- informatik-vortraege - lists.rwth-aachen.de

Einladung: Informatik-Oberseminar Vipin Ravindran Vijayalakshmi
by Bolke-Hermanns, Helene 24 Aug '21

24 Aug '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 6. September 2021, 10:00 Uhr Zoom: https://rwth.zoom.us/j/91549249969?pwd=K1VGbDhkaUlpUDdhSFR3dXh1aWFzQT09 Meeting ID: 915 4924 9969 Passcode: 391915 Referent: Vipin Ravindran Vijayalakshmi, M. Sc. Lehrstuhl für Management Science Thema: Selfishness in Strategic Resource Allocation Problems Abstract: Non-cooperative game theory has emerged as an essential tool in analyzing and predicting the outcome of decentralized systems. Various algorithmic aspects arising due to strategic behavior among multiple non-cooperative users in several classes of resource allocation problems can be studied using a game theoretic approach. In this talk, we consider congestion games which are often used to model various scenarios of resource allocation by non-cooperative users. Congestion games constitute of a class of games in which a pure Nash equilibrium always exists. The hardness of computing a pure Nash equilibrium in congestion games has been of significant interest in the scientific community over the past two decades. As computing an exact pure Nash equilibrium is known to be hard, we study a weaker notion of pure Nash equilibrium called an approximate pure Nash equilibrium and to that extend, analyze an efficient algorithm that computes approximate pure Nash equilibria in congestion games with an improved approximation guarantee. In spite of being the predominant class of games to model resource allocation problems involving different users, congestion games lack an element of time dependence, especially in certain application scenarios such as the road transportation network. It is quite unnatural to assume that all users of a road section experience the same congestion. We consider a scheduling game on parallel machines, in which jobs try to minimize their completion time by choosing a machine to be processed on. Each machine uses an individual priority list to decide on the order according to which the jobs on the machine are processed. Here, we study the existence of a pure Nash equilibrium and characterize classes of instances in which a pure Nash equilibrium is guaranteed to exist. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Peter Lindner
by Peter Lindner 15 Aug '21

15 Aug '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 24. August 2021, 10:00 Uhr Zoom: https://rwth.zoom.us/j/97621241861?pwd=TURveUs0YVRWck5XRWprNEJCdFJmZz09 Meeting ID: 976 2124 1861 Passcode: 592961 Referent: Peter Lindner, M. Sc. Lehrstuhl für Informatik 7 (Logik und Theorie diskreter Systeme) Thema: The Theory of Infinite Probabilistic Databases Abstract: Probabilistic (relational) databases extend the conventional relational database model by probability distributions over database instances. Such models are desirable for, and applicable to, a wide range of scenarios where data is subject to uncertainty. For a long time, the theoretical state of the art was to view a probabilistic database as a discrete probability space with only finitely many possible outcome instances. In this talk, we give an overview over our contributions regarding the extension of this model, along with various key concepts, to infinite probability spaces. This is required to support basic probabilistic methods, like continuous noise models and tackles default shortcomings of the restriction to discrete spaces. To this end, we present and explore a suitable unifying framework for infinite probabilistic databases that is based on point processes. This covers all applications of interest, and complies with the desired requirements for such a framework. It's inherent abstractness allows us to discuss central notions from the theory of probabilistic databases like query semantics and independence assumptions from a plain probability theoretic point of view. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberserminar Francis Engelmann
by Francis Engelmann 06 Aug '21

06 Aug '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 16. August 2021, 14.00 Uhr Ort: Zoom Videokonferenz https://rwth.zoom.us/j/97522924662?pwd=NWN1Z2lkWGJOTkt0N2QyTHBRdSs1Zz09 <https://www.google.com/url?q=https://rwth.zoom.us/j/97522924662?pwd%3DNWN1Z…> Meeting ID: 975 2292 4662 Passcode: 451032 Referent: Francis Engelmann, M.Sc. Lehrstuhl Informatik 13 Thema: 3D Scene Understanding on Point Clouds Abstract: In this talk I present my thesis contributions to the emerging field of 3D scene understanding. That is, given a 3D scene representation as input, we address tasks such as 3D object detection, shape reconstruction and pose estimation, as well as 3D semantic- and instance-segmentation. The recent availability of inexpensive depth sensors has made 3D data widely accessible. At the same time, current aspirations in the field of robotics, augmented reality and self-driving cars require efficient and reliable algorithms for understanding different 3D scene representations, such as polygon meshes, point clouds or volumetric structures. While 3D data overcomes inherent limitations of projected 2D views, such as occlusions, scale-ambiguity and lack of geometry, it also introduces new challenges including sparsity and non-uniform sampling. Therefore, existing methods for 2D image processing do not generalize well to 3D data structures. In this talk, we present novel approaches specific to 3D scene understanding. The main contributions are organized into three parts: The core contribution of the first part is a probabilistic formulation which integrates 3D shape and motion priors as well as stereo depth measurements into a global optimization problem. The resulting approach can jointly estimate the 3D shape, pose and motion of multiple vehicles in urban street scenes. The second part deals with new deep learning models for processing 3D point clouds. In particular, we propose sequential and recurrent consolidation units for increasing the spatial context of point networks, and a simple yet efficient dilation mechanism for increasing the receptive field size of deep point convolutional networks. Finally, in the third part, we introduce advanced deep learning models. For semantic segmentation, we present the combination of two types of convolutions operating jointly on point clouds and mesh surfaces. In instance segmentation, we propose a new paradigm combining bottom-up and top-down approaches as introduced in previous works. The talk concludes with a discussion and directions for future research.

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UnRAVeL "Behind the Scenes" Survey Lecture
by Tim Seppelt 26 Jul '21

26 Jul '21

Dear all, part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of „randomness“ and „uncertainty“ in UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is delivered by one of the researchers involved in UnRAVeL. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. This year, 12 UnRAVeL professors will answer the following questions, based on one of their recent scientific results: * How did you get to this result? * How did you come up with certain key ideas? * How did you cope with obstacles on the way? Which ideas you had did not work out? Following these talks, PhD students will give an informal summary of their doctoral studies within UnRAVeL. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2021 and engage in discussions with researchers and doctoral students. Details information can be found on https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-… All events take place on *Thursdays from 16:30 to 18:00 on Zoom* https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 * 15/04/2021 Survey Lecture: Erika Ábrahám: Probabilistic Hyperproperties * 22/04/2021 Jürgen Giesl: Inferring Expected Runtimes of Probabilistic Programs * 29/04/2021 Erich Grädel: Hidden Variables in Quantum Mechanics and Logics of Dependence and Independence * 06/05/2021 Christof Löding: Learning Automata for Infinite Words * 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks * 10/06/2021 Britta Peis: Sensitivity Analysis for Submodular Function Optimization with Applications in Algorithmic Game Theory * 17/06/2021 Nils Nießen: Optimised Maintenance of Railway Infrastructure * 24/06/2021 Gerhard Lakemeyer: Uncertainty in Robotics * 01/07/2021 Joost-Pieter Katoen: The Surprises of Probabilistic Termination * 08/07/2021 Christina Büsing: Robust Minimum Cost Flow Problem Under Consistent Flow Constraints * 15/07/2021 Ringvorlesung: Gerhard Woeginger: Bilevel optimization * 22/07/2021 Ulrike Meyer: Malware Detection We are looking forward to seeing you at the lectures. Best regards, Tim Seppelt for the organisation committee https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…

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Einladung: Informatik-Oberseminar Joachim Protze
by Joachim Protze 18 Jul '21

18 Jul '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 22. Juli 2002, 13.00 Uhr Ort: Zoom Videokonferenz https://rwth.zoom.us/j/97475619537?pwd=NzBLYkFqREVISyt3QnNSd1ZoK2NZZz09 Referent: Dipl.-Inf. Joachim Protze Lehrstuhl Informatik 12 Thema: Modular Techniques and Interfaces for Data Race Detection in Multi-Paradigm Parallel Programming Abstract: The demand for ever-growing computing capabilities in scientific computing and simulation has led to heterogeneous computing systems with multiple parallelism levels. The aggregated performance of the Top 500 high-performance computing (HPC) systems showed an annual growth rate of 85% for the years 1993-2013. As this growth rate significantly exceeds the growth rate of 40% to 60% supported by Moore’s law, the additional growth was always supported by an increasing number of computing nodes with distributed memory and connected by a network. The message passing interface (MPI) proved to be the dominating programming paradigm for distributed memory computing as the most coarse-grain level of parallelism in HPC. While performance gain from Moore’s law in the last century mainly went into single-core performance by increasing the clock frequency, we see an increasing number of computing cores per socket since the beginning of this century. The cores within a socket or a node share the memory. Although MPI can be used and is used for shared memory parallelization, explicit use of shared memory as with OpenMP can improve the scalability and performance of parallel applications. As a result, hybrid MPI and OpenMP programming is a common paradigm in HPC. Memory access anomalies such as data races are a severe issue in parallel programming. Data race detection has been studied for years, and different static and dynamic analysis techniques have been presented. This work will not try and propose fundamentally new analysis techniques but will show how high-level abstraction of MPI and OpenMP can be mapped to the low-level abstraction of analysis tools without impact on the analysis’s soundness. This work develops and presents analysis workflows to identify memory access anomalies in hybrid, multi- paradigm parallel applications. This work collects parallel variants of memory access anomalies known from sequential programming and identifies specific patterns for distributed and shared memory programming. Furthermore, this work identifies the high-level synchronization, concurrency, and memory access semantics implicitly and explicitly defined by the parallel programming paradigms’ specifications to provide a mapping to the analysis abstraction. As part of these high-level concurrency concepts, we can identify several sources of concurrency within a thread. This work compares two techniques to handle this high- level concurrency for data race analysis and finds that a combined approach works best in the general case. The evaluation shows that this work’s analysis workflow provides a high precision while enabling increased recall for concurrency within a thread. In this talk, we will focus on the mapping of high-level concurrency abstractions to low-level analysis abstractions as an important key point of this thesis and present the results of the work. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Richard Wilke
by Richard Wilke 14 Jul '21

14 Jul '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 23. Juli 2021, 10:00 Uhr Zoom: https://rwth.zoom.us/j/97181863376?pwd=VmZIUzlNTXhQRFl0S25uRFBTRW0wdz09 Meeting-ID: 971 8186 3376 Kenncode: 867315 Referent: Richard Wilke, M.Sc. LuFG Mathematische Grundlagen der Informatik Thema: Reasoning about Dependence and Independence: Teams and Multiteams Abstract: Team semantics is the mathematical basis of modern logics for reasoning about dependence and independence. Its core feature is that formulae are evaluated against a set of assignments, called a team. This approach dates back to Hodges (1997) who used it to provide a compositional semantics for independence friendly logic. Building on this idea, Väänänen (2007) suggested that dependencies between variables should not be treated as annotations of quantifiers, but as atomic properties of teams. However, being based on sets, team semantics can only be used to reason about the presence or absence of data. Multiteam semantics instead takes multiplicities of data into account and is based on multisets of assignments, called multiteams. In this talk we give an overview of this formalism, explore a wide spectrum of logics with multiteam semantics and compare them with regard to their expressive power. We exhibit some striking differences between multiteam and team semantics, and also show where these formalisms are similar. Moreover, we present a game-theoretic semantics for our logic and establish connections between logics with multiteam semantics and variants of existential second-order logic. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Evgeny Kusmenko
by Kusmenko, Evgeny 06 Jul '21

06 Jul '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 19. Juli 2021, 14.30 Uhr Zoom: https://rwth.zoom.us/j/99446295120?pwd=NWU0QmIvVkpONlV2R0xmbmloTkhZZz09 Referent: Dipl.-Ing. Evgeny Kusmenko Lehrstuhl Informatik 3 Thema: Model-Driven Development Methodology and Domain-Specific Languages for the Design of Artificial Intelligence in Cyber-Physical Systems Abstract: The development of cyber-physical systems poses a multitude of challenges requiring experts from different fields. Such systems cannot be developed successfully without the support of appropriate processes, languages, and tools. Model-driven software engineering is an important approach which helps development teams to cope with the increasing complexity of today's cyber-physical systems. In this talk we are going to discuss a model-driven engineering methodology with a particular focus on interconnected intelligent cyber-physical systems such as cooperative vehicles. The basis of the proposed methodology is a component-and-connector architecture description language focusing on the decomposition and integration of cyber-physical system software. It features a strong, math-oriented type system abstracting away from the technical realization and incorporating physical units. To facilitate the development of highly-interconnected self-adaptive systems, the language enables its users to model component and connector arrays and supports architectural runtime-reconfiguration. Architectural elements can be altered, added, and removed dynamically upon the occurrence of trigger events. In order to fully cover the development process, the proposed methodology, in addition to structural modeling, provides means for behavior specification and its seamless integration into the components of the architecture. A matrix-oriented scripting language enables the developer to specify algorithms using a syntax close to the mathematical domain. What is more, a dedicated deep learning modeling language is provided for the development and training of neural networks as directed acyclic graphs of neuron layers. The framework supports different learning methods including supervised, reinforcement, and generative adversarial learning, covering a broad range of applications from image and natural language processing to decision making and test data generation. The presented toolchain enables an automated generation of fully functional C++ code together with the corresponding build and training scripts based on the architectural models and behavior specifications. Finally, to facilitate the integration and deployment of the modeled software in distributed environments, we use a tagging approach to model the middleware and to control a middleware generation toolchain. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Patrick Landwehr
by Landwehr, Patrick 05 Jul '21

05 Jul '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 14. Juli 2021, 10.00 Uhr Ort: Zoom Videokonferenz Link: https://rwth.zoom.us/j/94853770165?pwd=Uzk4TjI4TGNJQ3owaXJDbUxMT2d0UT09 Meeting-ID: 948 5377 0165 Kenncode: 046795 Referent: Patrick Landwehr M.Sc. Lehrstuhl Informatik 7 Thema: Tree Automata with Constraints on Infinite Trees Abstract: Tree automata on infinite trees are a powerful tool that is widely used for decision procedures and synthesis of logical specifications. It is well known that finite tree automata have good algorithmic properties, but somewhat limited expressive power. For example, they cannot verify that certain subtrees of an input tree are equal. In order to model such properties, we study extensions of tree automata that use so called constraints to compare whole subtrees of an input. We distinguish between two types of constraints: local constraints and global constraints. Local constraints can be used to compare the direct subtrees of each node. In this thesis we first summarize the existing results of tree automata with local constraints for infinite trees. Then we paritally answer the open question whether the class of languages recognizable by these automata is closed under projection. That is, we show that in the case of automata with Büchi acceptance condition the class of recognizable languages is closed under projection. As a consequence, we obtain a new decision algorithm for the emptiness problem as well as a proof for the fact that each non-empty language recognized by a Büchi tree automaton with sibling constraints contains a regular tree. Moreover, we also study logical characterizations of this class of languages. Tree automata with global constraints are able to compare compare subtrees whose positions are defined by the states reached in a run. For example, this model can verify that all subtrees rooted at positions where a certain state is reached are equal. In this thesis we generalize the model introduced on finite trees to the setting of infinite trees. We show that most closure properties and decidability results can be extended from finite to infinite trees. However, new techniques are required in order to do so. While the decidability of the emptiness problem remains an open question in general, we present decidability results for some subclasses of tree automata with global constraints. That is, if the automaton tests only for equality of subtrees (and not for inequality) the emptiness problem is decidable. The same is true if the underlying language (i.e. when ignoring the constraints) is countable. We also study the special case of automata with global constraints on unary infinite trees (omega-words). Here we show that in contrast to branching trees, the class of languages recognizable by these automata is closed under complement. Finally, we present precise logical characterizations for all of the subclasses mentioned, by extensions of monadic second order logic on infinite trees (or omega-words). Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Andrea Schnorr
by Andrea Schnorr 03 Jul '21

03 Jul '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 19. Februar 2021, 11.00 Uhr Zoom: https://rwth.zoom.us/j/2452218628 Referent: Andrea Schnorr, M.Sc. LuFG i12 Thema: Feature Tracking for Space-Filling Structures Abstract: Feature-based visualization is a proven strategy to deal with the massive amounts of data emerging from time-dependent simulations: the analysis focuses on meaningful structures, i.e., said features. Feature tracking algorithms aim at automatically finding corresponding objects in successive time steps of these time-dependent data sets in order to assemble the individual objects into spatio-temporal features. Classically, feature-based visualization has focused on sparse structures, i.e. structures which cover only a small portion of the data domain. Given a sufficiently high temporal resolution, existing tracking approaches are able to reliably resolve the correspondence between feature objects of successive time steps. Our research is motivated by our collaborators' work on the statistical analysis of structures that are space-filling by definition: dissipation elements. Space-filling structures partition the entire domain. Our collaborators aim at extending their statistical analysis to a time-dependent setting. Hence, we introduce an efficient approach for general feature tracking which handles both sparse and space-filling data. To this end, we develop a framework for automatic evaluation of tracking approaches, an algorithmic framework for feature tracking, and an efficient implementation of this framework. First, we propose a novel evaluation framework based on algorithmic data generators, which provide synthetic data sets and the corresponding ground truth data. This framework facilitates the structured quantitative analysis of an approach's feature tracking performance and the comparison of different approaches based on the resulting measurements. Second, we introduce a novel approach for tracking both sparse and space-filling features. The correspondence between neighboring time-steps is determined by successively solving two graph optimization problems. In the first phase, one-to-one assignments are resolved by computing a maximum-weight, maximum-cardinality matching on a bi-partite graph. In its second phase, the algorithm detects events by finding a maximum weight independent set in a graph of all possible, potentially conflicting event explanations. Third, we show an optimized version of the second stage of the tracking framework which exploits the model-specific graph structure arising for the tracking problem. The method's effectiveness is demonstrated by a set of case studies including the use of the evaluation framework as well as the analysis of miscellaneous real-world simulation data sets. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Herr Peter Marcel de Lange
by Leany Maaßen 17 Jun '21

17 Jun '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 20. Juli 2021, 10:00-11:00 Uhr Zoom: <https://rwth.zoom.us/j/93538347394?pwd=YXlqZ1VqSE0vRjRnclRtQTY5RVJOZz09> https://rwth.zoom.us/j/93538347394?pwd=YXlqZ1VqSE0vRjRnclRtQTY5RVJOZz09 Meeting-ID: 935 3834 7394 Kenncode: 388699 Referent: Herr Peter Marcel de Lange, M.Sc. Lehrstuhl Informatik 5 Thema: Scaffolding Decentralized Community Information Systems for Lifelong Learning Communities Abstract: With the rise of the Web 2.0, social networking sites and content management systems enabled professional communities to create Web content. But it simultaneously put the communities at the mercy of the platform operators and software providers. Decentralized community information systems bring in a new perspective by offering self-hosting, self-governing and self-developing communities. In this dissertation, we followed a design science approach that provides support for communities to create and host their own, decentralized community information systems. On the one hand, we produced several artifacts to provide possible answers to the question of what properties such an infrastructure needs to fulfill. On the other hand, we transfer the metaphor of educational scaffolding to the domain of service development. We demonstrated and evaluated our open source artifacts on a European scale, with three longitudinal studies conducted within several communities from different areas of technology enhanced learning, such as the European voluntary service, vocational and educational training providers and in higher education mentoring scenarios. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

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