J. Computer Applications
Refine
Document Type
- Doctoral Thesis (11)
- Report (2)
- Article (1)
Has Fulltext
- yes (14)
Is part of the Bibliography
- no (14)
Keywords
- Ambient Intelligence (2)
- Elektrotechnik (2)
- Szenario (2)
- Adjoint method (1)
- Azidifizierung (1)
- B-Spline (1)
- BMP1 MAP-Kinase (1)
- Botrytis cinerea (1)
- Botrytis fabae (1)
- Data Analysis (1)
Faculty / Organisational entity
Data-driven and Sparse-to-Dense Concepts in Scene Flow Estimation for Automotive Applications
(2022)
Highly assisted driving and autonomous vehicles require a detailed and accurate perception of the environment. This includes the perception of the 3D geometry of the scene and the 3D motion of other road users. The estimation of both based on images is known as the scene flow problem in computer vision. This thesis deals with a solution to the scene flow problem that is suitable for application in autonomous vehicles. This application imposes strict requirements on accuracy, robustness, and speed. Previous work was lagging behind in at least one of these metrics. To work towards the fulfillment of those requirements, the sparse-to-dense concept for scene flow estimation is introduced in this thesis. The idea can be summarized as follows: First, scene flow is estimated for some points of the scene for which this can be done comparatively easily and reliably. Then, an interpolation is performed to obtain a dense estimate for the entire scene. Because of the separation into two steps, each part can be optimized individually. In a series of experiments, it is shown that the proposed methods achieve competitive results and are preferable to previous techniques in some aspects. As a second contribution, individual components in the sparse-to-dense pipeline are replaced by deep learning modules. These are a highly localized and highly accurate feature descriptor to represent pixels for dense matching, and a network for robust and generic sparse-to-dense interpolation. Compared to end-to-end architectures, the advantage of deep modules is that they can be trained more effciently with data from different domains. The recombination approach applies a similar concept as the sparse-to-dense approach by solving and combining less diffcult, auxiliary sub-problems. 3D geometry and 2D motion are estimated separately, the individual results are combined, and then also interpolated into a dense scene flow. As a final contribution, the thesis proposes a set of monolithic end-to-end networks for scene flow estimation.
Recommender systems recommend items (e.g., movies, products, books) to users. In this thesis, we proposed two comprehensive and cluster-induced recommendation-based methods: Orthogonal Inductive Matrix Completion (OMIC) and Burst-induced Multi-armed Bandit (BMAB). Given the presence of side information, the first method is categorized as context-aware. OMIC is the first matrix completion method to approach the problem of incorporating biases, side information terms and a pure low-rank term into a single flexible framework with a well-principled optimization procedure. The second method, BMAB, is context-free. That is, it does not require any side data about users or items. Unlike previous context-free multi-armed bandit approaches, our method considers the temporal dynamics of human communication on the web and treats the problem in a continuous time setting. We built our models' assumptions under solid theoretical foundations. For OMIC, we provided theoretical guarantees in the form of generalization bounds by considering the distribution-free case: no assumptions about the sampling distribution are made. Additionally, we conducted a theoretical analysis of community side information when the sampling distribution is known and an adjusted nuclear norm regularization is applied. We showed that our method requires just a few entries to accurately recover the ratings matrix if the structure of the ground truth closely matches the cluster side information. For BMAB, we provided regret guarantees under mild conditions that demonstrate how the system's stability affects the expected reward. Furthermore, we conducted extensive experiments to validate our proposed methodologies. In a controlled environment, we implemented synthetic data generation techniques capable of replicating the domains for which OMIC and BMAB were designed. As a result, we were able to analyze our algorithms' performance across a broad spectrum of ground truth regimes. Finally, we replicated a real-world scenario by utilizing well-established recommender datasets. After comparing our approaches to several baselines, we observe that they achieved state-of-the-art results in terms of accuracy. Apart from being highly accurate, these methods improve interpretability by describing and quantifying features of the datasets they characterize.
In the past, information and knowledge dissemination was relegated to the
brick-and-mortar classrooms, newspapers, radio, and television. As these
processes were simple and centralized, the models behind them were well
understood and so were the empirical methods for optimizing them. In today’s
world, the internet and social media has become a powerful tool for information
and knowledge dissemination: Wikipedia gets more than 1 million edits per day,
Stack Overflow has more than 17 million questions, 25% of US population visits
Yahoo! News for articles and discussions, Twitter has more than 60 million
active monthly users, and Duolingo has 25 million users learning languages
online. These developments have introduced a paradigm shift in the process of
dissemination. Not only has the nature of the task moved from being centralized
to decentralized, but the developments have also blurred the boundary between
the creator and the consumer of the content, i.e., information and knowledge.
These changes have made it necessary to develop new models, which are better
suited to understanding and analysing the dissemination, and to develop new
methods to optimize them.
At a broad level, we can view the participation of users in the process of
dissemination as falling in one of two settings: collaborative or competitive.
In the collaborative setting, the participants work together in crafting
knowledge online, e.g., by asking questions and contributing answers, or by
discussing news or opinion pieces. In contrast, as competitors, they vie for
the attention of their followers on social media. This thesis investigates both
these settings.
The first part of the thesis focuses on the understanding and analysis of
content being created online collaboratively. To this end, I propose models for
understanding the complexity of the content of collaborative online discussions
by looking exclusively at the signals of agreement and disagreement expressed
by the crowd. This leads to a formal notion of complexity of opinions and
online discussions. Next, I turn my attention to the participants of the crowd,
i.e., the creators and consumers themselves, and propose an intuitive model for
both, the evolution of their expertise and the value of the content they
collaboratively contribute and learn from on online Q&A based forums. The
second part of the thesis explores the competitive setting. It provides methods
to help the creators gain more attention from their followers on social media.
In particular, I consider the problem of controlling the timing of the posts of
users with the aim of maximizing the attention that their posts receive under
the idealized setting of full-knowledge of timing of posts of others. To solve
it, I develop a general reinforcement learning based method which is shown to
have good performance on the when-to-post problem and which can be employed in
many other settings as well, e.g., determining the reviewing times for spaced
repetition which lead to optimal learning. The last part of the thesis looks at
methods for relaxing the idealized assumption of full knowledge. This basic
question of determining the visibility of one’s posts on the followers’ feeds
becomes difficult to answer on the internet when constantly observing the feeds
of all the followers becomes unscalable. I explore the links of this problem to
the well-studied problem of web-crawling to update a search engine’s index and
provide algorithms with performance guarantees for feed observation policies
which minimize the error in the estimate of visibility of one’s posts.
This work presents a visual analytics-driven workflow for an interpretable and understandable machine learning model. The model is driven by a reverse
engineering task in automotive assembly processes. The model aims
to predict the assembly parameters leading to the given displacement field
on the geometries surface. The derived model can work on both measurement
and simulation data. The proposed approach is driven by the scientific
goals from visual analytics and interpretable artificial intelligence alike. First, a concept for systematic uncertainty monitoring, an object-oriented, virtual reference scheme (OOVRS), is developed. Afterward, the prediction task is solved via a regressive machine learning model using adversarial neural networks.
A profound model parameter study is conducted and assisted with an interactive visual analytics pipeline. Further, the effects of the learned
variance in displacement fields are analyzed in detail. Therefore a visual analytics pipeline is developed, resulting in a sensitivity benchmarking tool. This allows the testing of various segmentation approaches to lower the machine learning input dimensions. The effects of the assembly parameters are
investigated in domain space to find a suitable segmentation of the training
data set’s geometry. Therefore, a sensitivity matrix visualization is developed. Further, it is shown how this concept could directly compare results
from various segmentation methods, e.g., topological segmentation, concerning the assembly parameters and their impact on the displacement field variance. The resulting databases are still of substantial size for complex simulations with large and high-dimensional parameter spaces. Finally, the applicability of video compression techniques towards compressing visualization image databases is studied.
In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.
Robuste Optimierung wird zur Entscheidungsunterstützung eines komplexen Beschaffungs- und Transportmodells genutzt, um die Risikoeinstellung der Entscheidenden abzubilden und gleichzeitig ein robustes Ergebnis zu erzielen. Die Modellierung des Problems ist umfassend dargestellt und Ergebnisse der nicht-deterministischen Planung bei verschiedenen Parametern und Risikoeinstellungen gegenübergestellt. Die Datenunsicherheit wird an einem Praxisfall erläutert und Methoden und -empfehlungen zum Umgang mit dieser dargestellt.
The neural networks have been extensively used for tasks based on image sensors. These models have, in the past decade, consistently performed better than other machine learning methods on tasks of computer vision. It is understood that methods for transfer learning from neural networks trained on large datasets can reduce the total data requirement while training new neural network models. These methods tend not to perform well when the data recording sensor or the recording environment is unique from the existing large datasets. The machine learning literature provides various methods for prior-information inclusion in a learning model. Such methods employ methods like designing biases into the data representation vectors, enforcing priors or physical constraints on the models. Including such information into neural networks for the image frames and image-sequence classification is hard because of the very high dimensional neural network mapping function and little information about the relation between the neural network parameters. In this thesis, we introduce methods for evaluating the statistically learned data representation and combining these information descriptors. We have introduced methods for including information into neural networks. In a series of experiments, we have demonstrated methods for adding the existing model or task information to neural networks. This is done by 1) Adding architectural constraints based on the physical shape information of the input data, 2) including weight priors on neural networks by training them to mimic statistical and physical properties of the data (hand shapes), and 3) by including the knowledge about the classes involved in the classification tasks to modify the neural network outputs. These methods are demonstrated, and their positive influence on the hand shape and hand gesture classification tasks are reported. This thesis also proposes methods for combination of statistical and physical models with parametrized learning models and show improved performances with constant data size. Eventually, these proposals are tied together to develop an in-car hand-shape and hand-gesture classifier based on a Time of Flight sensor.
Botrytis cinerea, der Erreger der Graufäule, infiziert hunderte verschiedene Pflanzenspezies und verursacht weltweit enorme landwirtschaftliche Verluste. Dabei tötet er das Wirtsgewebe sehr schnell mithilfe lytischer Enzyme und Nekrose-induzierender Metaboliten und Proteine ab. Das Signal-Mucin Msb2 ist in B. cinerea, wie in anderen pathogenen Pilzen, wichtig für die Oberflächenerkennung, Differenzierung von Appressorien und die Penetration des Pflanzengewebes. Msb2 agiert oberhalb der BMP1 Pathogenitäts-MAPK-Kaskade. In dieser Studie konnte eine direkte Interaktion zwischen Msb2 und BMP1, sowie zwischen den beiden Sensorproteinen Msb2 und Sho1 nachgewiesen werden. Dennoch führte die Deletion von sho1 lediglich zu geringfügigen Defekten im Wachstum, der Hyphendifferenzierung und der Bildung von Infektionsstrukturen. Sho1 zeigte nur einen geringen Einfluss auf die Aktivierung von BMP1. Das Fehlen von Sho1 verursachte keine Virulenzdefekte, während der Doppel-KO von msb2 und sho1 zu einer stärkeren Reduzierung der Läsionsausbreitung im Vergleich zu msb2 Mutanten führte. Es wurden mehrere keimungsregulierte, BMP1 abhängige Gene deletiert und die Mutanten phänotypisch charakterisiert. Keines der Gene für lytische Enzyme oder putative Effektorproteine beeinflusste die Virulenz. Mutanten, denen das für ein Ankyrin-repeat Protein codierende arp1 Gen fehlt, zeigten eine gestörte Oberflächenerkennung, gravierende Wachstumsdefekte und reduzierte Virulenz.
B. cinerea VELVET-Mutanten sind in der lichtabhängigen Differenzierung und der Ausbreitung nekrotischer Läsionen beeinträchtigt. In dieser Arbeit ermöglichte die Charakterisierung mehrerer Mutanten ein besseres Verständnis der molekularen Vorgänge, aufgrund derer der VELVET-Komplex die Entwicklung und Pathogenese in B. cinerea reguliert. Quantitative Vergleiche der in planta Transkriptome und Sekretome führten zur Identifizierung eines für drei VELVET-Mutanten gemeinsamen Sets an herunterregulierten Genen, welche für CAZymes, Proteasen und Virulenz-assoziierte Proteine codieren. Die meisten dieser Gene wurden zusätzlich im Wildtyp während der Infektion verstärkt exprimiert, was zusätzliche Hinweise auf deren Relevanz im Infektionsprozess lieferte. Die drastisch verringerte Expression von Genen für Proteasen konnte mit niedrigerer Proteaseaktivität und der unvollständigen Mazeration des Gewebes an der Infektionsstelle in Verbindung gebracht werden. Der neu etablierte quantitative Sekretom-Vergleich des Wildtyps und der VELVET-Mutanten mithilfe 15N-markierter Proteine korrelierte eindeutig mit den Transkriptomdaten sekretierter Proteine. Damit wurde gezeigt, dass die Abundanz der Proteine maßgeblich von deren mRNA-Level abhängt. Die Unfähigkeit zur Ansäuerung des Wirtsgewebes ist einer der interessantesten phänotypischen Aspekte der VELVET-Mutanten. Während Citrat die dominierende von B. cinerea ausgeschiedene Säure ist, sekretierten VELVET-Mutanten deutlich weniger Citrat. Weder für Oxalat noch für Gluconat konnte eine wichtige Rolle während der Infektion bestätigt werden. Die Läsionsausbreitung der Mutanten wurde sowohl durch Zugabe von Vollmedium, als auch durch künstlich konstant niedrig eingestellte pH-Werte an den Infektionsstellen gefördert, während die Einstellung auf neutrale pH-Werte die Expansion beim B. cinerea Wildtyp stark beeinträchtigte. Damit ist die Ansäuerung in Tomatenblättern ein wichtiger Virulenzmechanismus, der möglicherweise essentiell für die Aktivität der sekretierten Proteine ist.
Überraschenderweise scheint eine Ansäuerung des Gewebes für die erfolgreiche Infektion der Ackerbohne Vicia faba nicht notwendig zu sein. Weder B. cinerea noch der am nächsten verwandte Botrytis fabae, welcher sich als Spezialist auf V. faba aggressiver verhält, zeigten während der erfolgreichen Infektion eine Ansäuerung des Ackerbohnenblattgewebes. B. fabae ist auf wenige Wirtspflanzen der Fabaceae begrenzt. Die Grundlagen der Wirtsspezifität sind bisher unklar. Vergleichende Transkriptom- und Sekretom-Analysen ergaben Hinweise für die molekularen Ursachen der unterschiedlichen Wirtsspektren von B. cinerea und B. fabae. In dieser Arbeit konnte die schlechte Infektion durch B. fabae auf Tomatenblättern mit einer deutlich niedrigeren Proteaseaktivität in Verbindung gebracht werden, während artifiziell konstant niedrige pH-Werte die Läsionsausbreitung kaum förderten. Im Gegensatz zur Infektion von Tomatenblättern wurden jedoch auf V. faba insgesamt deutlich niedrigere Proteaseaktivitäten in den Sekretomen beider Spezies gemessen. Diese Daten weisen darauf hin, dass die beiden Spezies nicht nur generell unterschiedliche Infektionsstrategien anwenden, sondern dass die Virulenzmechanismen zusätzlich vom infizierten Pflanzengewebe abhängig sind.
Temporal Data Management and Incremental Data Recomputation with Wide-column Stores and MapReduce
(2017)
In recent years, ”Big Data” has become an important topic in academia
and industry. To handle the challenges and problems caused by Big Data,
new types of data storage systems called ”NoSQL stores” (means ”Not-only-
SQL”) have emerged.
”Wide-column stores” are one kind of NoSQL stores. Compared to relational database systems, wide-column stores introduce a new data model,
new IRUD (Insert, Retrieve, Update and Delete) semantics with support for
schema-flexibility, single-row transactions and data expiration constraints.
Moreover, each column stores multiple data versions with associated time-
stamps. Well-known examples are Google’s ”Big-table” and its open sourced
counterpart ”HBase”. Recently, such systems are increasingly used in business intelligence and data warehouse environments to provide decision support, controlling and revision capabilities.
Besides managing the current values, data warehouses also require management and processing of historical, time-related data. Data warehouses
frequently employ techniques for processing changes in various data sources
and incrementally applying such changes to the warehouse to keep it up-to-
date. Although both incremental data warehousing maintenance and temporal data management have been the subject of intensive research in the
relational database and finally commercial database products have picked up
the ability for temporal data processing and management, such capabilities
have not been explored systematically for today’s wide-column stores.
This thesis helps to address the shortcomings mentioned above. It care-
fully analyzes the properties of wide-column stores and the applicability
of mechanisms for temporal data management and incremental data ware-
house maintenance known from relational databases, extends well-known approaches and develops new capabilities for providing equivalent support in
wide-column stores.
In this thesis we develop a shape optimization framework for isogeometric analysis in the optimize first–discretize then setting. For the discretization we use
isogeometric analysis (iga) to solve the state equation, and search optimal designs in a space of admissible b-spline or nurbs combinations. Thus a quite
general class of functions for representing optimal shapes is available. For the
gradient-descent method, the shape derivatives indicate both stopping criteria and search directions and are determined isogeometrically. The numerical treatment requires solvers for partial differential equations and optimization methods, which introduces numerical errors. The tight connection between iga and geometry representation offers new ways of refining the geometry and analysis discretization by the same means. Therefore, our main concern is to develop the optimize first framework for isogeometric shape optimization as ground work for both implementation and an error analysis. Numerical examples show that this ansatz is practical and case studies indicate that it allows local refinement.
