting the emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are considered for predicting emotions (and expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie to, thereby, capture the relationships between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying self-attention first on vectors of features obtained from different modalities in each movie segment and then on the feature representations of all subsequent (temporal) movie segments. We extensively trained and validated our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting the emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately. In addition, the Feature AAN outperformed other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performed better than the baseline models when predicting the valence dimension.
Diese Bachelorarbeit befasst sich mit der Themenklassifikation von unstrukturiertem Text. Aufgrund der stetig steigenden Menge von textbasierten Daten werden automatisierte Klassifikationsmethoden in vielen Disziplinen benötigt und erforscht. Aufbauend auf dem text2ddc-Klassifikator, der am Text Technology Lab der Goethe-Universität Frankfurt am Main entwickelt wurde, werden die Auswirkungen der Vergrößerung des Trainingskorpus mittels unterschiedlicher Methoden untersucht. text2ddc nutzt die Dewey Decimal Classification (DDC) als Zielklassifikation und wird trainiert auf Artikeln der Wikipedia. Nach einer Einführung, in der Grundlagen beschrieben werden, wird das Klassifikationsmodell von text2ddc vorgestellt, sowie die Probleme und daraus resultierenden Aufgaben betrachtet. Danach wird die Aktualisierung der bisherigen Daten beschrieben, gefolgt von der Vorstellung der verschiedenen Methoden, das Trainingskorpus zu erweitern. Mit insgesamt elf Sprachen wird experimentiert. Die Evaluation zeigt abschließend die Verbesserungen der Qualität der Klassifikation mit text2ddc auf, diskutiert die problematischen Fälle und gibt Anregungen für weitere zukünftige Arbeiten.
Begriffe sind häufig nicht eindeutig. Eine „Bank“ kann ein Finanzinstitut oder eine Sitzgelegenheit sein und die Stadt Frankfurt existiert mehr als einmal. Dennoch können sie in vielen Fällen problemlos von Menschen unterschieden werden. Computer sind noch nicht in der Lage, diese Leistung mit vergleichbarer Genauigkeit zu erfüllen.
Der in dieser Arbeit vorgestellte Ansatz baut auf dem für das Deutsche bereits gute Ergebnisse erzielenden fastSense auf und verwendet ein neuronales Netz, um Namen und Begriffe in englischen Texten mit Hilfe der Wikipedia zu disambiguieren. Dabei konnte eine Genauigkeit von bis zu 89,5% auf Testdaten erreicht werden.
Mit dem entwickelten Python-Modul kann das trainierte Modell in bestehende Anwendungen eingebunden werden. Die im Modul enthaltenen Programme ermöglichen es, neue Modelle zu trainieren und zu testen.
Autonomous steering of an electric bicycle based on sensor fusion using model predictive control
(2019)
In this thesis a control and steering module for an autonomous bicycle was developed. Based on sensor fusion and model predictive control, the module is able to trace routes autonomously.
The system is developed to run on a Raspberry Pi. An ultrasonic sensor and a 2D Lidar sensor are used for distance measurements. The vehicle’s position is determined by using GPS signals. Additionally, a camera is used to capture pictures for the roadside detection. In order to recognize the road and the position of the vehicle on it, computer vision techniques are used. The captured images are denoised, Canny edge detection is performed and a perspective transformation is applied. Thereafter a sliding window algorithm selects the edges belonging to the roadside and a second order polynomial is fitted to the selected data. Based on this, the road curvature and the lateral position of the vehicle on the road are calculated. The implemented software is thus able to detect straight and curved roads as well as the vehicle’s lateral offset.
A route planning module was implemented to navigate the vehicle from the start to the destination coordinates. This is done by creating an abstract graph of the roads and using Dijkstra’s algorithm to determine the shortest path.
Four MPC controllers were implemented to control the movements of the vehicle. They are based on state space equations derived from the linear single-track vehicle model. This relatively straightforward model makes it possible to predict the vehicle behavior and is efficient to compute. Each controller was built with different parameters for different vehicle speeds to account for the non-linearity of the system. The controllers simulate the future states of the system at each timeslot and select appropriate control signals for steering, throttle and brakes.
In this thesis, all the components of the steering and control module were individually validated. It was established that the each individual component works as expected and certain constraints and accuracy limits were identified. Finally, the closed loop capabilities of the system were assessed using a test vehicle. Despite some limitations imposed by this setup, it was shown that the control module is indeed capable of autonomously navigating a vehicle and avoiding collisions.
Machine Learning (ML) is so pervasive in our todays life that we don't even realise that, more often than expected, we are using systems based on it. It is also evolving faster than ever before. When deploying ML systems that make decisions on their own, we need to think about their ignorance of our uncertain world. The uncertainty might arise due to scarcity of the data, the bias of the data or even a mismatch between the real world and the ML-model. Given all these uncertainties, we need to think about how to build systems that are not totally ignorant thereof. Bayesian ML can to some extent deal with these problems. The specification of the model using probabilities provides a convenient way to quantify uncertainties, which can then be included in the decision making process.
In this thesis, we introduce the Bayesian ansatz to modeling and apply Bayesian ML models in finance and economics. Especially, we will dig deeper into Gaussian processes (GP) and Gaussian process latent variable model (GPLVM). Applied to the returns of several assets, GPLVM provides the covariance structure and also a latent space embedding thereof. Several financial applications can be build upon the output of the GPLVM. To demonstrate this, we build an automated asset allocation system, a predictor for missing asset prices and identify other structure in financial data.
It turns out that the GPLVM exhibits a rotational symmetry in the latent space, which makes it harder to fit. Our second publication reports, how to deal with that symmetry. We propose another parameterization of the model using Householder transformations, by which the symmetry is broken. Bayesian models are changed by reparameterization, if the prior is not changed accordingly. We provide the correct prior distribution of the new parameters, such that the model, i.e. the data density, is not changed under the reparameterization. After applying the reparametrization on Bayesian PCA, we show that the symmetry of nonlinear models can also be broken in the same way.
In our last project, we propose a new method for matching quantile observations, which uses order statistics. The use of order statistics as the likelihood, instead of a Gaussian likelihood, has several advantages. We compare these two models and highlight their advantages and disadvantages. To demonstrate our method, we fit quantiled salary data of several European countries. Given several candidate models for the fit, our method also provides a metric to choose the best option.
We hope that this thesis illustrates some benefits of Bayesian modeling (especially Gaussian processes) in finance and economics and its usage when uncertainties are to be quantified.
This article deals with the solution of linear ill-posed equations in Hilbert spaces. Often, one only has a corrupted measurement of the right hand side at hand and the Bakushinskii veto tells us, that we are not able to solve the equation if we do not know the noise level. But in applications it is ad hoc unrealistic to know the error of a measurement. In practice, the error of a measurement may often be estimated through averaging of multiple measurements. We integrated that in our anlaysis and obtained convergence to the true solution, with the only assumption that the measurements are unbiased, independent and identically distributed according to an unknown distribution.
Die digitale Pathologie ist ein neues, aber stetig wachsendes, Feld in der Medizin. Die kontinuierliche Entwicklung von verbesserten digitalen Scannern erlaubt heute das Abscannen von kompletten Gewebeschnitten und Whole Slide Images gewinnen an Bedeutung. Ziel dieser Arbeit ist die Methodenentwicklung zur Analyse von Whole Slide Images des klassischen Hodgkin Lymphoms. Das Hodgkin-Lymphom, oder Morbus Hodgkin, ist eine Tumorerkrankung des Lymphsystems, bei der die monoklonalen Tumorzellen in der Regel von B-Lymphozyten im Vorläuferstadium abstammen.
Etwas mehr als 9.000 Hodgkin-Lymphom-Fälle werden jährlich in den USA diagnostiziert. Zwar ist die 5-Jahre-Überlebensrate für Hodgkin-Lymphome mit 85,3 % vergleichsweise hoch, dennoch werden etwa 1.100 Todesfälle pro Jahr in den USA registriert. Auf mikroskopischer Ebene sind die Hodgkin-Reed-Sternberg Zellen (HRS-Zellen) typisch für das klassische Hodgkin Lymphom. HRS-Zellen haben einen oder mehrere Zellkerne, die stark vergrößert sind und eine grobe Chromatinstruktur aufweisen. Immunhistologisch gibt es für HRS-Zellen charakterisierende Marker, so sind HRS-Zellen positiv für den Aktivierungsmarker CD30.
Neben der konventionellen Mikroskopie, ermöglichen Scanner das Digitalisieren von ganzen Objektträgern (Whole Slide Image). Whole Slide Images werden bisher wenig in der Routinediagnostik eingesetzt. Ein großer Vorteil von digitalisierten Gewebeschnitten bietet sich bei der computergestützten Analyse. Automatisierte Bildanalyseverfahren wie Zellerkennung können Pathologen bei der Diagnose unterstützen, indem sie umfassende Statistiken zur Anzahl und Verteilung von immungefärbten Zellen bereitstellen.
Die untersuchten immunohistologischen Bilder wurden vom Dr. Senckenbergisches Institut für Pathologie des Universitätsklinikums Frankfurt bereit gestellt. Die betrachteten Gewebeschnitte sind gegen CD30 immungefärbt, einem Membranrezeptor, welcher in HRS-Zellen und aktivierten Lymphozyten exprimiert wird. Die Gewebeschnitte wurden mit einem Aperio ScanScope slide scanner digitalisiert und liegen mit einer hohen Auflösung von 0,25 μm pro Pixel vor. Bei den vorliegenden Gewebeschnittgrößen ergeben sich Bilder mit bis zu 90.000 x 90.000 Pixeln.
Der untersuchte Bilddatensatz umfasst 35 Bilder von Lymphknotengewebeschnitten der drei Krankheitsbilder: Gemischtzelliges klassisches Hodgkinlymphom, noduläres klassisches Hodgkinlymphom und Lymphadenitis. Die Bildverarbeitungspipeline wurden teils neu implementiert, teils von etablierten Bilderkennungssoftware und -bibliotheken wie CellProfiler und Java Advanced Imaging verwendet. CD30-positive Zellobjekte werden in den Gewebeschnitten automatisiert erkannt und neben der globalen Position im Whole Slide Image weitere Morphologiedeskriptoren berechnet, wie Fläche, Feret-Durchmesser, Exzentrität und Solidität. Die Zellerkennung zeigt mit 84 % eine hohe Präzision und mit 95 % eine sehr gute Sensitivität.
Es konnte gezeigt werden, dass in Lymphadenitisfällen im Schnitt deutlich weniger CD30- positive Zellen präsent sind als in klassisches Hodgkinlymphom. Während hier im Schnitt nur rund 3.000 Zellen gefunden wurden, lag der Durchschnitt für das Mischtyp klassisches Hodgkinlymphom bei rund 19.000 CD30 positiven Zellen. Während die CD30-positiven Zellen in Lymphadenitisfällen relativ gleichmäßig verteilt sind, bilden diese in klassischen Hodgkinlymphom-Fällen Zellcluster höherer Dichte.
Die berechneten Morphologiedeskriptoren bieten die Möglichkeit die Gewebeschnitte und den Krankheitsverlauf näher zu beschreiben. Zudem sind bisher Größe und Erscheinungsbild der HRS-Zellen hauptsächlich anhand manuell ausgewählter Zellen bestimmt worden. Ein Maß für die Ausdehnung der Zellen ist der maximale Feret-Durchmesser. Bei CD30-Zellen im klassischen Hodgkinlymphom liegt dieser im Durchschnitt bei 20 μm und ist somit deutlich größer als die durchschnittlich gemessenen 15 μm in Lymphadenitis.
Es wurde ein graphentheoretischer Ansatz gewählt, um die CD30 positiven Zellen und ihre räumliche Nachbarschaft zu modellieren. In CD30-Zellgraphen von klassischen Hodgkinlymphom-Gewebeschnitten ist der durchschnittliche Knotengrad gegenüber den von Lymphadenitis-Bildern stark erhöht. Der Vergleich mit Zufallsgraphen zeigt, dass die beobachteten Knotengradverteilungen nicht für eine zufällige Verteilung der Zellen im Gewebeschnitt sprechen. Eigenschaften und Verteilung von Communities in CD30-Zellgraphen können hinzugenommen werden, um klassisches Hodgkinlymphom Gewebeschnitte näher zu charakterisieren.
Diese Arbeit zeigt, dass die Auswertung von Whole Slide Image unterstützend zur Verbesserung der Diagnose möglich ist. Die mehr als 400.000 automatisch erkannten CD30-positiven Zellobjekte wurden morphologisch beschrieben, und zusammen mit ihrer Position im Gewebeschnitt ist die Betrachtung wichtiger Eigenschaften des klassischen Hodgkinlymphoms realisierbar. Zellgraphen können durch weitere Zelltypen erweitert werden und auf andere Krankheitsbilder angewendet werden.
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability.
Blockchains in public administration : a RADIUS on blockchain framework for public administration
(2023)
The emergence of blockchain technology has generated a great deal of attention, as reflected in numerous scientific and journalistic articles. However, the implementation of blockchain for public administrations in Germany has encountered a setback owing to unsuccessful initiatives. Initial enthusiasm was followed by disillusionment. Nevertheless, technology continues to evolve. This paper examines whether the use of a blockchain can still optimize the processes of public administrations. Not only the failed projects are analysed, but also more current applications of the technology and their potential relevance for the administration, especially in the state of Hesse.
To answer if blockchains are promising to administrations, a Design Science Research (DSR) research approach is chosen. The DSR method is a research-based approach that aims to create new and innovative solutions to real-world problems through the development and evaluation of artefacts such as models, methods, or prototypes. For this work, the implementation of a framework to realize an Authentication, Authorization, and Accounting (AAA) system on the blockchain was identified as profitable. The framework aims to implement the aforementioned AAA tasks using a blockchain. The Remote Authentication Dial-In User Service (RADIUS) protocol has been identified as a potential protocol of the AAA system. The goal is to create a way to implement the system either entirely on a blockchain or as a hybrid system. Various blockchain technologies will be considered. Suitable for development, the framework AAA-me is named.
The development of AAA-me has shown that the desired framework for implementing RADIUS on the blockchain is possible in various degrees of implementation. Previous work mostly relied on full development. Additionally, it has been shown that AAA-me can be used to perform hybrid integration at different implementation levels. This makes AAA-me stand out from the few hybrid previous approaches. Furthermore, AAA-me was investigated in different laboratory environments. This was to determine the expected resilience against Single Point of Failure (SPOF). The results of the lab investigation indicated that a RADIUS system on top of a blockchain can provide benefits in terms of security and performance. In the lab environment, times were measured within which a series of authorization requests were processed. In addition, it was illustrated how a RADIUS system implemented using blockchain can protect itself against Man-in-the-Middle (MITM) attacks.
Finally, in collaboration with the Hessian Central Office for Data Processing (German: Hessische Zentrale für Datenverarbeitung) (HZD), another test lab demonstrated how a RADIUS system on the blockchain can integrate with the existing IT systems of the German state of Hesse. Based on these findings, this work reevaluated the applicability of blockchain technology for public administration processes.
The work has thus shown that the use of a blockchain can still be purposeful. However, it has also been shown that an implementation can bring many problems with it. The small number of blockchain developers and engineers also poses the risk of finding people to develop and maintain a system. In addition, one faces the problem of determining an architecture now that will be applied to many projects in the future. However, each project can, in turn, have an impact on the choice of architecture. Once one has solved this problem and a blockchain infrastructure is available, it can be established quickly and be more SPOF resistant, for example, for Public Key Infrastructure (PKI) systems.
AAA-me was only applied in lab and test environments. As a result, no real data ran over its own infrastructure. This allowed the necessary flexibility for development. However, system-related properties could appear in real situations that are not detectable here in this way. Furthermore, the initial stage of AAA-me’s development is still in its infancy. Many manual adjustments need to be made in order for this to integrate with an existing RADIUS system. Also, no system security effort in and of itself has been carried out in the lab environments. Thus, vulnerabilities can quickly open up on web servers due to misconfigurations and missing updates. For the above reasons, productive use should be discouraged unless major developments are carried out.
In the first part of the thesis, we show that the payment flow of a linear tax on trading gains from a security with a semimartingale price process can be constructed for all càglàd and adapted trading strategies. It is characterized as the unique continuous extension of the tax payments for elementary strategies w.r.t. the convergence "uniformly in probability". In this framework, we prove that under quite mild assumptions dividend payoffs have almost surely a negative effect on investor’s after-tax wealth if the riskless interest rate is always positive. In addition, we give an example for tax-efficient strategies for which the tax payment flow can be computed explicitly.
In the second part of the thesis, we investigate the impact of capital gains taxes on optimal investment decisions in a quite simple model. Namely, we consider a risk neutral investor who owns one risky stock from which she assumes that it has a lower expected return than the riskless bank account and determine the optimal stopping time at which she sells the stock to invest the proceeds in the bank account up to the maturity date. In the case of linear taxes and a positive riskless interest rate, the problem is nontrivial because at the selling time the investor has to realize book profits which triggers tax payments. We derive a boundary that is continuous and increasing in time, and decreasing in the volatility of the stock such that the investor sells the stock at the first time its price is smaller or equal to this boundary.
The relevant field of interest in High Energy Physics experiments is shifting to searching and studying extremely rare particles and phenomena. The search for rare probes requires an increase in the number of available statistics by increasing the particle interaction rate. The structure of the events also becomes more complicated, the multiplicity of particles in each event increases, and a pileup appears. Due to technical limitations, such data flow becomes impossible to store fully on available storage devices. The solution to the problem is the correct triggering of events and real-time data processing.
In this work, the issue of accelerating and improving the algorithms for reconstruction of the charged particles' trajectories based on the Cellular Automaton in the STAR experiment is considered to implement them for track reconstruction in real-time within the High-Level Trigger. This is an important step in the preparation of the CBM experiment as part of the FAIR Phase-0 program. The study of online data processing methods in real conditions at similar interaction energies allows us to study this process and determine the possible weaknesses of the approach.
Two versions of the Cellular Automaton based track reconstruction are discussed, which are used, depending on the detecting systems' features. HFT~CA Track Finder, similar to the tracking algorithm of the CBM experiment, has been accelerated by several hundred times, using both algorithm optimization and data-level parallelism. TPC~CA Track Finder has been upgraded to improve the reconstruction quality while maintaining high calculation speed. The algorithm was tuned to work with the new iTPC geometry and provided an additional module for very low momentum track reconstruction.
The improved track reconstruction algorithm for the TPC detector in the STAR experiment was included in the HLT reconstruction chain and successfully tested in the express production for the online real data analysis. This made it possible to obtain important physical results during the experiment runtime without the full offline data processing. The tracker is also being prepared for integration into a standard offline data processing chain, after which it will become the basic track search algorithm in the STAR experiment.
We give theorems about asymptotic normality of general additive functionals on patricia tries, derived from results on tries. These theorems are applied to show asymptotic normality of the distribution of random fringe trees in patricia tries. Formulas for asymptotic mean and variance are given. The proportion of fringe trees with 𝑘 keys is asymptotically, ignoring oscillations, given by (1−𝜌(𝑘))/(𝐻 +𝐽)𝑘(𝑘−1) with the source entropy 𝐻, an entropy-like constant 𝐽, that is 𝐻 in the binary case, and an exponentially decreasing function 𝜌(𝑘). Another application gives asymptotic normality of the independence number and the number of 𝑘-protected nodes.
Urn models are simple examples for random growth processes that involve various competing types. In the study of these schemes, one is generally interested in the impact of the specific form of interaction on the allocation of elements to the types. Depending on their reciprocal action, effects of cancellation and self-reinforcement become apparent in the long run of the system. For some urn models, the influencing is of a smoothing nature and the asymptotic allocation to the types is close to being a result of independent and identically distributed growth events. On the contrary, for others, almost sure random tendencies or logarithmically periodic terms emerge in the second growth order. The present thesis is devoted to the derivation of central limit theorems in the latter case. For urns of this kind, we use a "non-classical" normalisation to derive asymptotic joint normality of the types. This normalisation takes random tendencies and phases into account and consequently involves random centering and, also, possibly random scaling.
The main contribution of the thesis is in helping to understand which software system parameters mostly affect the performance of Big Data Platforms under realistic workloads. In detail, the main research contributions of the thesis are:
1. Definition of the new concept of heterogeneity for Big Data Architectures (Chapter 2);
2. Investigation of the performance of Big Data systems (e.g. Hadoop) in virtualized environments (Section 3.1);
3. Investigation of the performance of NoSQL databases versus Hadoop distributions (Section 3.2);
4. Execution and evaluation of the TPCx-HS benchmark (Section 3.3);
5. Evaluation and comparison of Hive and Spark SQL engines using benchmark queries (Section 3.4);
6. Evaluation of the impact of compression techniques on SQL-on-Hadoop engine performance (Section 3.5);
7. Extensions of the standardized Big Data benchmark BigBench (TPCx-BB)(Section 4.1 and 4.3);
8. Definition of a new benchmark, called ABench (Big Data Architecture Stack Benchmark), that takes into account the heterogeneity of Big Data architectures (Section 4.5).
The thesis is an attempt to re-define system benchmarking taking into account the new requirements posed by the Big Data applications. With the explosion of Artificial Intelligence (AI) and new hardware computing power, this is a first step towards a more holistic approach to benchmarking.
Modern mobile devices offer a great variety of data that can be recorded. This broad range of information offers the possibility to tailor applications more to the needs of a user. Several context information can be collected, like e.g. information about position or movement. Besides integrated sensors, a broad range of additional sensors are available which can be connected to a mobile device. These additional sensors offer for example the possibility to measure physiological signals of a user.The human body offers a broad range of different signals. These signals have been used in several examples to conclude on the state of a user. The different signals allow to get a deeper insight into emotional or mental state of a user. Electrodermal activity gives feedback about the current arousal level of a user. Heart rate and heart rate variability can give an estimation about valence and mental load of a user. Several models exist to conclude from information like valence and arousal on different emotional states. Russell defined a two dimensional model, using valence and arousal to define affective states. Yerkes and Dodson developed a curve that expresses the relationship between arousal and performance of a user. Different examples exist, that use physiological signals to determine the user state for tailoring and adapting of applications. At the time of this work most of these examples did not address the usage of physiological signals for user state estimation in mobile applications and in mobile scenarios. Mobile scenarios lead to several challenges that need to be addressed. Influencing factors on physiological signals, like e.g. movement have to be controlled. Furthermore a user might be interrupted and influenced by environmental aspects. The combination of physiological data and context information might improve the interpretation of user state in mobile scenarios. In this work, we present a model that addresses the challenges of usage in mobile scenarios to offer an estimation of user state to mobile applications. To address a broad range of mobile applications, affective and cognitive state are provided as output. As input heart rate and electrodermal activity are used, as well as context information about movement and performance. Electrodermal activity is measured by a simple sensor that can be worn as a wristband. Heart rate is measured by a chest strap as used in sports. The input channels are transformed to affective and cognitive state based on a fuzzy rule based approach. With help of fuzzy logic, uncertainty can be expressed and the data continuously being processed. At the start, input channels are fuzzified by defined functions. After a that, a first fuzzy rule set transforms the input signals into values for valence, arousal and mental load. In a second step, these values and context information are transformed with another fuzzy rule set to values for affective and cognitive state. Affective state is based on the model of Russell, where valence and arousal are used to determine different emotional states. The output of the model are eight different affective states (alarmed, excited, happy, relaxed, tired, bored, sad and frustrated), which can have a high, medium, low or very low value as output. Cognitive state is determined based on mental load and context information about performance and movement. The output value can be very high, high, medium or low. The model was implemented as background service for Android devices. Different applications have been used for evaluation of the model. The model has been integrated in a multiplayer space shooter game, called ”Zone of Impulse”, which mainly benefits from the affective state. Cognitive state is more addressed in applications like a simple vocable trainer, which adapts difficulty based on user state. A study to evaluate different aspects of the model has been conducted. The study was designed to investigate the suitability of the model for mobile scenarios. The game ”zone of impulse” and the vocable trainer have been investigated in different configurations. Versions with integrated model have been compared to version of the applications without model, as well as versions of the model without context information. In total 41 participants took part in the study. A part of the participants had to do the tasks of the study in a mobile scenario, walking around several streets. The remaining participants had to do the tasks in a controlled environment in a sitting position. Different aspects were collected with ratings and questionnaires. Overall, participants rated that they did not feel impaired by the sensors they had to wear. The results showed, that the combination of physiological data and context information had an advantage against versions without context information in part of the ratings. A comparison between versions with and without model showed, that the subjective mental load ratings were significantly better for the version with model. Subjective ratings for aspects like fun, overstrain and support were mixed. When comparing the application versions in indoor and outdoor scenarios, no significant difference could be found, which leads to the assumption that there is no loss of interpretation quality in outdoor scenarios. The results also showed that the model seems to be robust enough to compensate the loss of an input channel, as there was no significant difference between application versions with full integrated model and versions with one channel lost. With the model developed in this work, context information and physiological data were combined to improve user state estimation. Furthermore pitfalls of user state estimation in mobile scenarios are overcome with this combination. However, the model has only been evaluated with a limited amount of applications and situations that mobile scenarios offer.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Therefore, self-X features like self-organization, self-adaptation and self-healing are key principles for such systems.
Additionally, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. In an automotive environment for example, the brake has a higher criticality level regarding safety as the infotainment. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system. This leads to the need to accommodate multiple levels of criticality while ensuring safety and reliability, which increases the already high complexity even more.
This thesis deals with the question on how to conveniently, effectively and efficiently handle the management and complexity of mixed-critical CPNs (MC-CPNs). Since this cannot be done by the system developer without the assistance of the system itself any longer, it is essential to develop new approaches and techniques to ensure that such systems can operate under a range of conditions while meeting stringent requirements.
Based on five research hypothesis, this thesis introduces a comprehensive adaptive mixed-criticality supporting middleware for Cyber-Physical Networks (Chameleon), which efficiently and autonomously takes care of the management and complexity of CPNs with regard to the mixed-criticality aspect.
Chameleon contributes to the state-of-art by introducing and combining the following concepts:
- A comprehensive self-adaption mechanism on all levels of the system model is provided.
- This mechanism allows a flexible combination of parametric and structural adaptation actions (relocation, scheduling, tuning, ...) to modify the behavior of the system.
- Real-time constraints of mixed-critical applications (hard real-time, soft real-time, best-effort) are considered in all possible adaptation conditions and actions by the use of the importance parameter.
- CPNs are supported by the introduction of different scopes (local, system, global) for the adaptation conditions and actions. This also enables the combination of different scopes for conditions and actions.
- The realization of the adaptation with a MAPE-K loop instantiated by a distributed LCS allows for real-time capable reasoning of adaptation actions which also works on resource-spare systems.
- The developed rule language Rango offers an intuitive way to specify an initial rule set for LCS in the context of CPS/CPNs and supports the system administrators in the process of rule set generation.
In the recent past, we are making huge progress in the field of Artificial Intelligence. Since the rise of neural networks, astonishing new frontiers are continuously being discovered. The development is so fast that overall no major technical limits are in sight. Hence, digitization has expanded from the base of academia and industry to such an extent that it is prevalent in the politics, mass media and even popular arts. The DFG-funded project Specialized Information Service for Biodiversity Research and the BMBF-funded project Linked Open Tafsir can be placed exactly in that overall development. Both projects aim to build an intelligent, up-to-date, modern research infrastructure on biodiversity and theological studies for scholars researching in these respective fields of historical science. Starting from digitized German and Arabic historical literature containing so far unavailable valuable knowledge on biodiversity and theological studies, at its core, our dissertation targets to incorporate state-of-the-art Machine Learning methods for analyzing natural language texts of low-resource languages and enabling foundational Natural Language Processing tasks on them, such as Sentence Boundary Detection, Named Entity Recognition, and Topic Modeling. This ultimately leads to paving the way for new scientific discoveries in the historical disciplines of natural science and humanities. By enriching the landscape of historical low-resource languages with valuable annotation data, our work becomes part of the greater movement of digitizing the society, thus allowing people to focus on things which really matter in science and industry.
Debate topic expansion
(2022)
Given a debate topic, it is often to make an expansion of the topic, the reasons can be the followings: (1) The scope of the debate topic is too shallow and we eager to discuss more. (2) A debate topic is sometimes related to the others and the discussion will not be complete when we do not discuss the others as well. (3) We may want to discuss the particular concept or the core the debate topic. It's thus meaningful to build a model in order to find the expansions of the topics.
IBM Research Team has proposed a method to expand the boundary and find the expansion topics of the given debate topics in 2019. There are two types of topic expansions in their paper, consistent and contrastive expansions. We focus on the consistent expansions. Consistent expansions are defined as the expansions that expand our topics in a positive way or at least neutral.
The main objective of this paper is to follow and examine the steps of IBM Research Team's idea and since the original discusses the model in english, we would like to implement a topic expansion model with 7 steps, including pattern extraction, filtering, training, etc, in another language (german) using translator and compare the result between different models to propose the final german model at the end.
Deep learning and isolation based security for intrusion detection and prevention in grid computing
(2018)
The use of distributed computational resources for the solution of scientific problems, which require highly intensive data processing is a fundamental mechanism for modern scientific collaborations. The Worldwide Large Hadron Collider Computing Grid (WLCG) is one of the most important examples of a distributed infrastructure for scientific projects and is one of the pioneering examples of grid computing. The WLCG is the global grid that analyzes data from the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), with 170 sites in 40 countries and more than 600,000 processing cores. The grid service providers grant users access to resources that they can utilize on demand for the execution of custom software applications used for the analysis of data. The code that the users can execute is completely flexible, and commonly there are no significant restrictions. This flexibility and the availability of immense computing power increases the security challenges of these environments. Attackers are a concern for grid administrators. These attackers may request the execution of software with a malicious code that gives them the possibility of compromising the underlying institutions’ infrastructure. Grid systems need security countermeasures to keep the user code running, without allowing access to critical components but whilst still retaining flexibility. The administrators of grid systems also need to be continuously monitoring the activities that the applications are carrying out. An analysis of these activities is necessary to detect possible security issues, to identify ongoing incidents and to perform autonomous responses. The size and complexity of grid systems make manual security monitoring and response expensive and complicated for human analysts. Legacy intrusion detection and prevention systems (IDPS) such as Snort and OSSEC are traditionally used for security incident monitoring in the grid, cloud, clusters and standalone systems. However, IDPS are limited due to the use of hardcoded fixed rules that need to be updated continuously to cope with different threats.
This thesis introduces an architecture for improving security in grid computing. The architecture integrates the use of security by isolation, behavior monitoring and deep learning (DL) for the classification of real-time traces of the running user payloads also known as grid jobs. The first component of the proposal, the Linux containers (LCs), are used to provide isolation between grid jobs and to gather specific traceable information about the behavior of individual jobs. LCs offer a safe environment for the execution of arbitrary user scripts or binaries, protecting the sensitive components of the grid member organizations. The containers consist of a software sandboxing technique and form a lightweight alternative to other technologies such as virtual machines (VMs) that usually implement a full machine-level emulation and can, therefore, significantly affect the performance. This performance loss is commonly unacceptable in high-throughput computing scenarios. Containers enable the collection of monitoring information from the processes running inside them. The data collected via the LCs monitoring is employed to feed a DL-based IDPS.
DL methods can acquire knowledge from experience, which eliminates the need for operators to formally specify all the knowledge that a system requires. These methods can improve IDPS by building models that are utilized to detect security incidents automatically, having the ability to generalize to new classes of issues. DL can produce lower false positive rates for intrusion detection, but also provides a measure of false negatives, which can be improved with new training data. Convolutional neural networks (CNNs) are utilized for the distinction between regular and malicious job classes. A set of samples is collected from regular production grid jobs from the grid infrastructure of “A Large Ion Collider Experiment” (ALICE) and malicious Linux binaries from a malware research website. The features extracted from these samples are utilized for the training and validation of the machine learning (ML) models. The utilization of a generative approach to enhance the required training data is also proposed. Recurrent neural networks (RNN) are used as generative models for the simulation of training data that complements and improves the real collected dataset. This data augmentation strategy is useful to supplement the lack of training data in ML processes.
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Density visualization pipeline: a tool for cellular and network density visualization and analysis
(2020)
Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.
A Large Ion Collider Experiment (ALICE) is one of the four large experiments at the Large Hadron Collider (LHC) at the European Organization for Particle Physics (CERN). ALICE focuses on the physics of the strong interaction and in particular on the Quark-Gluon Plasma. This is a state of matter in which quarks are de-confined. It is believed that it existed in the earliest moments of the evolution of the universe. The ALICE detector studies the products of the collisions between heavy-nuclei, between protons, and between protons and heavy-nuclei. The sub-detector closest to the interaction point is the Inner Tracking System (ITS), which is used to measure the momentum and trajectory of the particles generated by the collisions and allows reconstructing primary and secondary interaction vertices. The ITS needs to have an accurate spatial resolution, together with a low material budget to limit the effect of multiple scattering on low-energetic particles to precisely reconstruct their trajectory. During the Long Shutdown 2 (2019-2020) of the LHC, the current ITS will be replaced by a completely redesigned sub-detector, which will improve readout rate and particle tracking performance especially at low-momentum.
The ALice PIxel DEtector (ALPIDE) chip was designed to meet the requirements of the upgraded ITS in terms of resolution, material budget, radiation hardness, and readout rate. The ALPIDE chip is a Monolithic Active Pixel Sensor (MAPS) realised in Complementary Metal-Oxide Semiconductor (CMOS) technology. Sensing element, analogue front-end, and its digital readout are integrated into the same silicon die. The readout architecture of the new ITS foresees that data is transmitted via a high-speed serial link directly from the ALPIDE to the off-detector electronics. The data is transmitted off-chip by a so-called Data Transmission Unit (DTU) which needs to be tolerant to Single-Event Effects induced by radiation, in order to guarantee reliable operation. The ALPIDE chip will operate in a radiation field with a High-Energy Hadron peak flux of 7.7·10^5 cm^-2s^-1.
The data are sent by the ALPIDE on copper cables to the readout system, which aggregates them and re-transmits them via optical fibres to the counting room. The position where the readout electronics will be placed is constrained by the maximum transmission distance reasonably achievable by the ALPIDE Data Transmission Unit and mechanical constraints of the ALICE experiment. The radiation field at that location is not negligible for its effects on electronics: the high-energy hadrons flux can reach 10^3 cm^-2s^-1. Static RAM (SRAM)-based Field Programmable Gate Arrays (FPGAs) are favoured over Application Specific Integrated Circuits (ASICs) or Radiation Hard by Design (RHBD) commercial devices because of cost effectiveness. Moreover, SRAM-based FPGAs are re-configurable and provide the data throughput required by the ITS. The main issue with SRAM-based FPGAs, for the intended application, is the susceptibility of their Configuration RAM (CRAM) to Single-Event Upsets: the number of CRAM bits is indeed much higher than the logic they configure. Total Ionizing Dose (TID) at the readout designed position is indeed still acceptable for Component Off The Shelf (COTS), provided that proper verification is carried out.
This dissertation focuses on two parts of the design of the readout system: the Data Transmission Unit of the ALPIDE chip and the design of fundamental modules for the SRAM-based FPGA of the readout electronics. In the first part, a module of the Data Transmission Unit is designed, optimising the trade-off between power consumption, radiation tolerance, and jitter performance. The design was tested and thoroughly characterised, including tests while under irradiation with a 30 MeV protons. Furthermore the Data Transmission Unit performance was validated after the integration into the first prototypes of ITS modules. In the second part, the problem of developing a radiation-tolerant SRAM-based FPGA design is investigated and a solution is provided. First, a general methodology for designing radiation-tolerant Finite State Machines in SRAM-based FPGAs is analysed, implemented, and verified. Later, the radiation-tolerant FPGA design for the ITS readout is described together with the radiation effects mitigation techniques that were selectively applied to the different modules. The design was tested with multiple irradiation tests and the results are stated below.
Deep learning with neural networks seems to have largely replaced traditional design of computer vision systems. Automated methods to learn a plethora of parameters are now used in favor of previously practiced selection of explicit mathematical operators for a specific task. The entailed promise is that practitioners no longer need to take care of every individual step, but rather focus on gathering big amounts of data for neural network training. As a consequence, both a shift in mindset towards a focus on big datasets, as well as a wave of conceivable applications based exclusively on deep learning can be observed.
This PhD dissertation aims to uncover some of the only implicitly mentioned or overlooked deep learning aspects, highlight unmentioned assumptions, and finally introduce methods to address respective immediate weaknesses. In the author’s humble opinion, these prevalent shortcomings can be tied to the fact that the involved steps in the machine learning workflow are frequently decoupled. Success is predominantly measured based on accuracy measures designed for evaluation with static benchmark test sets. Individual machine learning workflow components are assessed in isolation with respect to available data, choice of neural network architecture, and a particular learning algorithm, rather than viewing the machine learning system as a whole in context of a particular application. Correspondingly, in this dissertation, three key challenges have been identified: 1. Choice and flexibility of a neural network architecture. 2. Identification and rejection of unseen unknown data to avoid false predictions. 3. Continual learning without forgetting of already learned information. These latter challenges have already been crucial topics in older literature, alas, seem to require a renaissance in modern deep learning literature. Initially, it may appear that they pose independent research questions, however, the thesis posits that the aspects are intertwined and require a joint perspective in machine learning based systems. In summary, the essential question is thus how to pick a suitable neural network architecture for a specific task, how to recognize which data inputs belong to this context, which ones originate from potential other tasks, and ultimately how to continuously include such identified novel data in neural network training over time without overwriting existing knowledge.
Thus, the central emphasis of this dissertation is to build on top of existing deep learning strengths, yet also acknowledge mentioned weaknesses, in an effort to establish a deeper understanding of interdependencies and synergies towards the development of unified solution mechanisms. For this purpose, the main portion of the thesis is in cumulative form. The respective publications can be grouped according to the three challenges outlined above. Correspondingly, chapter 1 is focused on choice and extendability of neural network architectures, analyzed in context of popular image classification tasks. An algorithm to automatically determine neural network layer width is introduced and is first contrasted with static architectures found in the literature. The importance of neural architecture design is then further showcased on a real-world application of defect detection in concrete bridges. Chapter 2 is comprised of the complementary ensuing questions of how to identify unknown concepts and subsequently incorporate them into continual learning. A joint central mechanism to distinguish unseen concepts from what is known in classification tasks, while enabling consecutive training without forgetting or revisiting older classes, is proposed. Once more, the role of the chosen neural network architecture is quantitatively reassessed. Finally, chapter 3 culminates in an overarching view, where developed parts are connected. Here, an extensive survey further serves the purpose to embed the gained insights in the broader literature landscape and emphasizes the importance of a common frame of thought. The ultimately presented approach thus reflects the overall thesis’ contribution to advance neural network based machine learning towards a unified solution that ties together choice of neural architecture with the ability to learn continually and the capability to automatically separate known from unknown data.
Ausgangspunkt der Forschungsarbeit ist der Gebrauch von Gesten in mathematischen Interaktionen von Lernenden. Es wird untersucht, inwiefern Gesten Teil des mathematischen Aushandlungsprozesses sind. Damit ist die Rekonstruktion einer potentiell fachlichen Bedeutung des Gestengebrauchs beim Mathematiklernen das zentrale Forschungsanliegen.
Theoretisch gerahmt wird die Arbeit von Erkenntnissen aus der psychologisch-linguistischen Gestenforschung zur systematischen Beschreibung von Gestik im Zusammenspiel mit der gleichzeitig geäußerten Lautsprache (McNeill, 1992; Kendon, 2004). Es werden ebenso ausgewählte Forschungen zur Gestik beim Mathematiklernen beleuchtet (Arzarello, 2006; Wille, 2020; Kiesow, 2016). Die mathematikdidaktische Interaktionstheorie begründet den sozial-konstruktivistischen Lernbegriff (Krummheuer, 1992). Ausgewählte Aspekte der Semiotik nach C. S. Peirce bieten eine theoretische Fundierung des Zeichenbegriffs und des Kerns mathematischen Agierens, verstanden als diagrammatisches Arbeiten (Peirce, 1931, CP 1.54 u. 1932, CP 2.228).
Von besonderer Bedeutung für die vorliegende Forschungsarbeit ist der linguistische Ansatz der Code-Integration und -Manifestation von redebegleitenden Gesten im Sprachsystem nach Fricke (2007, 2012) in Verbindung mit dem Peirce’schen Diagrammbegriff. Diese Perspektive ermöglicht eine theoretische Fundierung der zunächst empirisch beobachtbaren Multimodalität der Ausdrucksweisen von Lernenden beim gemeinsamen Mathematiktreiben. Der Peirce’sche Diagrammbegriff dient hierbei zur Rekonstruktion einer systemischen Relevanz von Gesten für das Betreiben von Mathematik: Bestimmte Gesten sind semiotisch als mathematische Zeichen beschreibbar und haben potentiell konstituierende Funktion für das diagrammatische Arbeiten der Lernenden. Der übergeordnete Forschungsfokus lautet: Wie nutzen Grundschüler*innen Gestik und Lautsprache, insbesondere in deren Zusammenspiel, um ihre mathematischen Ideen in den interaktiven Aushandlungsprozess einzubringen und über den Verlauf der Interaktion aufzugreifen, möglicherweise weiterzuentwickeln oder auch zu verwerfen? In der Ausdifferenzierung wird die Funktion der verwendeten Gesten und die Rekonstruktion von potentiell gemeinsam gebrauchten Gesten der Interagierenden in den Blick genommen.
Methodisch lässt sich die Forschungsarbeit der qualitativen Sozialforschung (Bohnsack, 2008) bzw. der interpretativen mathematikdidaktischen Unterrichtsforschung zuordnen (Krummheuer & Naujok, 1999). Es werden Beispiele aus mathematischen Interaktionssituationen ausgewertet, in denen sich Paare von Zweitklässler*innen mit einem mathematischen Problem aus der Kombinatorik und der Geometrie beschäftigen. Eine eigens theoriekonform entwickelte Transkriptpartitur dient zur Aufarbeitung der Videodaten. Mit der textbasierten Interaktionsanalyse (Krummheuer, 1992) und der grafisch angelegten Semiotischen Analyse (Schreiber, 2010) in einer Weiterentwicklung der Semiotischen Prozess-Karten (Huth, 2014) werden zwei hierarchisch aufeinander aufbauende Analyseverfahren verwendet.
Zentrale Forschungsergebnisse sind 1) die funktionale und gestalterische Flexibilität des Gestengebrauchs beim diagrammatischen Arbeiten der Lernenden, 2) die Rekonstruktion von Modusschnittstellen der Gesten mit anderen Ausdrucksmodi in Funktion, interaktionaler Bedeutungszuschreibung und Chronologie, und 3) die häufige Verwendung der Gesten als Modus der Wahl der Lernenden in mathematischen Interaktionen. Gesten weisen eine unmittelbare und voraussetzungslose Verfügbarkeit auf, eine funktionale und gestalterische Flexibilität in der mathematischen Auseinandersetzung und die Möglichkeit, Funktionen anderer Modi (vorübergehen) zu übernehmen. Es zeigt sich eine konstitutive und fachliche Bedeutung der Gestik für das mathematisch-diagrammatische Agieren der Lernenden. In der Arbeit wird daraus schließlich das doppelte Kontinuum der Gesten für das Mathematiklernen entwickelt. Es zeigt in der Dimension der Funktion des Gestengebrauchs und der Dimension des Objektbezugs der Gestengestalt die Vielfältigkeit der Gestenfunktionen im gemeinsamen diagrammatischen Arbeiten der Lernenden und gibt Einblick in die verwendeten Gestengestalten.
Die Forschungsarbeit offenbart den Bedarf einer Beachtung von Gesten in der fachdidaktischen Planung und Gestaltung von Mathematikunterricht und in der Erforschung und Diagnostik der mathematischen Entwicklung von Lernenden. Es handelt sich bei Gesten in mathematischen Interaktionen nicht um ein reines Beiwerk der Äußerung, sondern um einen fachlich bedeutsamen Modus in Bezug auf das Mathematiklernen. Der Gebrauch von Gestik ermöglicht die Erzeugung von Diagrammen im Handumdrehen und eröffnet perspektivisch eine Erforschung ihrer Bedeutung für mathematische Lehr-Lern-Prozesse.
Die in dieser Zusammenfassung angegebene Literatur findet sich im Literaturverzeichnis der vorgelegten Forschungsarbeit.
Digital distractions can interfere with goal attainment and lead to undesirable habits that are hard to get red rid of. Various digital self-control interventions promise support to alleviate the negative impact of digital distractions. These interventions use different approaches, such as the blocking of apps and websites, goal setting, or visualizations of device usage statistics. While many apps and browser extensions make use of these features, little is known about their effectiveness. This systematic review synthesizes the current research to provide insights into the effectiveness of the different kinds of interventions. From a search of the ‘ACM’, ‘Springer Link’, ‘Web of Science’, ’IEEE Xplore’ and ‘Pubmed’ databases, we identified 28 digital self-control interventions. We categorized these interventions according to their features and their outcomes. The interventions showed varying degrees of effectiveness, and especially interventions that relied purely on increasing the participants' awareness were barely effective. For those interventions that sanctioned the use of distractions, the current literature indicates that the sanctions have to be sufficiently difficult to overcome, as they will otherwise be quickly dismissed. The overall confidence in the results is low, with small sample sizes, short study duration, and unclear study contexts. From these insights, we highlight research gaps and close with suggestions for future research.
In particle collider experiments, elementary particle interactions with large momentum transfer produce quarks and gluons (known as partons) whose evolution is governed by the strong force, as described by the theory of quantum chromodynamics (QCD)1. These partons subsequently emit further partons in a process that can be described as a parton shower2, which culminates in the formation of detectable hadrons. Studying the pattern of the parton shower is one of the key experimental tools for testing QCD. This pattern is expected to depend on the mass of the initiating parton, through a phenomenon known as the dead-cone effect, which predicts a suppression of the gluon spectrum emitted by a heavy quark of mass mQ and energy E, within a cone of angular size mQ/E around the emitter3. Previously, a direct observation of the dead-cone effect in QCD had not been possible, owing to the challenge of reconstructing the cascading quarks and gluons from the experimentally accessible hadrons. We report the direct observation of the QCD dead cone by using new iterative declustering techniques4,5 to reconstruct the parton shower of charm quarks. This result confirms a fundamental feature of QCD. Furthermore, the measurement of a dead-cone angle constitutes a direct experimental observation of the non-zero mass of the charm quark, which is a fundamental constant in the standard model of particle physics.
The main goal of this work was to create a network environment for the Unity Engine project StolperwegeVR, developed by the Text Technology Lab of Goethe University, in which you will be able to annotate one to several documents in a group. For this, basic network utils like seeing other users or moving objects had to be implemented which had to be easy to use and work with in the future.
We prove new existence results for a nonlinear Helmholtz equation with sign-changing nonlinearity of the form − delta u−k2u=Q(x)/u/p−2u, uEW2, p(RN) – delta u − k2u=Q(x)/u/p−2u, uEW2, p(RN) with k>0, k>0, N≥3N≥3, pE[2(N+1)N − 1, 2NN − 2)pE[2(N+1)N − 1, 2NN−2) and QEL ∞ (RN)QEL ∞ (RN). Due to the sign-changes of Q, our solutions have infinite Morse-Index in the corresponding dual variational formulation.
This thesis explores a variety of methods of text quantification applicable in the field of educational text technology. Besides the cohort of existing linguistic, lexical, syntactic, and semantic text quantification methods, additional methods based on Bidirectional Encoder Representations from Transformers (BERT) are introduced and analysed. The model, developed in this thesis, is tested on a multilingual data composed of task descriptions used in Test of Understanding in College Economics (TUCE). Quantitative features extracted from raw textual data are analysed using an array of evaluation methods with the goal of finding the best predictors of the target variable - the rate of correct student responses in TUCE.
Zeitreihen von spontan auftretenden Topograpfien elektrischer Felder an der Kopfoberfläche, die durch eine Elektroenzephalografie (EEG) gemessen werden, zeigen Zeiträume („EEG-Microstates“), während denen die Topografie quasi-stabil ist. Diese EEG-Microstates werden üblicherweise dadurch analysiert, dass die zu spezifischen Zeitpunkten beobachteten Ausprägungen des EEGs in eine kleine Anzahl von prototypischen Topografien („Karten“) eingeteilt werden. Dadurch erhält man eine diskrete Kartensequenz.
Um die Struktur der Übergangswahrscheinlichkeiten in experimentellen Kartensequenzen zu beschreiben, werden diese Sequenzen durch eine reduzierte Markov-Kette modelliert mit nur einem Parameter pro Karte. Die Markov-Ketten können mithilfe von zwei bestimmten stochastischen Prozessen konstruiert werden. Durch den einen Prozess werden zufällige Intervalle definiert, die zufällig den verschiedenen Karten zugeordnet werden. Durch den anderen Prozess werden zufällige Abtastungszeitpunkte bestimmt, zu denen die Karte des jeweils aktuellen Intervalls abgelesen wird.
Neben der Motivation und Vorstellung des Markov-Ketten-Modells werden in dieser Arbeit Schätzer für die Modellparameter vorgeschlagen und diskutiert sowie ihre asymptotischen Varianzen hergeleitet. Zudem wird ein Anpassungstest durchgeführt und es werden Abwandlungen des Modells untersucht.
Eine 1-1-Korrespondenz zwischen einer Klasse von Leftist-Bäumen und erweiterten t-nären Bäumen
(2006)
Leftist-Bäume sind eine Teilmenge der geordneten Bäume mit der Eigenschaft, daß der [kürzeste] Weg von jedem inneren Knoten zu einem Blatt des Teilbaums mit diesem Knoten als Wurzel immer über den am weitesten links stehenden Sohn dieses Knotens verläuft.
In der vorliegenden Arbeit wird eine 1-1-Korrespondenz zwischen erweiterten t-nären Bäumen und der Klasse der Leftist-Bäumen mit erlaubten Knotengraden 0, t, 2t-1, ... 1+t(t-1) präsentiert. Diese 1-1-Korrespondenz verallgemeinert ein Ergebnis von R. Kemp.
Das Ziel dieser Arbeit ist die realitätsgetreue Entwicklung eines interaktiven 3D-Stadtmodells, welches auf den ÖPNV zugeschnitten ist. Dabei soll das Programm anhand von Benutzereingaben und mit Hilfe einer Datenquelle, automatisch eine dreidimensionale Visualisierung der Gebäude erzeugen und den lokalen ÖPNV mitintegrieren. Als Beispiel der Ausarbeitung diente das ÖPNV-Netz der Stadt Frankfurt. Hierbei wurde auf die Problematik der Erhebung von Geoinformationen und der Verarbeitung von solchen komplexen Daten eingegangen. Es wurde ermittelt, welche Nutzergruppen einen Mehrwert durch eine derartige 3D Visualisierung haben und welche neuen Erweiterungs- und Nutzungspotenziale das Modell bietet.
Dem Leser soll insbesondere ein Einblick in die Generierung von interaktiven 3D-Modellen aus reinen Rohdaten verschafft werden. Dazu wurde als Entwicklungsumgebung die Spiele-Engine Unity eingesetzt, welche sich als sehr fähiges und modernes Entwicklungswerkzeug bei der Erstellung von funktionalen 3D-Visualisierungen herausgestellt hat. Als Datenquelle wurde das OpenStreetMap Projekt benutzt und im Rahmen dieser Arbeit behandelt. Anschließend wurde zur Evaluation, das Modell verschiedenen Nutzern bereitgestellt und anhand eines Fragebogens evaluiert.
We live in age of data ubiquity. Even the most conservative estimates predict exponential growth in produced, transmitted and stored data. Big data is used to power business analytics as well as to foster scientific discoveries. In many cases, explosion of produced data exceeds capabilities of digital storage systems. Scientific high-performance computing environments cope with this problem by utilizing large, distributed, storage systems. These complex systems can only provide a high degree of reliability and durability by means of data redundancy. The most straight-forward way of doing that is by replicating the data over different physical devices. However, more elaborate approaches, such as erasure coding, can provide similar data protection while utilizing less storage. Recently, software-defined reliability methods began to replace traditional, hardware- based, solutions. Complicated failure modes of storage system components also warrant checksums to guaranty long-term data integrity. To cope with ever increasing data volumes, flexible and efficient software implementation of error correction codes is of great importance. This thesis introduces a method for realizing a flexible Reed-Solomon erasure code using the “Just-In-Time” compilation technique. By exploiting intrinsic arithmetic redundancy in the algorithm, and by relying on modern optimizing compilers, we obtain a throughput-efficient erasure code implementation. Additionally, exploitation of data parallelism is achieved effortlessly by instructing the compiler to produce SIMD code for desired execution platform. We show results of codes implemented using SSE and AVX2 SIMD instruction sets for x86, and NEON instruction set for ARM platforms. Next, we introduce a framework for efficient vectorized RAID-Z redundancy operations of ZFS file system. Traditional, table-based Galois field multiplication algorithms are replaced with custom SSE and AVX2 parallel methods, providing significantly faster and more efficient parity operations. The implementation of this framework was made publicly available as a part of ZFS on Linux project, since version 0.7. Finally, we propose a new erasure scheme for use with existing, high performance, parallel filesystems. Described reliability middleware (ECCFS) allows definition of flexible, file-based, reliability policies, adapting to customized user needs. By utilizing the block erasure code, the ECCFS achieves optimal storage, computation, and network resource utilization, while providing a high level of reliability. The distributed nature of the middleware allows greater scalability and more efficient utilization of storage and network resources, in order to improve availability of the system.
In our work, we establish the existence of standing waves to a nonlinear Schrödinger equation with inverse-square potential on the half-line. We apply a profile decomposition argument to overcome the difficulty arising from the non-compactness of the setting. We obtain convergent minimizing sequences by comparing the problem to the problem at “infinity” (i.e., the equation without inverse square potential). Finally, we establish orbital stability/instability of the standing wave solution for mass subcritical and supercritical nonlinearities respectively.
Existence of nonradial domains for overdetermined and isoperimetric problems in nonconvex cones
(2022)
In this work we address the question of the existence of nonradial domains inside a nonconvex cone for which a mixed boundary overdetermined problem admits a solution. Our approach is variational, and consists in proving the existence of nonradial minimizers, under a volume constraint, of the associated torsional energy functional. In particular we give a condition on the domain D on the sphere spanning the cone which ensures that the spherical sector is not a minimizer. Similar results are obtained for the relative isoperimetric problem in nonconvex cones.
Data-parallel programming is more important than ever since serial performance is stagnating. All mainstream computing architectures have been and are still enhancing their support for general purpose computing with explicitly data-parallel execution. For CPUs, data-parallel execution is implemented via SIMD instructions and registers. GPU hardware works very similar allowing very efficient parallel processing of wide data streams with a common instruction stream.
These advances in parallel hardware have not been accompanied by the necessary advances in established programming languages. Developers have thus not been enabled to explicitly state the data-parallelism inherent in their algorithms. Some approaches of GPU and CPU vendors have introduced new programming languages, language extensions, or dialects enabling explicit data-parallel programming. However, it is arguable whether the programming models introduced by these approaches deliver the best solution. In addition, some of these approaches have shortcomings from a hardware-specific focus of the language design. There are several programming problems for which the aforementioned language approaches are not expressive and flexible enough.
This thesis presents a solution tailored to the C++ programming language. The concepts and interfaces are presented specifically for C++ but as abstract as possible facilitating adoption by other programming languages as well. The approach builds upon the observation that C++ is very expressive in terms of types. Types communicate intention and semantics to developers as well as compilers. It allows developers to clearly state their intentions and allows compilers to optimize via explicitly defined semantics of the type system.
Since data-parallelism affects data structures and algorithms, it is not sufficient to enhance the language's expressivity in only one area. The definition of types whose operators express data-parallel execution automatically enhances the possibilities for building data structures. This thesis therefore defines low-level, but fully portable, arithmetic and mask types required to build a flexible and portable abstraction for data-parallel programming. On top of these, it presents higher-level abstractions such as fixed-width vectors and masks, abstractions for interfacing with containers of scalar types, and an approach for automated vectorization of structured types.
The Vc library is an implementation of these types. I developed the Vc library for researching data-parallel types and as a solution for explicitly data-parallel programming. This thesis discusses a few example applications using the Vc library showing the real-world relevance of the library. The Vc types enable parallelization of search algorithms and data structures in a way unique to this solution. It shows the importance of using the type system for expressing data-parallelism. Vc has also become an important building block in the high energy physics community. Their reliance on Vc shows that the library and its interfaces were developed to production quality.
FEM–BEM coupling for the thermoelastic wave equation with transparent boundary conditions in 3D
(2022)
We consider the thermoelastic wave equation in three dimensions with transparent boundary conditions on a bounded, not necessarily convex domain. In order to solve this problem numerically, we introduce a coupling of the thermoelastic wave equation in the interior domain with time-dependent boundary integral equations. Here, we want to highlight that this type of problem differs from other wave-type problems that dealt with FEM–BEM coupling so far, e.g., the acoustic as well as the elastic wave equation, since our problem consists of coupled partial differential equations involving a vector-valued displacement field and a scalar-valued temperature field. This constitutes a nontrivial challenge which is solved in this paper. Our main focus is on a coercivity property of a Calderón operator for the thermoelastic wave equation in the Laplace domain, which is valid for all complex frequencies in a half-plane. Combining Laplace transform and energy techniques, this coercivity in the frequency domain is used to prove the stability of a fully discrete numerical method in the time domain. The considered numerical method couples finite elements and the leapfrog time-stepping in the interior with boundary elements and convolution quadrature on the boundary. Finally, we present error estimates for the semi- and full discretization.
The future heavy-ion experiment CBM (FAIR/GSI, Darmstadt, Germany) will focus on the measurements of very rare probes, which require the experiment to operate under extreme interaction rates of up to 10 MHz. Due to high multiplicity of charged particles in heavy-ion collisions, this will lead to the data rates of up to 1 TB/s. In order to meet the modern achievable archival rate, this data ow has to be reduced online by more than two orders of magnitude.
The rare observables are featured with complicated trigger signatures and require full event topology reconstruction to be performed online. The huge data rates together with the absence of simple hardware triggers make traditional latency limited trigger architectures typical for conventional experiments inapplicable for the case of CBM. Instead, CBM will employ a novel data acquisition concept with autonomous, self-triggered front-end electronics.
While in conventional experiments with event-by-event processing the association of detector hits with corresponding physical event is known a priori, it is not true for the CBM experiment, where the reconstruction algorithms should be modified in order to process non-event-associated data. At the highest interaction rates the time difference between hits belonging to the same collision will be larger than the average time difference between two consecutive collisions. Thus, events will overlap in time. Due to a possible overlap of events one needs to analyze time-slices rather than isolated events.
The time-stamped data will be shipped and collected into a readout buffer in a form of a time-slice of a certain length. The time-slice data will be delivered to a large computer farm, where the archival decision will be obtained after performing online reconstruction. In this case association of hit information with physical events must be performed in software and requires full online event reconstruction not only in space, but also in time, so-called 4-dimensional (4D) track reconstruction.
Within the scope of this work the 4D track finder algorithm for online reconstruction has been developed. The 4D CA track finder is able to reproduce performance and speed of the traditional event-based algorithm. The 4D CA track finder is both vectorized (using SIMD instructions) and parallelized (between CPU cores). The algorithm shows strong scalability on many-core systems. The speed-up factor of 10.1 has been achieved on a CPU with 10 hyper-threaded physical cores.
The 4D CA track finder algorithm is ready for the time-slice-based reconstruction in the CBM experiment.
We establish weighted Lp-Fourier extension estimates for O(N−k)×O(k)-invariant functions defined on the unit sphere SN−1, allowing for exponents p below the Stein–Tomas critical exponent 2(N+1)/N−1. Moreover, in the more general setting of an arbitrary closed subgroup G⊂O(N) and G-invariant functions, we study the implications of weighted Fourier extension estimates with regard to boundedness and nonvanishing properties of the corresponding weighted Helmholtz resolvent operator. Finally, we use these properties to derive new existence results for G-invariant solutions to the nonlinear Helmholtz equation −Δu−u = Q(x)|u|p−2u,u∈W2,p(RN), where Q is a nonnegative bounded and G-invariant weight function.
The thesis is composed of four Chapters.
In the first Chapter, the boundary expression of the one-sided shape derivative of nonlocal Sobolev best constants is derived. As a simple consequence, we obtain the fractional version of the so-called Hadamard formula for the torsional rigidity and the first Dirichlet eigenvalue. An application to the optimal obstacle placement problem for the torsional rigidity and the first eigenvalue of the fractional Laplacian is given.
In the second Chapter, we introduce and prove a new maximum principle for doubly antisymmetric functions. The latter can be seen as the first step towards studying the optimal obstacle placement problem for the second fractional eigenvalue. Using the new maximum principle we derive new symmetry results for odd solutions to semilinear Dirichlet boundary value problems with Lipschitz nonlinearity.
In the third Chapter, we derive new integration by parts formula for the fractional Laplace operator with a general globally Lipschitz vector field and in particular, we obtain a new Pohozaev type identity generalizing the one obtained by X. Ros-Oton and J. Serra. As an application we obtain nonexistence results for semilinear Dirichlet boundary problems in bounded domains that are not necessarly starshaped.
In the last Chapter, we study symmetry properties of second eigenfunctions of annuli. Using results from the first Chapter and the maximum principle in Chpater 2, we extend the result on the optimal obstacle placement problem from the first eigenvalue to the second eigenvalue.
We show explicit formulas for the evaluation of (possibly higher-order) fractional Laplacians (-△)ˢ of some functions supported on ellipsoids. In particular, we derive the explicit expression of the torsion function and give examples of s-harmonic functions. As an application, we infer that the weak maximum principle fails in eccentric ellipsoids for s ∈ (1; √3 + 3/2) in any dimension n ≥ 2. We build a counterexample in terms of the torsion function times a polynomial of degree 2. Using point inversion transformations, it follows that a variety of bounded and unbounded domains do not satisfy positivity preserving properties either and we give some examples.
This thesis concerns three specific constraint satisfaction problems: the k-SAT problem, random linear equations and the Potts model. We investigated a phenomenon called replica symmetry, its consequences and its limitation. For the $k$-SAT problem, we were able to show that replica symmetry holds up to a threshold $d^{*}$. However, after another critical threshold $d^{**}$, we discovered that replica symmetry could not hold anymore, which enabled us to establish the existence of a replica symmetry breaking region. For the random linear problem, a peculiar phenomenon occurs. We observed that a more robust version of replica symmetry (strong replica symmetry) holds up to a threshold $d=e$ and ceases to hold after. This phenomenon is linked to the fact that before the threshold $d=e$, the fraction of frozen variables, i.e. variable forced to take the same value in all solutions, is concentrated around a deterministic value but vacillates between two values with equal probability for $d>e$. Lastly, for the Potts model, we show that a phenomenon called metastability occurs. The latter phenomenon can be understood as a consequence of trivial replica symmetry breaking scheme. This metastability phenomenon further produces slow mixing results for two famous Markov chains, the Glauber and the Swendsen-Wang dynamics.
Are nearby places (e.g., cities) described by related words? In this article, we transfer this research question in the field of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar, regardless of their distances and the underlying communities of authors. To investigate this, we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way, we come to the conclusion that geographical places, whether close to each other or not, are located in neighboring semantic places that span similar subnetworks in the topic universe.
Background: The ability to approximate intra-operative hemoglobin loss with reasonable precision and linearity is prerequisite for determination of a relevant surgical outcome parameter: This information enables comparison of surgical procedures between different techniques, surgeons or hospitals, and supports anticipation of transfusion needs. Different formulas have been proposed, but none of them were validated for accuracy, precision and linearity against a cohort with precisely measured hemoglobin loss and, possibly for that reason, neither has established itself as gold standard. We sought to identify the minimal dataset needed to generate reasonably precise and accurate hemoglobin loss prediction tools and to derive and validate an estimation formula.
Methods: Routinely available clinical and laboratory data from a cohort of 401 healthy individuals with controlled hemoglobin loss between 29 and 233 g were extracted from medical charts. Supervised learning algorithms were applied to identify a minimal data set and to generate and validate a formula for calculation of hemoglobin loss.
Results: Of the classical supervised learning algorithms applied, the linear and Ridge regression models performed at least as well as the more complex models. Most straightforward to analyze and check for robustness, we proceeded with linear regression. Weight, height, sex and hemoglobin concentration before and on the morning after the intervention were sufficient to generate a formula for estimation of hemoglobin loss. The resulting model yields an outstanding R2 of 53.2% with similar precision throughout the entire range of volumes or donor sizes, thereby meaningfully outperforming previously proposed medical models.
Conclusions: The resulting formula will allow objective benchmarking of surgical blood loss, enabling informed decision making as to the need for pre-operative type-and-cross only vs. reservation of packed red cell units, depending on a patient’s anemia tolerance, and thus contributing to resource management.
This work describes development of a comprehensive methodology for analyzing vibro-acoustic and wear mechanisms in transmission systems. The thesis addresses certain gaps present in the fields of structure dynamics and abrasion mechanism and opens new areas for further research.
The paper attempts to understand new and relatively unexplored challenges like influences of wear on the dynamics of drive train. It also focuses on developing new techniques for analyzing the vibration and acoustic behavior of the drive unit structures and surrounding fluids respectively.
The developed methodology meets the requirements of both the complete system and component level modeling by using specially identified combination of different simulation techniques. Based on the created template model, a three-stage spur plus helical gearbox is constructed and simulated as an application example. In addition to the internal mechanical excitation mechanisms, the transmission model also includes the rotational and translational dynamics of the gears, shafts and bearings. It is followed by illustration of wear among the rotating components.
Different kinds of static and dynamic analyses are performed and coupled at various levels depending on the mechanical complexities involved. Furthermore, the structure dynamic vibration of the housing and the associated sound particle radiations are mapped into the surrounding fluid. Additionally, the approach for selection of the potential parameters for optimization is depicted. Final part focuses on the measurements of different system states used for validation of the model. In the end, results obtained from both simulations and experiments are analyzed and assessed for there respective performances.
Generic tasks for algorithms
(2020)
Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area of computer science, there is less material available for CT. In this article, we propose so-called Generic Tasks for algorithms inspired by common programming tasks from CS education. Generic Tasks can be seen as a family of tasks with a common underlying structure, format, and aim, and can serve as best-practice examples. They thus bring many advantages, such as facilitating the process of creating new content and supporting asynchronous teaching formats. The Generic Tasks that we propose were evaluated by 14 experts in the field of Science, Technology, Engineering, and Mathematics (STEM) education. Apart from a general estimation in regard to the meaningfulness of the proposed tasks, the experts also rated which and how strongly six core CT skills are addressed by the tasks. We conclude that, even though the experts consider the tasks to be meaningful, not all CT-related skills can be specifically addressed. It is thus important to define additional tasks for CT that are detached from algorithms and programming.
Zielsetzung dieser Arbeit ist es Nutzern, ohne Programmierkenntnisse oder Fachwissen im Bereich der Informatik, Zugang zu der automatischen Verarbeitung von Texten zu gewährleisten. Speziell soll es um Geotagging, also das Referenzieren verschiedener Objekte auf einer Karte, gehen. Als Basis soll ein ontologisches Modell dienen, mit Hilfe dessen Struktur die Objekte in Klassen eingeteilt werden. Zur Verarbeitung des Textes werden NaturalLanguage Processing Werkzeuge verwendet. Natural Language Processing beschreibt Methoden zur maschinellen Verarbeitung natürlicher Sprache. Sie ermöglichen es, die in Texten enthaltenen unstrukturierten Informationen in eine strukturierte Form zu bringen. Die so erhaltenen Informationen können für weitere maschinelle Verarbeitungsschritte verwendet oder einem Nutzer direkt bereitgestellt werden. Sollten sie direkt bereitgestellt werden, ist es ausschlaggebend, sie in einer Form zu präsentieren, die auch ohne Fachkenntnisse oder Vorwissen verständlich ist. Im Bereich der Geographie wird oft der Ansatz befolgt, die erhaltenen Informationen auf Basis verschiedener Karten, also visuell zu verarbeiten. Visualisierungen dienen hierbei der Veranschaulichung von Informationen. Durch sie werden die relevanten Aspekte dem Nutzer verdeutlicht und so die Komplexität der Informationen reduziert. Es bietet sich also an, die durch das Natural Language Processing gesammelten Informationen in Form einer Visualisierung für den Nutzer zugänglich zu machen. Im Rahmen dieser Arbeit über Geotagging und Ontologie-basierte Visualisierung für das TextImaging wird ein Tool entwickelt, das diese Brücke schlägt. Die Texte werden auf einer Karte visualisiert und bieten so eine Möglichkeit, beschriebene geographische Zusammenhänge auf einen Blick zu erfassen. Durch die Kombination der Visualisierung auf einer Karte und der Markierung der entsprechenden Entitäten im Text kann eine zuverlässige und nutzerfreundliche Visualisierung erzeugt werden. Bei einer abschließenden Evaluation hat sich gezeigt das mit dem Tool der Zeitaufwand und die Anzahl der fehlerhaften Annotationen reduziert werden konnte.Die von dem Tool gebotenen Funktionen machen dieses auch für weiterführende Arbeiten interessant. Eine Möglichkeit ist die entwickelten Annotatoren zu verwenden um ein ontology matching auf Basis bestimmter Texte auszuführen. Im Bereich der Visualisierung bieten sich Projekte wie die Visualisierung historischer Texte auf Basis automatisch ermittelter, zeitgerechter Karten an.
In dieser Arbeit werden drei Themenkomplexe aus dem Bereich der Externspeicheralgorithmen näher beleuchtet: Approximationsalgorithmen, dynamische Algorithmen und Echtzeitanfragen. Das Thema Approximationsalgorithmen wird sowohl im Kapitel 3 als auch im Kapitel 5 behandelt.
In Kapitel 3 wird ein Algorithmus vorgestellt, welcher den Durchmesser eines Graphen heuristisch bestimmt. Im RAM- Modell ist eine modifizierte Breitensuche selbst ein günstiger und äußerst genauer Algorithmus. Dies ändert sich im Externspeicher. Dort ist die Hauptspeicher-Breitensuche durch die O(n + m) unstrukturierten Zugriffe auf den externen Speicher zu teuer. 2008 wurde von Meyer ein Verfahren zu effizienten Approximation des Graphdurchmessers im Externspeicher gezeigt, welches O(k · scan(n + m) + sort(n + m) + √(n·m/k·B)· log(n/k) + MST(n, m)) I/Os bei einem multiplikativen Approximationsfehler von O(√k · log (k)) benötigt. Die Implementierung, welche in dieser Arbeit vorgestellt wird, konnte in vielen praktischen Fällen die Anzahl an I/Os durch Rekursion auf O(k · scan(n + m) + sort(n + m) + MST(n, m)) I/Os reduzieren. Dabei wurden verschiedene Techniken untersucht, um die Auswahl der Startpunkte (Masterknoten) zum rekursiven Schrumpfen des Graphen so wählen zu können, dass der Fehler möglichst klein bleibt. Weiterhin wurde eine adaptive Regel eingeführt, um nur so viele Masterknoten zu wählen, dass der geschrumpfte Graph nach möglichst wenigen Rekursionsaufrufen in den Hauptspeicher passt. Es wirdgezeigt, dass die untere Schranke für den worst case-Fehler dabei auf Ω(k^{4/3−e}) mit hoher Wahrscheinlichkeit steigt. Die experimentelle Auswertung zeigt jedoch, dass in der Praxis häufig deutlich bessere Ergebnisse erzielt werden.
In Kapitel 4 wird ein Algorithmus vorgestellt, welcher, nach dem Einfügen einer neuen Kante in einen Graphen, den zugehörigen Baum der Breitensuche unter Verwendung von O(n · (n/B^{2/3} + sort(n) · log (B))) I/Os mit hoher Wahrscheinlichkeit aktualisiert. Dies ist für hinreichend große B schneller als die statische Neuberechnung. Zur Umsetzung des Algorithmus wurde eine neue deterministische Partitionsmethode entwickelt, bei der die Größe der Cluster balanciert und effizient veränderbar ist. Hierfür wird ein Dendrogramm des Graphen auf einer geeigneten Baumrepräsentation, wie beispielsweise Spannbaum, berechnet. Dadurch hat jeder Knoten ein Label, welches aufgrund seiner Lage innerhalb der Baumrepräsentation berechnet worden ist. Folglich kann mittels schneller Bit-Operationen das Label um niederwertige Stellen gekürzt werden, um Cluster der Größe µ = 2 i zu berechnen, wobei der Clusterdurchmesser auf µ beschränkt ist, was für die I/O-Komplexität gewährleistet sein muss, da der Trade-off aus MM_BFS zwischen Cluster- und Hotpoolgröße genutzt wird. In der experimentellen Auswertung wird gezeigt, dass die Performanz von dynamischer Breitensuche sowohl auf synthetischen als auch auf realen Daten oftmals schneller ist als eine statische Neuberechnung des Baums der Breitensuche. Selbst wenn dies nicht der Falls ist, so sind wir nur um kleine, konstante Faktoren langsamer als die statische Implementierung von MM_BFS.
Schließlich wird in Kapitel 5 ein Approximationsalgorithmus vorgestellt, welcher sowohl dynamische Komponenten beinhaltet als auch die Eigenschaft besitzt, Anfragen in Echtzeit zu beantworten. Um die Echtzeitfähigkeit zu erreichen, darf eine Anfrage nur O(1) I/Os hervorrufen. Im Szenario dieser Arbeit wurden Anfragen zu Distanzen zwischen zwei beliebigen Knoten u und v auf realen Graphdaten mittels eines Distanzorakels beantwortet. Es wird eine Implementierung sowohl für mechanische Festplatten als auch für SSDs vorgestellt, wobei kontinuierliche Anfragen im Onlineszenario von SSDs in Millisekunden gelöst werden können, während ein großer Block von Anfragen auf beiden Architekturen in Mikrosekunden pro Anfrage amortisiert gelöst werden kann.
This dissertation is concerned with the task of map-based self-localization, using images of the ground recorded with a downward-facing camera. In this context, map-based (self-)localization is the task of determining the position and orientation of a query image that is to be localized. The map used for this purpose consists of a set of reference images with known positions and orientations in a common coordinate system. For localization, the considered methods determine correspondences between features of the query image and those of the reference images.
In comparison with localization approaches that use images of the surrounding environment, we expect that using images of the ground has the advantage that, unlike the surrounding, the visual appearance of the ground is often long-term stable. Also, by using active lighting of the ground, localization becomes independent of external lighting conditions.
This dissertation includes content of several published contributions, which present research on the development and testing of methods for feature-based localization of ground images. Our first contribution examines methods for the extraction of image features that have not been designed to be used on ground images. This survey shows that, with appropriate parametrization, several of these methods are well suited for the task.
Based on this insight, we develop and examine methods for various subtasks of map-based localization in the following contributions. We examine global localization, where all reference images have to be considered, as well as local localization, where an approximation of the query image position is already known, which allows for disregarding reference images with a large distance to this position.
In our second contribution, we present the first systematic comparison of state-of-the-art methods for ground texture based localization. Furthermore, we present a method, which is characterized by its usage of our novel feature matching technique. This technique is called identity matching, as it matches only those features with identical descriptors, in contrast to the state-of-the-art that also matches features with similar descriptors. We show that our method is well suited for global and local localization, as it has favorable scaling with the number of reference images considered during the localization process. In another contribution, we develop a variant of our localization method that is significantly faster to compute, as it applies a sampling approach to determine the image positions at which local features are extracted, instead of using classical feature detectors.
Two further contributions are concerned with global localization. The first one introduces a prediction model for the global localization performance, based on an evaluation of the local localization performance. This allows us to quickly evaluate any considered parameter settings of global localization methods. The second contribution introduces a learning-based method that computes compact descriptors of ground images. This descriptor can be used to retrieve the overlapping reference images of a query image from a large set of reference images with little computational effort.
The most recent contribution included in this dissertation presents a new ground image database, which was recorded with a dedicated platform using a downward-facing camera. In addition to the data, we also explain our guidelines for the construction of the platform. In comparison with existing databases, our database contains more images and presents a larger variety of ground textures. Furthermore, this database enables us to perform the first systematic evaluation of how localization performance is affected by the time interval between the point in time at which the reference images are recorded and the point in time at which the query image is recorded. We find out that for outdoor areas all ground texture based localization methods have reliability issues, if the time interval between the recording of the query and reference images is large, and also if there are different weather conditions. These findings point to remaining challenges in ground texture base localization that should be addressed in future work.
Through the glasses of didactic reduction, we consider a (periodic) tessellation Δ of either Euclidean or hyperbolic 𝑛-space 𝑀. By a piecewise isometric rearrangement of Δ we mean the process of cutting 𝑀 along corank-1 tile-faces into finitely many convex polyhedral pieces, and rearranging the pieces to a new tight covering of the tessellation Δ. Such a rearrangement defines a permutation of the (centers of the) tiles of Δ, and we are interested in the group of 𝑃𝐼(Δ) all piecewise isometric rearrangements of Δ. In this paper, we offer (a) an illustration of piecewise isometric rearrangements in the visually attractive hyperbolic plane, (b) an explanation on how this is related to Richard Thompson's groups, (c) a section on the structure of the group pei(ℤ𝑛) of all piecewise Euclidean rearrangements of the standard cubically tessellated ℝ𝑛, and (d) results on the finiteness properties of some subgroups of pei(ℤ𝑛).
Analysing survival or fixation probabilities for a beneficial allele is a prominent task in the field of theoretical population genetics. Haldane's asymptotics is an approximation for the fixation probability in the case of a single beneficial mutant with small selective advantage in a large population.
In this thesis we analyse the interplay between genetic drift and directional selection and prove Haldane's asymptotics in different settings: For the fixation probability in Cannings models with moderate selection and for the survival probability of a slightly supercritical branching processes in a random environment.
In Chapter 3 we introduce a class of Cannings models with selection that allow for a forward and backward construction. In particular, a Cannings ancestral selection process can be defined for this class of models, which counts the number of potential parents and is in sampling duality to the forward frequency process. By means of this duality the probability of fixation can be expressed through the expectation of the Cannings ancestral selection process in stationarity. A control of this expectation yields that the fixation probability fulfils Haldane's asymptotics in a regime of moderately weak selection (Thm. 8).
In Chapter 4 we study the fixation probability of Cannings models in a regime of moderately strong selection. Here couplings of the frequency process of beneficial individuals with slightly supercritical Galton-Watson processes imply that the fixation probability is given by Haldane's asymptotics (Thm. 9).
Lastly, in Chapter 5 we consider slightly supercritical branching processes in an independent and identically distributed random environment and study the probability of survival as the number of expected offspring tends from above to one. We show that only if variance and expectation of the random offspring mean are of the same order the random environment has a non-trivial influence on the probability of survival, which results in a modification of Haldane's asymptotics. Out of the critical parameter regime the population goes extinct or survives with a probability that fulfils Haldane's asymptotics (Thm. 10).
The proof establishes an expression for the survival probability in terms of the shape function of the random offspring generating functions. This expression exhibits similarities to perpetuities known from a financial context. Consequently, we prove a limiting theorem for perpetuities with vanishing interest rates (Thm. 11).
Hierarchical self-organizing systems for task-allocation in large scaled distributed architectures
(2019)
This thesis deals with the subject of autonomous, decentralized task allocation in a large scaled multi-core network. The self-organization of such interconnected systems becomes more and more important for upcoming developments. It is to be expected that the complexity of those systems becomes hardly manageable to human users. Self-organization is part of a research field of the Organic Computing initiative, which aims to find solutions for technical systems by imitating natural systems and their processes. Within this initiative, a system for task allocation in a small scaled multi-core network was already developed, researched and published. The system is called the Artificial Hormone System (AHS), since it is inspired by the endocrine system of mammals. The AHS produces a high amount of communication load in case the multi-core network is of a bigger scale.
The contribution of this thesis is two new approaches, both based on the AHS in order to cope with large scaled architectures. The major idea of those two approaches is to introduce a hierarchy into the AHS in order to reduce the produced communication load. The first and more detailed researched approach is called the Hierarchical Artificial Hormone System (HAHS), which orders the processing elements in clusters and builds an additional communication layer between them. The second approach is the Recursive Artificial Hormone System (RAHS), which also clusters the system’s processing elements and orders the clusters into a topological tree structure for communication.
Both approaches will be explained in this thesis by their principle structure as well as some optional methods. Furthermore, this thesis presents estimations for the worst case timing behavior and the worst-case communication load of the HAHS and RAHS. At last, the evaluation results of both approaches, especially in comparison to the AHS, will be shown and discussed.
In this article, we prove the Hodge conjecture for a desingularization of the moduli space of rank 2, semi-stable, torsion-free sheaves with fixed odd degree determinant over a very general irreducible nodal curve of genus at least 2. We also compute the algebraic Poincaré polynomial of the associated cohomology ring.
In online video games toxic interactions are very prevalent and often
even considered an imperative part of gaming.
Most studies analyse the toxicity in video games by analysing the messages that are sent during a match, while only a few focus on other interactions. We focus specifically on the in-game events to try to identify toxic matches, by constructing a framework that takes a list of time-based events and projects them into a graph structure which we can then analyse with current methods in the field of graph representation learning.
Specifically we use a Graph Neural Network and Principal Neighbour-
hood Aggregation to analyse the graph structure to predict the toxicity of a match.
We also discuss the subjectivity behind the term toxicity and why the
process of only analysing in-game messages with current state-of-the-art NLP methods isn’t capable to infer if a match is perceived as toxic or not.
Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Non-Fungible Token und die Blockchain Technologie haben in dem vergangenen Jahr immer mehr an Popularität gewonnen. Wie bei jeder neuartigen Technologie stellt sich jedoch die Frage, in welchen Bereichen diese eine Anwendung finden können.
Das Ziel in der vorliegenden Arbeit ist es zu beantworten, ob Non-Fungible Token und die Blockchain Technologie eine sinnvolle Anwendung im Bereich von akademischen Zertifikaten hat.
Um diese Frage zu beantworten, sind Gründe für die Anwendung von Non-Fungible Token gegen Nachteile abgewogen und Lösungsansätze für potentielle Risiken erhoben worden. Außerdem wurde selbstständig ein ERC-721 Token Contract für akademische Zertifikate mittels Solidity entwickelt.
Die Arbeit zeigt, dass Blockchain basierte akademische Zertifikate vor allem die Mobilität von Studenten unterstützen, den administrativen Aufwand der Ausstellung und Verifizierung von Abschlusszeugnissen verringern und entgegen der Fälschung von Abschlüssen arbeiten. Außerdem können erwägte Risiken und Nachteile durch Zusammenschluss von Institutionen zu einer Konsortialen Blockchain umgangen werden.
Die erfolgreiche Entwicklung des ERC-721 Token Contracts “MetaDip” zeigt eine potentielle Umsetzung für die Digitalisierung von Abschlusszeugnissen und demonstriert, dass Non-Fungible Token basierte akademische Zertifikate aktuell bereits technisch realisierbar sind.
Die Arbeit legt dar, dass Non-Fungible Token und die Blockchain Technologie eine vielversprechende Zukunft für akademische Zertifikate bietet und bereits von vereinzelten Institutionen realisiert wird. Jedoch müssen noch einige Vorkehrungen getroffen werden, bevor eine breite Umsetzung von Blockchain basierten akademischen Zertifikaten möglich ist.
Szenen automatisch aus Texten generieren zu können ist eine interessante Aufgabe der Informatik. Für diese Aufgabe wurde VANNOTATOR (Mehler und Abrami 2019, Abrami, Spiekermann und Mehler 2019, Spiekermann, Abrami und Mehler 2018) entwickelt, ein Framework, das die Beschreibung bzw. Beschriftung von VR-Szenen ermöglicht. Damit für diese Szenen die benötigten 3D-Objekte bereitgestellt werden können, sind entsprechende Datenbanken vonnöten. Diese Datenbanken müssen umfangreich annotiert sein, damit diese Aufgabe bewältigt werden kann. Deshalb wurde im Falle des VANNOTATORs auf die ShapeNetSem Datenbank zurückgegriffen (Abrami, Henlein, Kett u. a. 2020).
Je detailreicher eine Szene dargestellt wird, desto detailreicher kann diese auch durch einen Text beschrieben werden. Aus diesem Grund wird die Datenbank um einen Teilbereich von PartNet (Mo u. a. 2019) erweitert. Dieser erlaubt die Option, Objekte zu segmentieren, und erweitert hierdurch das annotierbare Vokabular. Manche der bereits vorhandenen ShapeNetSem-Objekte verfügen über die Eigenschaft, dass sie auch PartNet-Objekte sind. Diese Arbeit befasst sich mit der Umsetzung, wie ShapeNetSem-Objekte mit hinterlegten PartNetObjekten durch diese ersetzt werden können. Um das zu bewerkstelligen, wurde ein Panel entworfen, in welchem ein PartNet-Objekt mit samt seinen einzelnen Segmenten aufgeführt wird. Diese Segmente können nun wie ShapeNetSem-Objekte ausgewählt und in einer Szene platziert werden. Dadurch werden 1.881 Objekte mit wiederum 34.016 Unterobjekten VANNOTATOR zur Verfügung gestellt. Dieses vergrößerte Vokabular hilft Natural Language Processing noch effektiver und präziser voranzutreiben.
Neuropsychiatric disorders are complex, highly heritable but incompletely understood disorders. The clinical and genetic heterogeneity of these disorders poses a significant challenge to the identification of disorder related biomarkers. Besides significant progress in unveiling the genetic basis of these disorders, the underlying causes and biological mechanisms remain obscure. With the advancement in the array, sequencing, and big data technologies, a huge amount of data is generated from individuals across different platforms and in various data structures. But there is a paucity of bioinformatics tools that can integrate this plethora of data. Therefore, there is a need to develop an integrative bioinformatics data analysis tool that combines biological and clinical data from different data types to better understand the underlying genetics.
This thesis presents a bioinformatics pipeline implementing data from different platforms to provide a thorough understanding of the genetic etiology of a neuropsychiatric quantitative as well as a qualitative trait of interest. Throughout the thesis, we present two aspects: one is the development and architecture of the bioinformatics pipeline named MApping the Genetics of neuropsychiatric traits to the molecular NETworks of the human brain (MAGNET). The other part demonstrates the implementation and usefulness of MAGNET analysing large Autism Spectrum Disorder (ASD) cohorts.
MAGNET is a freely available command-line tool available on GitHub (https://github.com/SheenYo/MAGNET). It is implemented within one framework using data integration approaches based on state-of-the-art algorithms and software to ultimately identify the genes and pathways genetically associated with a trait of interest. MAGNET provides an edge over the existing tools since it performs a comprehensive analysis taking care of the data handling and parsing steps necessary to communicate between the different APIs (Application Program Interface). Thus, this avoids the in-between data handling steps required by researchers to provide output from one analysis to the next. Moreover, depending on the size of the dataset users can deduce important information regarding their trait of interest within a time frame of a few days. Besides gaining insights into genetic associations, one of the central features is the mapping of the associated genes onto developing human brain implementing transcriptome data of 16 different brain regions starting from the 5th post-conceptional week to over 40 years of age.
In the second part as proof of concept, we implemented MAGNET on two ASD cohorts. ASD is a group of psychiatric disorders. Clinically, ASD is characterized by the following psychopathology: A) limitations in social interaction and communication, and B) restricted, repetitive behavior. The etiology of this disorder is extremely complex due to its heterogeneous clinical traits and genetics. Therefore, to date, no reliable biomarkers are identified. Here, the aim is to characterize the genetic architecture of ASD taking into account the two aforementioned ASD diagnostic domains. As well as to investigate if these domains are genetically linked or independent of each other. Moreover, we addressed the question if these traits share genetic risk with the categorical diagnosis of ASD and how much of the phenotypic variance of these traits can be explained by the underlying genetics.
We included affected individuals from two ASD cohorts, i.e. the Autism Genome Project (AGP) and a German cohort consisting of 2,735 and 705 families respectively. MAGNET was applied to each of the ASD subdomains as a quantitative dependent variable. MAGNET is divided into five main sections i.e. (1) quality check of the genotype data, (2) imputation of missing genotype data, (3) association analysis of genotype and trait data, (4) gene-based analysis, and (5) enrichment analysis using gene expression data from the human brain.
MAGNET was applied to each of the individual traits in each cohort to perform quality control of the genetic data and imputed the missing data in an automated fashion. MAGNET identified 292 known and new ASD risk genes. These genes were subsequently assigned to biological signaling pathways and gene ontologies via MAGNET. The underlying biological mechanisms converged with respect to neuronal transmission and development processes. By reconciling these genes with the transcriptome of the developing human brain, MAGNET was able to identify that the significant genes associated with the subdomains are expressed at specific time points in brain areas such as the hippocampus, amygdala, and cortical regions. Further, we found that ASD subdomains related to domain A but not
to domain B have a shared genetic etiology.
Nowadays, digitalization has an immense impact on the landscape of jobs. This technological revolution creates new industries and professions, promises greater efficiency and improves the quality of working life. However, emerging technologies such as robotics and artificial intelligence (AI) are reducing human intervention, thus advancing automation and eliminating thousands of jobs and whole occupational images. To prepare employees for the changing demands of work, adequate and timely training of the workforce and real-time support of workers in new positions is necessary. Therefore, it is investigated whether user-oriented technologies, such as augmented reality (AR) and virtual reality (VR) can be applied “on-the-job” for such training and support—also known as intelligence augmentation (IA). To address this problem, this work synthesizes results of a systematic literature review as well as a practically oriented search on augmented reality and virtual reality use cases within the IA context. A total of 150 papers and use cases are analyzed to identify suitable areas of application in which it is possible to enhance employees' capabilities. The results of both, theoretical and practical work, show that VR is primarily used to train employees without prior knowledge, whereas AR is used to expand the scope of competence of individuals in their field of expertise while on the job. Based on these results, a framework is derived which provides practitioners with guidelines as to how AR or VR can support workers at their job so that they can keep up with anticipated skill demands. Furthermore, it shows for which application areas AR or VR can provide workers with sufficient training to learn new job tasks. By that, this research provides practical recommendations in order to accompany the imminent distortions caused by AI and similar technologies and to alleviate associated negative effects on the German labor market.
Goal-Conditioned Reinforcement Learning (GCRL) is a popular framework for training agents to solve multiple tasks in a single environment. It is cru- cial to train an agent on a diverse set of goals to ensure that it can learn to generalize to unseen downstream goals. Therefore, current algorithms try to learn to reach goals while simultaneously exploring the environment for new ones (Aubret et al., 2021; Mendonca et al., 2021). This creates a form of the prominent exploration-exploitation dilemma. To relieve the pres- sure of a single agent having to optimize for two competing objectives at once, this thesis proposes the novel algorithm family Goal-Conditioned Re- inforcement Learning with Prior Intrinsic Exploration (GC-π), which sep- arates exploration and goal learning into distinct phases. In the first ex- ploration phase, an intrinsically motivated agent explores the environment and collects a rich dataset of states and actions. This dataset is then used to learn a representation space, which acts as the distance metric for the goal- conditioned reward signal. In the final phase, a goal-conditioned policy is trained with the help of the representation space, and its training goals are randomly sampled from the dataset collected during the exploration phase. Multiple variations of these three phases have been extensively evaluated in the classic AntMaze MuJoCo environment (Nachum et al., 2018). The fi- nal results show that the proposed algorithms are able to fully explore the environment and solve all downstream goals while using every dimension of the state space for the goal space. This makes the approach more flexible compared to previous GCRL work, which only ever uses a small subset of the dimensions for the goals (S. Li et al., 2021a; Pong et al., 2020).
In der aktuellen Zeit gibt es eine Vielzahl an annotierten Texten und anderen Medien. Genauso gibt es verschiedenste Möglichkeiten neue Texte zu annotieren, sowohl manuell als auch automatisch. Es gibt Systeme, die diese Annotationen in andere, visuell ansprechendere Medien umwandeln. Zu diesen Systemen gehören auch die Text2Scene Systeme, dort wird ein annotierter Text in eine dreidimensionale Szene umgewandelt. Ein Teil dieser Text2Scene Systeme können auch Personen durch Modelle von Menschen darstellen, aber bis jetzt gibt es noch kein System, dass Avatar Modelle selber synthetisieren kann.
Der Fokus dieser Arbeit liegt sowohl darauf eine Schnittstelle bereitzustellen, mit der Avatare mit bestimmten Parametern erstellt werden können, als auch die Möglichkeit diese Avatare in der virtuellen Realität anzuzeigen und zu bearbeiten. Man kann in einer virtuellen Szene die Eigenschaften bestimmter Körperteile anpassen und die Kleidung der Avatare auswählen.
The single-source shortest-path problem is a fundamental problem in computer science. We consider a generalization of the shortest-path problem, the $k$-shortest path problem. Let $G$ be a directed edge-weighted graph with $n$ nodes and $m$ edges and $s,t$ be two fixed nodes. The goal is to compute $k$ paths $P_1,\dots,P_k$ between two fixed nodes $s$ and $t$ in non-decreasing order of their length such that all other paths between $s$ and $t$ are at least as long as the $k$\nth path $P_k$. We focus on the version of the $k$-shortest path problem where the paths are not allowed to visit nodes multiple times, sometime referred to as $k$-shortest simple path problem.
The probably best known $k$-shortest path algorithm is Yen's algorithm. It has a worst-case time complexity of O(kn\cdot scp(n,m)), where scp(n,m) is the complexity of the single-source shortest-path algorithm used as a subroutine. In case of Dijkstra's algorithm scp(n,m) is O(m + n\log n). One of the more recent improvements of Yen's algorithm is by Feng.
Even though Feng's algorithm is much faster in practice, it has the same worst-case complexity as Yen's algorithm.
The main results presented in this thesis are upper bounds on the average-case of Yen's and Feng's algorithm, as well as practical improvements and a parallel implementation of Yen's and Feng's algorithms including these improvements. The implementation is publicly available under GPLv3 open source license.
We show in our analysis that Yen's algorithm has an average-case complexity of O(k \log(n)\cdot scp(n,m)) on G(n,p) graphs with at least logarithmic average-degree and random edge weights following a distribution with certain properties.
On G(n,p) graphs with constant to logarithmic average-degree and uniform random edge-weights over $[0;1]$, we show an average-case complexity of O(k\cdot\frac{\log^2 n}{np}\cdot scp(n,m)). Feng's algorithm has an even better average-case complexity of O(k\cdot scp(n,m)) on unweighted G(n,p) graphs with logarithmic average-degree and for constant values of $k$. We further provide evidence that the same holds true for G(n,p) graphs with uniform random edge-weights over $[0;1]$.
On the practical side, we suggest new heuristics to prune even more single-source shortest-path computations than Feng's algorithm and evaluate all presented algorithms on G(n,p) and Grid graphs with up to 256 million nodes. We demonstrate speedups by a factor of up to 40 compared to Feng's algorithm.
Finally we discuss two ways to parallelize the suggested algorithms and evaluate them on grid graphs showing speedups by a factor of 2 using 4 threads and by a factor of up to 8 using 16 threads, respectively.
The main task of modern large experiments with heavy ions, such as CBM (FAIR), STAR (BNL) and ALICE (CERN) is a detailed study of the phase diagram of quantum chromodynamics (QCD) in the quark-gluon plasma (QGP), the equation of state of matter at extremely high baryonic densities, and the transition from the hadronic phase of matter to the quark-gluon phase.
In the thesis, the missing mass method is developed for the reconstruction of short-lived particles with neutral particles in their decay products, as well as its implementation in the form of fast algorithms and a set of software for prac- tical application in heavy ion physics experiments. Mathematical procedures implementing the method were developed and implemented within the KF Par- ticle Finder package for the future CBM (FAIR) experiment and subsequently adapted and applied for processing and analysis of real data in the STAR (BNL) experiment.
The KF Particle Finder package is designed to reconstruct most signal particles from the physics program of the CBM experiment, including strange particles, strange resonances, hypernuclei, light vector mesons, charm particles and char- monium. The package includes searches for over a hundred decays of short-lived particles. This makes the KF Particle Finder a universal platform for short-lived particle reconstruction and physics analysis both online and offline.
The missing mass method has been proposed to reconstruct decays of short-lived charged particles when one of the daughter particles is neutral and is not regis- tered in the detector system. The implementation of the missing mass method was integrated into the KF Particle Finder package to search for 18 decays with a neutral daughter particle.
Like all other algorithms of the KF Particle Finder package, the missing mass method is implemented with extensive use of vector (SIMD) instructions and is optimized for parallel operation on modern many-core high performance com- puter clusters, which can include both processors and coprocessors. A set of algorithms implementing the method was tested on computers with tens of cores and showed high speed and practically linear scalability with respect to the num- ber of cores involved.
It is extremely important, especially for the initial stage of the CBM experiment, which is planned for 2025, to demonstrate already now on real data the reliability of the developed approach, as well as the high efficiency of the current implemen- tation of both the entire KF Particle Finder package, and its integral part, the missing mass method. Such an opportunity was provided by the FAIR Phase-0 program, motivating the use in the STAR experiment of software packages orig- inally developed for the CBM experiment.
Application of the method to real data of the STAR experiment shows very good results with a high signal-to-background ratio and a large significance value. The results demonstrate the reliability and high efficiency of the missing mass method in the reconstruction of both charged mother particles and their neutral daughter particles. Being an integral part of the KF Particle Finder package, now the main approach for reconstruction and analysis of short-lived particles in the STAR experiment, the missing mass method will continue to be used for the physics analysis in online and offline modes.
The high quality of the results of the express data analysis has led to their status as preliminary physics results with the right to present them at international physics conferences and meetings on behalf of the STAR Collaboration.
Studying large discrete systems is of central interest in, non-exclusively, discrete mathematics, computer sciences and statistical physics. The study of phase transitions, e.g. points in the evolution of a large random system in which the behaviour of the system changes drastically, became of interest in the classical field of random graphs, the theory of spin glasses as well as in the analysis of algorithms [78,82, 121].
It turns out that ideas from the statistical physics’ point of view on spin glass systems can be used to study inherently combinatorial problems in discrete mathematics and theoretical computer sciences(for instance, satisfiability) or to analyse phase transitions occurring in inference problems (like the group testing problem) [68, 135, 168]. A mathematical flaw of this approach is that the physical methods only render mathematical conjectures as they are not known to be rigorous.
In this thesis, we will discuss the results of six contributions. For instance, we will explore how the
theory of diluted mean-field models for spin glasses helps studying random constraint satisfaction problems through the example of the random 2−SAT problem. We will derive a formula for the number of satisfying assignments that a random 2−SAT formula typically possesses [2].
Furthermore, we will discuss how ideas from spin glass models (more precisely, from their planted versions) can be used to facilitate inference in the group testing problem. We will answer all major open questions with respect to non-adaptive group testing if the number of infected individuals scales sublinearly in the population size and draw a complete picture of phase transitions with respect to the
complexity and solubility of this inference problem [41, 46].
Subsequently, we study the group testing problem under sparsity constrains and obtain a (not fully understood) phase diagram in which only small regions stay unexplored [88].
In all those cases, we will discover that important results can be achieved if one combines the rich theory of the statistical physics’ approach towards spin glasses and inherent combinatorial properties of the underlying random graph.
Furthermore, based on partial results of Coja-Oghlan, Perkins and Skubch [42] and Coja-Oghlan et al. [49], we introduce a consistent limit theory for discrete probability measures akin to the graph limit theory [31, 32, 128] in [47]. This limit theory involves the extensive study of a special variant of the cut-distance and we obtain a continuous version of a very simple algorithm, the pinning operation, which allows to decompose the phase space of an underlying system into parts such that a probability
measure, restricted to this decomposition, is close to a product measure under the cut-distance. We will see that this pinning lemma can be used to rigorise predictions, at least in some special cases, based on the physical idea of a Bethe state decomposition when applied to the Boltzmann distribution.
Finally, we study sufficient conditions for the existence of perfect matchings, Hamilton cycles and bounded degree trees in randomly perturbed graph models if the underlying deterministic graph is sparse [93].
In der modernen Hochschullehre haben sich eLearning-Elemente als ein Teil des Lehrrepertoires etabliert. Der Einsatz interaktiver webbasierter Selbstlernmodule (Web Based Trainings (WBT)) ist dabei eine Option. Hochschulen und Unternehmen versprechen sich dadurch neue Möglichkeiten des Lehrens und Lernens, um z. B. einen Ausgleich heterogener Vorerfahrungen sowie eine stärkere aktive Beteiligung der Lernenden zu bewirken. Damit die Erstellung und Strukturierung dieser Inhalte mit möglichst geringem Aufwand erfolgen kann, bieten Autorensysteme Unterstützung.
Zu den Grundfunktionen von Autorensystemen gehören unter anderem, das Einbinden gebräuchlicher Medienformate, die einfache Erstellung von Fragen sowie verschiedene Auswertungs- und Feedbackmöglichkeiten. Obwohl Autorensysteme schon vor vielen Jahren ihre erste praktische Anwendung fanden, gibt es nach wie vor Schwachstellen, die sich auf den gesamten Erstellungsprozess wie auch auf einzelne Funktionen beziehen. Im Detail wird bemängelt, dass die Werkzeuge zu komplex und unflexibel sind. Darüber hinaus fehlt häufig eine zufriedenstellende Verknüpfung der vielen Werkzeuge entlang der Prozesskette zu einer Gesamtlösung.
Des Weiteren wird die Konzentration auf die Produktionsphase kritisiert, wodurch andere wichtige Prozesse in den Hintergrund treten bzw. außer Acht gelassen werden.
Im Rahmen der Zusammenarbeit mit einem Automobilhersteller, für den die erste Version des Autorensystems LernBar weiterentwickelt wurde, spielte der Begriff „Lean Production“ inhaltlich in der Umsetzung der WBTs eine wesentliche Rolle. Die Lean Production, die über viele Jahre für die Automobilindustrie entwickelt, verbessert und angepasst wurde, liefert Optimierungsansätze für den Produktionsbereich. Ein wirtschaftlicher Nutzen des Lean-Ansatzes wird auch in anderen Bereichen gesehen wie z. B. in der Softwareentwicklung („Lean Software Development“) oder im Management („Lean Management“). Dabei bietet die Wertschöpfungsorientierung Lösungen für die widersprüchlichen Ziele mehr Leistungen zu geringeren Kosten, schneller und in höherer Qualität zugleich zu liefern. Aus der Grundidee der Lean Production entwickelte sich vorliegendes Dissertationsthema in Bezug darauf, inwiefern sich diese Prinzipien auf den WBT-Produktionsprozess übertragen lassen und die LernBar (das hierfür weiterentwickelnde Autorensystem) dabei Unterstützung bieten kann.
Zunächst wurde analysiert, welche Werkzeuge und Hilfestellungen benötigt werden, um unter dem Aspekt der Lean Production WBTs im universitären Umfeld erstellen zu können. In diesem Zusammenhang wurden Merkmale einer „Lean Media Production“ definiert sowie konzeptionell und technisch umgesetzt. Zur Verbesserung der Prozesse flossen Ergebnisse aus empirischer und praktischer Forschung ein. Im Vergleich zu anderen Entwicklungen bei denen häufig das Hauptziel eine umfangreiche Funktionalität ist, werden u.a. folgende übertragbare Ziele bei der Umsetzung verfolgt: Verschwendung vermeiden, eine starke Einbeziehung der Kunden, Werkzeuge die nahtlos ineinandergreifen, eine hohe Flexibilität und eine stetige Qualitätsverbesserung.
Zur Erreichung dieser Zielsetzungen wurden alle Prozesse kontinuierlich verbessert, sich auf das Wesentliche und die Wertschöpfung konzentriert sowie überflüssige Schritte eliminiert. Demnach ist unter dem Begriff „Lean Media Production“ ein skalierbarer, effizienter und effektiver Produktionsprozess zu verstehen, in dem alle Werkzeuge ineinandergreifen.
Die Realisierung der „Lean Media Production“ erfolgte anhand des Autorensystems LernBar, wobei die typischen Softwareentwicklungsphasen Entwurf, Implementierung und Evaluierung mehrfach durchlaufen wurden. Ausschlaggebend dabei war, dass der „Lean“-Aspekt berücksichtigt wurde und dies somit eine neue Vorgehensweise bei der Umsetzung eines Autorensystems darstellt. Im Verlauf der Entwicklungen ergaben sich, durch eine formative Evaluation, den Einsatz in Projekten und eine empirische Begleitforschung, neue Anforderungen an das System. Ein Vergleich der zwei Produktionssysteme, Automobil vs. WBT-Produktion, zeigt und bestätigt die Erwartung, dass nicht alle Prinzipien der Lean Production übertragbar sind.
Dennoch war diese Untersuchung notwendig, da sie Denkanstöße zur Entwicklung und Optimierung des Erstellungsprozesses eines WBTs gab. Auch die Ergebnisse der abschließenden Online-Befragung ergaben, dass die Ziele der Arbeit erreicht wurden, dass aber weiterer Optimierungsbedarf besteht. Die LernBar Release 3 bietet für alle Produktionsphasen Werkzeuge an, durch die eine effektive und effiziente Erstellung von WBTs von der Idee bis zur Distribution möglich ist.
Stand noch vor fünf Jahren zu Beginn dieser Arbeit das Endprodukt bei der LernBar Entwicklung im Vordergrund, verlagerte sich durch den Einfluss dieser Dissertation der Schwerpunkt auf den gesamten Produktionsprozess. Unter Berücksichtigung der in diesem Zusammenhang entwickelten Prinzipien einer „Lean Media Production“, nehmen bspw. die Wirtschaftlichkeit und die starke Kundenorientierung während des Produktionsprozesses einen wichtigen Stellenwert ein. Dieser Ansatz ist eine neue Vorgehensweise im Bereich der Entwicklung von Autorensystemen, der seine Anerkennung und Professionalität durch die Ergebnisse des selbstentwickelten Evaluationsbogens sowie dem stetig wachsenden Einsatz in Schulen, Hochschulen und Unternehmen belegen kann.
In weiteren Forschungsarbeiten ist zu untersuchen, welche Lean Production Prinzipien zu verwenden oder anzupassen sind, wenn z. B. in größeren Teams oder mobil produziert wird. Des Weiteren sollte überprüft werden, inwieweit die Lernenden mit dem Endprodukt zufrieden sind und in ihrem Lernprozess unterstützt werden. Durch diese Forschungsarbeit wurde ein Beitrag dazu geleistet, die Lehre und Ausbildung zu optimieren, indem die Autoren/Lehrende in der Erstellung ihrer digitalen Lerninhalte im gesamten Prozess von aufeinander abgestimmten Werkzeugen unterstützt werden.
In the first part of the thesis we investigate Lyapunov exponents for general flat vector bundles over Riemann surfaces and we describe properties of Lyapunov exponents on special loci of the moduli space of flat vector bundles. In the second part of the thesis we show how the knowledge of Lyapunov exponent over a sporadic Teichmüller curve can be used to compute the algebraic equation of the associated universal family of curves.
Machine learning (ML) techniques have evolved rapidly in recent years and have shown impressive capabilities in feature extraction, pattern recognition, and causal inference. There has been an increasing attention to applying ML to medical applications, such as medical diagnosis, drug discovery, personalized medicine, and numerous other medical problems. ML-based methods have the advantage of processing vast amounts of data.
With an ever increasing amount of medical data collection and large, inter-subject variability in the medical data, automated data processing pipelines are very much desirable since it is laborious, expensive, and error-prone to rely solely on human processing. ML methods have the potential to uncover interesting patterns, unravel correlations between complex features, learn patient-specific representations, and make accurate predictions. Motivated by these promising aspects, in this thesis, I present studies where I have implemented deep neural networks for the early diagnosis of epilepsy based on electroencephalography (EEG) data and brain tumor detection based on magnetic resonance spectroscopy (MRS) data.
In the project for early diagnosis of epilepsy, we are dealing with one of the most common neurological disorders, epilepsy, which is characterized by recurrent unprovoked seizures. It can be triggered by a variety of initial brain injuries and manifests itself after a time window which is called the latent period. During this period, a cascade of structural and functional brain alterations takes place leading to an increased seizure susceptibility.
The development and extension of brain tissue capable of generating spontaneous seizures is defined as epileptogenesis (EPG).
Detecting the presence of EPG provides a precious opportunity for targeted early medical interventions and, thus, can slow down or even halt the disease progression. In order to study brain signals in this latent window, animal epilepsy models are used to provide valuable data as it is extremely difficult to obtain this data from human patients. The aim of this study is to discover biomarkers of EPG using animal models and then to find the equivalent and counterparts in human patients' data. However, the EEG features for EPG are not well-understood and there is not a sufficiently large amount of annotated data for ML-based algorithms. To approach this problem, firstly, I utilized the timestamp information of the recorded EEG from an animal epilepsy model where epilepsy is induced by an electrical stimulation. The timestamp serves as a form of weak supervision, i.e., before and after the stimulation. Secondly, I implemented a deep residual neural network and trained it with a binary classification task to distinguish the EEG signals from these two phases. After obtaining a high discriminative ability on the binary classification task, I proposed to divide further the time span after the stimulation for a three-class classification, aiming to detect possible stages of the progression of the latent EPG phase. I have shown that the model can distinguish EEG signals at different stages of EPG with high accuracy and generalization ability. I have also demonstrated that some of the learned features from the network are clinically relevant.
In the task of detecting brain tumors based on MRS data, I first proposed to apply a deep neural network on the MRS data collected from over 400 patients for a binary classification task. To combat the challenge of noisy labeling, I developed a distillation step to filter out relatively ``cleanly'' labeled samples. A mixing-based data augmentation method was also implemented to expand the size of the training set. All the experiments were designed to be conducted with a leave-patient-out scheme to ensure the generalization ability of the model. Averaged across all leave-patient-out cross-validation sets, the proposed method performed on par with human neuroradiologists, while outperforming other baseline methods. I have demonstrated the distillation effect on the MNIST data set with manually-introduced label noise as well as providing visualization of the input influences on the final classification through a class activation map method.
Moreover, I have proposed to aggregate information at the subject level, which could provide more information and insights. This is inspired by the concept of multiple instance learning, where instance-level labels are not required and which is more tolerant to noisy labeling. I have proposed to generate data bags consisting of instances from each patient and also proposed two modules to ensure permutation invariance, i.e., an attention module and a pooling module. I have compared the performance of the network in different cases, i.e., with and without permutation-invariant modules, with and without data augmentation, single-instance-based and multiple-instance-based learning and have shown that neural networks equipped with the proposed attention or pooling modules can outperform human experts.
Reactive oxygen species are a class of naturally occurring, highly reactive molecules that change the structure and function of macromolecules. This can often lead to irreversible intracellular damage. Conversely, they can also cause reversible changes through post-translational modification of proteins which are utilized in the cell for signaling. Most of these modifications occur on specific cysteines. Which structural and physicochemical features contribute to the sensitivity of cysteines to redox modification is currently unclear. Here, I investigated the in uence of protein structural and sequence features on the modifiability of proteins and specific cysteines therein using statistical and machine learning methods. I found several strong structural predictors for redox modification, such as a higher accessibility to the cytosol and a high number of positively charged amino acids in the close vicinity. I detected a high frequency of other post-translational modifications, such as phosphorylation and ubiquitination, near modified cysteines. Distribution of secondary structure elements appears to play a major role in the modifiability of proteins. Utilizing these features, I created models to predict the presence of redox modifiable cysteines in proteins, including human mitochondrial complex I, NKG2E natural killer cell receptors and proximal tubule cell proteins, and compared some of these predictions to earlier experimental results.
We calculate the Masur–Veech volume of the gothic locus G in the stratum H(23) of genus 4. Our method is based on the use of the formulae for the Euler characteristics of gothic Teichmu ̈ller curves to determine the number of lattice points of given area. We also use this method to recal- culate the Masur–Veech volumes of the Prym loci P3 ⊂ H(4) and P4 ⊂ H(6) in genus 3 and 4.
Diese Arbeit beschäftigt sich mit der theoriegeleiteten Entwicklung eines digitalen Werkzeugs namens MathCityMap (MCM) für das außerschulische Lehren und Lernen von Mathematik.
Den Ausgangspunkt des Projekts bilden die sogenannten Mathtrails. Dies sind Wanderpfade zum Entdecken mathematischer Sachverhalte an realen Objekten in der Umwelt. Eine didaktische, methodische sowie lernpsychologische Analyse konstatiert Mathtrails zahlreiche Potentiale für den Lernprozess wie beispielsweise die Möglichkeit, Primärerfahrungen zu sammeln, das Interesse am Fach Mathematik zu steigern sowie das Lernen aktiv und konstruktiv zu gestalten. Trotz der genannten Vorteile wird deutlich, dass die Vorbereitung und Umsetzung der mathematischen Wanderpfade mit einem immensen Aufwand verbunden sind. Eine weitere Herausforderung für Lernende liegt im offenen Charakter der Mathtrails, die in der Regel in autonomen Kleingruppen abgelaufen werden. Aus der Literatur ist bekannt, dass insbesondere für schwächere Lerner die Gefahr besteht, durch die Anforderungen einer selbstständigen Arbeitsweise überfordert zu werden.
Als Lösungsansatz für die zuvor genannten Probleme wird im Rahmen dieser Arbeit die Entwicklung eines digitalen Werkzeugs für Mathtrails erläutert. Die erste Forschungsfrage beschäftigt sich mit den theoretischen Anforderungen an solch ein Tool:
1. Welchen Anforderungen muss ein digitales Werkzeug genügen, um die Vorzüge der Mathtrails zu erhalten, deren Aufwand zu minimieren und die Gefahren zu kompensieren?
Unter Berücksichtigung der theoretischen Grundlagen digitaler Werkzeuge und des „Mobile Learnings“ werden zunächst Möglichkeiten identifiziert, den Vorbereitungsaufwand zu minimieren. Konkret erscheinen die automatische Datenverarbeitung, das digitale Zusammen-arbeiten sowie das Teilen und Wiederverwenden von digitalen Aufgaben und Trails als theoretisch zielführende Bestandteile von MCM. Weiterhin sollen zur Unterstützung der Lerner bei der eigenständigen Bearbeitung von Mathtrails didaktisch bewährte Konzepte – wie gestufte Hilfestellungen und Feedback – eingesetzt werden.
Vor dem Hintergrund der soeben formulierten Anforderungen bilden der Entwicklungsprozess sowie die Beschreibung des aktuellen Ist-Zustandes des MCM-Systems zentrale Bestand-teile dieser Arbeit. Das System setzt sich aus zwei Komponenten für jeweils unterschiedliche Zielgruppen zusammen: das MCM-Webportal zum Erstellen von Mathtrails und die MCM-App zum Ablaufen selbiger. Die Hauptziele von MCM können in der Minimierung des Vorbereitungsaufwands sowie der Kompensation einer Überforderungsgefahr gesehen werden.
In ersten Feldversuchen konnte MCM bereits in einem frühen Stadium erfolgreich mit Lernenden der Sekundarstufe I getestet werden. Gleichzeitig fiel jedoch auf, dass das implementierte Feedback-System Schwächen aufwies und von Lernenden zum systematischen Erraten von Lösungen genutzt werden konnte. In der Folge wurden Spielelemente (Gamification), denen nicht nur eine motivationssteigernde Wirkung nachgesagt wird, sondern auch das Potential das Verhalten zu beeinflussen, Bestandteil der MCM-App. Die zweite Forschungs-frage dieser Arbeit zielt auf die Auswirkungen der Gamification-Integration ab und lautet:
2. Welchen Einfluss haben Gamification-Elemente auf die Motivation sowie auf das Nutzungs-verhalten des digitalen Werkzeugs von Neuntklässlern bei der Bearbeitung eines Mathtrails?
Zur Beantwortung der zweiten Forschungsfrage wurde eine empirische Studie mit 16 Schulklassen (304 Schülerinnen und Schüler) der neunten Jahrgangsstufe im Sommer 2017 durch-geführt. Die Ergebnisse können wie folgt zusammengefasst werden: Die Implementierung einer Rangliste (Leaderboard) in die MCM-App führte zwar nicht zu einer höheren Motivation, jedoch spornte der Wettbewerb die Teilnehmer an, viele Aufgaben zu bearbeiten. Im Ver-gleich zu der Kontrollgruppe ohne Gamification-Elemente löste die Experimentalgruppe signifikant mehr Aufgaben, legte die doppelte Strecke zurück und nutzte das Feedbacks-System seltener aus, um Lösungen zu erraten. Die Studie konnte empirisch den gewünschten Einfluss von Spielelementen auf die Benutzung eines digitalen Werkzeugs für das außerschulische Lernen von Mathematik aufzeigen.
Die Evaluation der Ziele von MCM erfolgt indirekt über die Analyse der Verbreitung der Mathtrail-Idee ohne MCM und mit MCM. Die dritte Forschungsfrage lautet dementsprechend:
3. Welchen Beitrag hat das digitale Werkzeug zur Verbreitung der Mathtrail-Idee nach 4 Jahren Projektlaufzeit geleistet?
Zur Beantwortung der dritten Forschungsfrage werden wissenschaftliche Publikationen zu Mathtrails analysiert. Es wird insbesondere in Publikationen mit und ohne Stichwort „MathCityMap“ unterschieden, um eine Aussage über den Einfluss des MCM-Projekts auf den wissenschaftlichen Diskurs treffen zu können. Stand August 2020 enthält bereits jede dritte Mathtrail-Publikation einen Bezug zu MCM. Weiterhin wird ein Vergleich zu vorherigen, ähnlichen Bemühungen – gemeint sind Online-Mitmach-Projekte für Mathtrails – gezogen. So existierten im Zeitraum 2000 bis 2010 im anglo-amerikanischen Raum erste Webseiten für mathematische Wanderpfade. Diese boten zusammengenommen 131 Mathtrails an. Im Vergleich hierzu existieren bereits über 2.500 MCM-Mathtrails in 57 Ländern.
Sowohl die Publikationen als auch die Anzahl der erstellten Trails stellen erste Indizien dafür dar, dass mit MCM die Realisation eines theoretischen Konzepts für ein digitales Mathtrail-Werkzeug gelungen ist und die Idee der Mathtrails verbreitet werden konnte.
This thesis presents a first-of-its-kind phenomenological framework that formally describes the development of acquired epilepsy and the role of the neuro-immune axis in this development. Formulated as a system of nonlinear differential equations, the model describes the interaction of processes such as neuroinflammation, blood- brain barrier disruption, neuronal death, circuit remodeling, and epileptic seizures. The model allows for the simulation of epilepsy development courses caused by a variety of neurological injuries. The simulation results are in agreement with ex- perimental findings from three distinct animal models of epileptogenesis. Simula- tions capture injury-specific temporal patterns of seizure occurrence, neuroinflam- mation, blood-brain barrier leakage, and progression of neuronal death. In addition, the model provides insights into phenomena related to epileptogenesis such as the emergence of paradoxically long time scales of disease development after injury, the dose-dependence of epileptogenesis features on injury severity, and the variability of clinical outcomes in subjects exposed to identical injury. Moreover, the developed framework allows for the simulation of therapeutic interventions, which provides insights into the injury-specificity of prominent intervention strategies. Thus, the model can be used as an in silico tool for the generation of testable predictions, which may aid pre-clinical research for the development of epilepsy treatments.
Measuring information processing in neural data: The application of transfer entropy in neuroscience
(2017)
It is a common notion in neuroscience research that the brain and neural systems in general "perform computations" to generate their complex, everyday behavior (Schnitzer, 2002). Understanding these computations is thus an important step in understanding neural systems as a whole (Carandini, 2012;Clark, 2013; Schnitzer, 2002; de-Wit, 2016). It has been proposed that one way to analyze these computations is by quantifying basic information processing operations necessary for computation, namely the transfer, storage, and modification of information (Langton, 1990; Mitchell, 2011; Mitchell, 1993;Wibral, 2015). A framework for the analysis of these operations has been emerging (Lizier2010thesis), using measures from information theory (Shannon, 1948) to analyze computation in arbitrary information processing systems (e.g., Lizier, 2012b). Of these measures transfer entropy (TE) (Schreiber2000), a measure of information transfer, is the most widely used in neuroscience today (e.g., Vicente, 2011; Wibral, 2011; Gourevitch, 2007; Vakorin, 2010; Besserve, 2010; Lizier, 2011; Richter, 2016; Huang, 2015; Rivolta, 2015; Roux, 2013). Yet, despite this popularity, open theoretical and practical problems in the application of TE remain (e.g., Vicente, 2011; Wibral, 2014a). The present work addresses some of the most prominent of these methodological problems in three studies.
The first study presents an efficient implementation for the estimation of TE from non-stationary data. The statistical properties of non-stationary data are not invariant over time such that TE can not be easily estimated from these observations. Instead, necessary observations can be collected over an ensemble of data, i.e., observations of physical or temporal replications of the same process (Gomez-Herrero, 2010). The latter approach is computationally more demanding than the estimation from observations over time. The present study demonstrates how to handles this increased computational demand by presenting a highly-parallel implementation of the estimator using graphics processing units.
The second study addresses the problem of estimating bivariate TE from multivariate data. Neuroscience research often investigates interactions between more than two (sub-)systems. It is common to analyze these interactions by iteratively estimating TE between pairs of variables, because a fully multivariate approach to TE-estimation is computationally intractable (Lizier, 2012a; Das, 2008; Welch, 1982). Yet, the estimation of bivariate TE from multivariate data may yield spurious, false-positive results (Lizier, 2012a;Kaminski, 2001; Blinowska, 2004). The present study proposes that such spurious links can be identified by characteristic coupling-motifs and the timings of their information transfer delays in networks of bivariate TE-estimates. The study presents a graph-algorithm that detects these coupling motifs and marks potentially spurious links. The algorithm thus partially corrects for spurious results due to multivariate effects and yields a more conservative approximation of the true network of multivariate information transfer.
The third study investigates the TE between pre-frontal and primary visual cortical areas of two ferrets under different levels of anesthesia. Additionally, the study investigates local information processing in source and target of the TE by estimating information storage (Lizier, 2012) and signal entropy. Results of this study indicate an alternative explanation for the commonly observed reduction in TE under anesthesia (Imas, 2005; Ku, 2011; Lee, 2013; Jordan, 2013; Untergehrer, 2014), which is often explained by changes in the underlying coupling between areas. Instead, the present study proposes that reduced TE may be due to a reduction in information generation measured by signal entropy in the source of TE. The study thus demonstrates how interpreting changes in TE as evidence for changes in causal coupling may lead to erroneous conclusions. The study further discusses current bast-practice in the estimation of TE, namely the use of state-of-the-art estimators over approximative methods and the use of optimization procedures for estimation parameters over the use of ad-hoc choices. It is demonstrated how not following this best-practice may lead to over- or under-estimation of TE or failure to detect TE altogether.
In summary, the present work proposes an implementation for the efficient estimation of TE from non-stationary data, it presents a correction for spurious effects in bivariate TE-estimation from multivariate data, and it presents current best-practice in the estimation and interpretation of TE. Taken together, the work presents solutions to some of the most pressing problems of the estimation of TE in neuroscience, improving the robust estimation of TE as a measure of information transfer in neural systems.
Biologische Signalwege bilden komplexe Netzwerke aus, um die Zellantwort sensibel regulieren zu können. Systembiologische Ansätze werden eingesetzt, um biologische Systeme anhand von Computer-gestützten Modellen zu untersuchen. Ein mathematisches Modell erlaubt, neben der logischen Erfassung der Regulation des biologischen Systems, die systemweite Simulation des dynamischen Verhaltens und Analyse der Robustheit und Anfälligkeit.
Der TNFR1-vermittelte Signalweg reguliert essenzielle Zellvorgänge wie Entzündungsantworten,
Proliferation und Zelltod. TNFR1 wird von dem Zytokin TNF-α stimuliert und fördert daraufhin die Bildung verschiedener makromolekularer Komplexe, welche unterschiedliche Zellantworten einleiten, von der Aktivierung des Transkriptionsfaktors NF-κB, welcher die Expression von proliferationsfördernden Genen reguliert, bis zu zwei Formen des Zelltods, der Apoptose und der Nekroptose. Die Regulation der verschiedenen Zellantworten wird auch als molekularer Schalter bezeichnet. Die exakten molekularen Vorgänge, welche die Zellantwort modulieren, sind noch nicht vollständig entschlüsselt. Eine Fehlregulation des Signalwegs kann chronische Entzündungen hervorrufen oder die Entstehung von Tumoren fördern.
In dieser Thesis haben wir die neuesten Erkenntnisse der Forschung des TNFR1-Signalwegs anhand von umfangreichen Interaktionsdaten aus der Literatur erstmals in einem Petrinetz-Modell erfasst und analysiert. Das manuell kuratierte Modell umfasst die sequenziellen Prozesse der NF-κB-Aktivierung, Apoptose und Nekroptose und berücksichtigt den Einfluss posttranslationaler Modifikationen.
Weiterhin wurden Analysemethoden für Signalwegs-Modelle entwickelt, welche die spezifischen Anforderungen dieser biologischen Systeme berücksichtigen und eine biologisch motivierte Netzwerkanalyse ermöglichen. Die Manatee-Invarianten identifizieren Signalflüsse im Gleichgewichtszustand in Modellen, die Zyklen aufweisen, und werden als Linearkombination von Transitions-Invarianten gebildet. Diese Signalflüsse erfassen idealerweise einen Prozess von der Rezeptorstimulation zur Zellantwort in einem Modell eines Signalwegs. Die Bestimmung aller möglichen Signalflüsse in Modellen von Signalwegen ist eine notwendige Voraussetzung für weitere biologisch motivierte Analysen, wie die in silico-Knockout Analyse. Wir haben ebenfalls ein neues Konzept zur Untersuchung von in silico-Knockouts vorgestellt. Die Effekte der in silico-Knockouts auf einzelne Komplexe und Prozesse des Signalwegs werden in der in silico-Knockout-Matrix repräsentiert. Wir haben die Software-Anwendung isiKnock entwickelt, welche beide Konzepte kombiniert und eine systematische Knockout-Analyse von Petrinetz-Modellen unterstützt.
Das Petrinetz-Modell des TNFR1-Signalwegs wurde auf seine elementaren Eigenschaften geprüft und die etablierten Analysen wie Platz-Invarianten und Transitions-Invarianten durchgeführt. Hierbei konnten die Transitions-Invarianten nicht in allen Fällen komplette biologische Signalflüsse beschreiben. Wir haben ebenfalls die neu vorgestellten Methoden auf das Petrinetz-Modell angewandt. Anhand der Manatee-Invarianten konnten wir die zusammenhängenden Signalflüsse identifizieren und nach ihrem biologischen Ausgang klassifizieren sowie die Auswirkungen der Rückkopplungen untersuchen. Wir konnten zeigen, dass die survival-Antwort durch die Aktivierung von NF-κB am häufigsten auftritt, danach die Apoptose, gefolgt von der Nekroptose. Die alternativen Signalflüsse in Form der Manatee-Invarianten spiegeln die Robustheit des biologischen Systems wider. Wir führten eine ausgiebige in silico-Knockout-Analyse basierend auf den Manatee-Invarianten durch, um die Proteine des Signalwegs nach ihrem Einfluss einzustufen und zu gruppieren. Die Proteine des Komplex I wiesen hierbei den größten Einfluss auf, angeführt von der Rezeptorstimulation und RIP1. Wir betrachteten und diskutierten die Regulation des molekularen Schalters anhand der Knockout-Analyse von selektierten Proteinen und deren Auswirkung auf wichtige Komplexe im Modell. Wir identifizierten die Ubiquitinierung in Komplex I sowie die NF-κB-abhängige Genexpression als die wichtigen Kontrollpunkte des TNFR1-Signalwegs. In Komplex II ist die Regulation der Aktivierung der Caspase-Aktivität entscheidend.
Die umfangreiche Netzwerkanalyse basierend auf Manatee-Invarianten und systematischer in silico-Knockout-Analyse verifizierte das Petrinetz-Modell und erlaubte die Untersuchung der Robustheit und Anfälligkeit des Systems. Die neu entwickelten Methoden ermöglichen eine fundierte, biologisch relevante Untersuchung von in silico-Modellen von Signalwegen. Der systembiologische Ansatz unterstützt die Aufklärung der Regulation und Funktion des verflochtenen Netzwerks des TNFR1-Signalwegs.