Labeling Points of Interest in Dynamic Maps using Disk Labels

Dynamic maps which support panning, rotating and zooming are available on every smartphone today. To label geographic features on these maps such that the user is presented with a consistent map view even on map interaction is a challenge. We are presenting a map labeling scheme, which allows to label maps at an interactive speed. For any possible map rotation the computed labeling remains free of intersections between labels. It is not required to remove labels from the map view to ensure this. The labeling scheme supports map panning and continuous zooming. During zooming a label appears and disappears only once. When zooming out of the map a label disappears only if it may overlap an equally or more important label in an arbitrary map rotation. This guarantees that more important labels are preferred to less important labels on small scale maps. We are presenting some extensions to the labeling that could be used for more sophisticated labeling features such as area labels turning into point labels at smaller map scales. The proposed labeling scheme relies on a preprocessing phase. In this phase for each label the map scale where it is removed from the map view is computed. During the phase of map presentation the precomputed label set must only be filtered, what can be done very fast. We are presenting some hints that allow to efficiently compute the labeling in the preprocessing phase. Using these a labeling of about 11 million labels can be computed in less than 20 minutes. We are also presenting a datastructure to efficiently filter the precomputed label set in the interaction phase

Unsupervised Structural Embedding Methods for Efficient Collective Network Mining

How can we align accounts of the same user across social networks? Can we identify the professional role of an email user from their patterns of communication? Can we predict the medical effects of chemical compounds from their atomic network structure? Many problems in graph data mining, including all of the above, are defined on multiple networks. The central element to all of these problems is cross-network comparison, whether at the level of individual nodes or entities in the network or at the level of entire networks themselves. To perform this comparison meaningfully, we must describe the entities in each network expressively in terms of patterns that generalize across the networks. Moreover, because the networks in question are often very large, our techniques must be computationally efficient. In this thesis, we propose scalable unsupervised methods that embed nodes in vector space by mapping nodes with similar structural roles in their respective networks, even if they come from different networks, to similar parts of the embedding space. We perform network alignment by matching nodes across two or more networks based on the similarity of their embeddings, and refine this process by reinforcing the consistency of each node’s alignment with those of its neighbors. By characterizing the distribution of node embeddings in a graph, we develop graph-level feature vectors that are highly effective for graph classification. With principled sparsification and randomized approximation techniques, we make all our methods computationally efficient and able to scale to graphs with millions of nodes or edges. We demonstrate the effectiveness of structural node embeddings on industry-scale applications, and propose an extensive set of embedding evaluation techniques that lay the groundwork for further methodological development and application.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162895/1/mheimann_1.pd

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Proceedings of SAT Competition 2020 : Solver and Benchmark Descriptions

Non peer reviewe

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Efficient tuning in supervised machine learning

The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the training can become very time-consuming for large datasets. For this reason efficient tuning methods are required, which are able to improve the predictions of the learning algorithms. In this thesis we incorporate model-assisted optimization techniques, for performing efficient optimization on noisy datasets with very limited budgets. Under this umbrella we also combine learning algorithms with methods for feature construction and selection. We propose to integrate a variety of elements into the learning process. E.g., can tuning be helpful in learning tasks like time series regression using state-of-the-art machine learning algorithms? Are statistical methods capable to reduce noise e ffects? Can surrogate models like Kriging learn a reasonable mapping of the parameter landscape to the quality measures, or are they deteriorated by disturbing factors? Summarizing all these parts, we analyze if superior learning algorithms can be created, with a special focus on efficient runtimes. Besides the advantages of systematic tuning approaches, we also highlight possible obstacles and issues of tuning. Di fferent tuning methods are compared and the impact of their features are exposed. It is a goal of this work to give users insights into applying state-of-the-art learning algorithms profitably in practiceBundesministerium f ür Bildung und Forschung (Germany), Cologne University of Applied Sciences (Germany), Kind-Steinm uller-Stiftung (Gummersbach, Germany)Algorithms and the Foundations of Software technolog

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system