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

Einladung: Informatik-Oberseminar Anton Pirogov

von Anton Pirogov

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 18. Februar 2021, 10:15 Uhr Ort: Zoom Videokonferenz Link: https://rwth.zoom.us/j/96519556069?pwd=MUVqbXpvZE5XVXBSdDNHZW5LMmJpQT09 Meeting-ID: 965 1955 6069 Kenncode: 482378 Referent: Anton Pirogov, M.Sc. Lehrstuhl Informatik 7 Thema: Determinization and Ambiguity of Classical and Probabilistic Büchi Automata Abstract: Büchi automata can be seen as a straight-forward generalization of ordinary NFA, adapted to handle infinite words. While they were originally introduced for applications in decidability of logics, they became a popular tool for practical applications, e.g. in automata-based approaches to model checking and synthesis problems. Being arguably both the simplest and most well-known variant in the zoo of so-called omega-automata that are considered in this setting, they serve as an intermediate representation of omega-regular specifications of verification or synthesis requirements that are usually expressed in a more declarative fashion, e.g. using linear temporal logic. Unfortunately, nondeterministic automata are not directly suitable for certain applications, whereas deterministic Büchi automata are less expressive. This problem is usually solved by either constructing deterministic automata of a different kind, or by restricting their ambiguity, i.e., the maximal number of accepting runs on some word. In both cases, the transformation is expensive, yielding an exponential blow-up of the state space in the worst case. Therefore, optimized constructions and heuristics for common special cases are useful and in demand for actual practical applications. In this talk we present a new general construction for determinization from nondeterministic Büchi to deterministic parity automata that unifies the dominant branches of previous approaches based on the Safra construction and the Muller-Schupp construction. Additionally, we sketch a number of new heuristics for determinization, some of which exploit properties of our unified construction. Apart from the classical nondeterministic and deterministic variants of automata, it is natural to consider probabilistic automata, i.e., automata that use a probability distribution on the states instead of nondeterminism to decide which state to go to next. It is known that in general, such automata are more expressive than classical automata and many decision problems are undecidable. We present subclasses of probabilistic automata that correspond to certain ambiguity classes and are not more expressive than classical automata. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 5 Monate

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Einladung Informatik-Oberseminar Konrad Anton Fögen

von Zinner, Marion

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/91716576115?pwd=ZzUwemxXWUJkQURjQjJibmlGb3dYZz09 Meeting ID: 917 1657 6115 Passcode: 496556 Referent: Konrad Anton Fögen M.Sc. Lehr- und Forschungsgebiet Softwarekonstruktion Thema: Combinatorial Robustness Testing based on Error-Constraints Abstract: In this talk, we present an extension to combinatorial testing (CT) which is an effective specification-based test method that is based on an input parameter model (IPM). We argue that robustness is an important property of a software, which must be tested in addition to a software's functionality. This requires invalid values and invalid value combinations to be able to observe a software's reaction to them. However, the effectiveness of CT deteriorates in the presence of invalid values or invalid value combinations. This phenomenon is called invalid input masking effect and is already acknowledged in some research. It led to extensions of CT that we call combinatorial robustness testing (CRT). The objective of CRT is to improve the fault detection by avoiding invalid input masking. This is achieved by separating the testing of valid values and valid value combinations from the testing of invalid values and invalid value combinations. While CRT is a promising extension of CT, it is still insufficiently researched. For instance, in related work, IPMs are extended with additional semantic information to specify invalid values. However, invalid value combinations cannot be specified directly. Therefore, the objective of this work is to further expand the idea of CRT. The aim is to develop a new CRT test method with a modeling approach to specify invalid values and invalid value combinations equally well. This modeling approach should also be incorporated into explicit test adequate criteria and test selection strategies. Furthermore, this modeling approach shall be supported by automated techniques. First, we conduct a controlled experiment to check if CRT is necessary at all or if CT is already appropriate to test robustness. Based on the result, we continue and develop a refined t-factor fault model that incorporates robustness faults and the inherent invalid input masking effect. Next, we develop a new test method for CRT and introduce a new structure that extends the structure of IPMs. It is called robustness input parameter model (RIPM) and contains the concept of error-constraints which is an additional set of logical expressions to describe the validity of values and value combinations. With the refined t-factor fault model and the new RIPM structure, new test adequacy criteria that incorporate the additional semantic information and new test selection strategies that satisfy the test adequacy criteria are developed. The new concept of error-constraints requires additional effort in modeling. Therefore, we develop two techniques to support the modeling of them. First, we develop a technique to identify and repair inconsistencies among error-constraints. Second, we develop a technique to automatically generate error-constraints based on the conformance to another system. Last but not least, all aforementioned concepts and techniques are operationalized and integrated in a test automation framework which includes a process, an architecture, and a Java-based reference implementation. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 5 Monate

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Einladung Informatik-Oberseminar Oliver Kautz

von Kautz, Oliver

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/96367107600?pwd=c2lMb2M1OXZXSHZFalRySUR2QTExUT09 Referent: Oliver Kautz M.Sc. Lehrstuhl Informatik 3 Thema: Model Analyses Based on Semantic Differencing and Automatic Model Repair Abstract: Models are the primary development artifacts used in model-driven software development. Therefore, models continuously evolve during the design, development, and maintenance of software systems. Thus, model differencing is an important task to understand the syntactic and semantic differences between model versions. Previous work produced general (and thus language-independent) approaches for syntactic model differencing, but only a few language-dependent approaches for semantic model differencing. Approaches combining syntactic with semantic model differencing by relating the syntactic changes of models to their semantic differences rarely exist. Previous work neglected the development of language-independent approaches abstracting from a concrete model property for detecting the syntactic elements of a model, which cause that the model does not satisfy the property. If the property encodes a requirement and the non-satisfaction represents the existence of a bug, then detecting the syntactic model elements causing the non-satisfaction of the property facilitates developers in detecting the syntactic model elements causing the bug. In this talk, we present a framework for precisely defining modeling languages, including syntax, semantics, and model evolution possibilities. We discuss syntactic and semantic model differencing. The framework is instantiated with four concrete modeling languages: Time-synchronous port automata, feature diagrams, sequence diagrams, and activity diagrams. Based on the framework for precisely defining modeling languages, we present a modeling language and property-independent framework for automatic model repairs. The framework facilitates developers in detecting the syntactic elements of a model causing that the model does not satisfy a property. Instantiating the framework with a concrete modeling language and a concrete model property enables the automatic calculation of syntactic changes that transform a model not satisfying the property to a model that satisfies the property. The framework relies on the assumption that it is possible to partition the syntactic changes applicable to each model into finitely many model-specific and property- specific equivalence classes. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 5 Monate

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Einladung Informatik-Oberseminar Martin Serror

von Martin Serror

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 29. Januar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/95719946489?pwd=S0lITm9pcW45b1k4SW5EVis2a1poQT09 Referent: Martin Serror, M.Sc. Lehrstuhl Informatik 4 Thema: On the Benefits of Cooperation for Dependable Wireless Communications Abstract: The emerging Industrial Internet-of-Things (IIoT) improves flexibility, productivity, and costs of industrial processes by connecting sensors, actuators, and controllers to each other and the Internet. On the factory floor, such interconnections increasingly rely on wireless communications, reducing deployment and maintenance costs while supporting the mobility of communication partners. The industrial domain, however, is mainly characterized by safety- and mission-critical Machine-to-Machine communication. Therefore, state-of-the-art wireless communication protocols for home and business environments, such as WLAN and Bluetooth, are not suited for the IIoT. Consequently, the IIoT requires dependable wireless communication, achieving both high reliability and low latency. A promising approach for so-called Ultra-Reliable Low-Latency Communication (URLLC) in the IIoT is cooperative diversity, since the participating stations already collaborate toward a common goal, i. e., keeping the industrial process running. There, a sending station exploits multiple independent transmission paths via cooperating relays to convey a packet to its destination reliably. In contrast to spatial diversity, this approach also works with single-input single-output transceivers. However, when considering relaying for URLLC, it is particularly challenging that all participants have to share the scarce transmission resources. Hence, in this talk, we investigate various mechanisms enabling dependable wireless communication, i. e., increasing communication reliability within a bounded low latency, where we mainly focus on the benefits of cooperative diversity. Therefore, we explore different design options for URLLC and evaluate them, leveraging the advantages of different methodological approaches. This talk thus offers valuable insights into designing communication protocols with challenging requirements. At the example of cooperation, we thoroughly retrace the development process from analysis to prototypical deployment. On the one hand, the achieved results contribute to URLLC for the IIoT; on the other hand, they provide a critical examination of the selected evaluation methodologies. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 6 Monate

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13.01.2020, 16:00: guest talk: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy ofAlgorithms for Differential Privacy

von Bolke-Hermanns, Helene-Maria

Happy new year, You are cordially invited to the next UnRAVeL guest talk on Wednesday, 13.01.2021, 16.00: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy of Algorithms for Differential Privacy Differential privacy is a mathematical framework for performing privacy-preserving computations over sensitive data. One important feature of differential privacy algorithms is their ability to achieve provable individual privacy guarantees and at the same time ensure that the outputs are reasonably accurate. Such algorithms compute noisy versions of the right answers to aggregate queries on sensitive data to ensure privacy. Privacy guarantees demand that the algorithm running on "similar" data sets produce responses that are statistically similar; this provides a very strong form of individual privacy. Accuracy, on the other hand, demands that the algorithms output, though noised, be sufficient close to the correct answer for a query. In this talk we will present preliminary results on the algorithmic complexity of checking the privacy and accuracy requirements for a given algorithm. Joint work with Giles Barthe, Rohit Chadha,Vishal Jagannath, Paul Krogmeier, and Prasad Sistla. Based on papers in LICS2020 and POPL 2021. Wednesday, 13.01.2020, 16:00 (!); https://www.unravel.rwth-aachen.de/go/id/kfruu https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

3 Jahre, 6 Monate

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Einladung: Informatik-Oberseminar Isaak Lim

von Isaak Lim

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 21. Januar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/98847090675?pwd=TnA5L3NCTkx0TjBmMWJEZHZnQkx1QT09 Meeting-ID: 988 4709 0675 Kenncode: 904296 Referent: Isaak Lim M.Sc. Lehrstuhl Informatik 8 Thema: Learned Embeddings for Geometric Data Abstract: Solving high-level tasks on 3D shapes such as classification, segmentation, vertex-to-vertex maps or computing the perceived style similarity between shapes requires methods that are able to extract the necessary information from geometric data and describe the appropriate properties. Constructing functions that do this by hand is challenging because it is unclear how and which information to extract for a task. Furthermore, it is difficult to determine how to use the extracted information to provide answers to the questions about shapes that are being asked (e.g. what category a shape belongs to). To this end, we propose to learn functions that map geometric data to an embedding space. The outputs of those maps are compressed encodings of the input geometric data that can be optimized to contain all necessary task-dependent information. These encodings can then be compared directly (e.g. via the Euclidean distance) or by other fairly simple functions to provide answers to the questions being asked. Neural networks can be used to implement such maps and comparison functions. This has the benefit that they offer flexibility and expressiveness. Furthermore, information extraction and comparison can be automated by designing appropriate objective functions that are used to optimize the parameters of the neural networks on geometric data collections with task-related meta information provided by humans. We therefore have to answer two questions. Firstly, given the often irregular nature of representations of 3D shapes, how can geometric data be represented as input to neural networks and how should such networks be constructed? Secondly, how can we design the resulting embedding space provided by neural networks in such a manner that we are able to achieve good results on high-level tasks on 3D shapes? In this talk we provide answers to these two questions. Concretely, de- pending on the availability of the data sources and the task specific requirements we compute encodings from geometric data representations in the form of images, point clouds and triangle meshes. Once we have a suitable way to encode the input, we explore different ways in which to design the learned embedding space by careful construction of appropriate objective functions that extend beyond straightforward cross-entropy minimization based approaches on categorical distributions. We show that these approaches are able to achieve good results in both discriminative as well as generative tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 6 Monate

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Einladung: Informatik-Oberseminar Alexander Hermans

von Alexander Hermans

************************************************************************ * * * Einladung * * * * Informatik-Oberseminar * * * ************************************************************************ Zeit: Montag, 21. Dezember 2020, 13:15-14:15 Uhr Zoom: https://us02web.zoom.us/j/89686061674?pwd=UFUxaWMrdFJtY09JSWRBKzNxUnU0QT09 <https://www.google.com/url?q=https://us02web.zoom.us/j/89686061674?pwd%3DUF…> Meeting-ID: 896 8606 1674 Kenncode: 923078 Referent: Alexander Hermans, M. Sc. Lehrstuhl Informatik 13 Thema: Learning-based Visual Scene and Person Understanding for Mobile Robotics Abstract: We have seen tremendous progress in the computer vision community across the past decades, especially with advancements in deep learning, which is now used as the core machine learning approach for most tasks. However, when deploying computer vision approaches in actual robotic applications, we often find that top-performing methods do not work as well as expected due to hardware constraints and different input data characteristics. We deal with visual scene and person understanding which are highly relevant for robotics applications. Robots need to be able to understand their environment and take special care around persons to ensure a safe navigation and interaction. We specifically deal with three important sub-tasks: semantic segmentation, 2D laser-based object detection, and person re-identification. Semantic segmentation deals with the task of labeling every pixel or point in a scene with a class label. This can in turn be used to extract higher level information about the surrounding scene, which can be used as context for further planning and interaction tasks. While the resulting segmentations provide object labels, they do not contain instance labels, making it hard to detect object instances. However, object detection is an important capability for allowing robots to safely navigate between dynamic objects. Especially the detection of persons is an important task, enabling robots to interact with us. Since many mobile platforms are already equipped with a 2D laser scanner, they are interesting input sensors for object detection, even though the resulting scans only contain sparse data. In addition to person detection, person re-identification is an important task. This can be used to improve tracking, but also allows to gather longer-term statistics and enables person specific interactions. For each of the three tasks we propose state-of-the-art approaches, however, we also consider aspects that are important for their real-world deployability and show applications of our methods within the context of robotics projects. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Jahre, 7 Monate

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Einladung: Informatik-Oberseminar Frau Can Sun

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 17. Dezember 2020, 15:00-16:00 Uhr Zoom: https://rwth.zoom.us/j/92609933435?pwd=OStsV2w0UUZrK2NmNnE4WDE0Y0pEQT09 Meeting-ID: 926 0993 3435 Kenncode: 584723 Referentin: Frau Can Sun, M. Sc. Lehrstuhl Informatik 5 Thema: Embedding of real-world complexity modeling in adaptive supply chain systems engineering Abstract: Successful supply chain complexity and change management is critical for companies to gain competitive advantages. This thesis aims to build a comprehensive supply chain complexity measurement framework through detailed modeling and apply it in the changeable environment. The research starts with modeling the supply chain and its dynamic change as sociotechnical systems. Then a complexity measurement framework that is decomposed into different levels of complexity drivers is developed, and a decision-making framework for various change scenarios is proposed by highlighting complexity as one of the decisive evaluation criteria. The proposed solutions are validated with a set of real-world case studies from the semiconductor industry. Each case represents one change scenario and addresses the complexity from a particular perspective; the problem is analyzed and modeled with the support of proposed models and frameworks. The results show that complexity can be measured by a set of indicators or formulas, and thus support decision-making by comparing the change-induced complexity. 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

3 Jahre, 7 Monate

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reminder: today, 10:30, Kuldeep Meel (NUS Singapore): Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet

von Bolke-Hermanns, Helen

Dear all, This is a short reminder about the talk of Kuldeep Meel (NUS Singapore)<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Gastwi…> today at 10:30. Title: Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet Given a Boolean formula F, the problem of model counting, also referred to as #SAT, is to compute the number of solutions of F. The hashing-based techniques for approximatecounting have emerged as a dominant approach, promising achievement of both scalability and rigorous theoretical guarantees. The standard construction of strongly 2-universal hash functions employs dense XORs (i.e., involving half of the variables in expectation), which is widely known to cause degradation in the runtime performance of state of the art SAT solvers. Consequently, the past few years have witnessed an intense activity in the design of sparse XORs as hash functions. In this talk, we will first survey the known results, and identify the crucial bottleneck contributing to their lack of ability to provide speedups. We will then formalize a relaxation of universal hashing, called concentrated hashing, and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that logarithmically sized XORs suffices for the design of sparse hash functions belonging concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. Our comprehensive experimental evaluation of our algorithm on 1896 benchmarks with computational effort of over 20,000 computational hours demonstrates speedup compared to existing approaches. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees. (Based on Joint work with S. Akshay, D. Agarwal, and Bhavishya; The corresponding papers were published at SAT-20 and LICS-20) Bio: Kuldeep Meel is Sung Kah Kay Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. He is a recipient of 2019 NRF Fellowship for AI. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 201 Wednesday, 2.12.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel https://www.unravel.rwth-aachen.de/go/id/kfsgc Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

3 Jahre, 7 Monate

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02.12.2020, 10:30, Kuldeep Meel (NUS Singapore): Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet

von Bolke-Hermanns, Helen

Dear members of the Computer Science Department, You are cordially invited to the talk of Kuldeep Meel (NUS Singapore)<https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Gastwi…> for next week, 2,12., 10:30. Title: Sparse Hashing for Scalable Approximate Model Counting: When Theory and Practice Finally Meet Given a Boolean formula F, the problem of model counting, also referred to as #SAT, is to compute the number of solutions of F. The hashing-based techniques for approximatecounting have emerged as a dominant approach, promising achievement of both scalability and rigorous theoretical guarantees. The standard construction of strongly 2-universal hash functions employs dense XORs (i.e., involving half of the variables in expectation), which is widely known to cause degradation in the runtime performance of state of the art SAT solvers. Consequently, the past few years have witnessed an intense activity in the design of sparse XORs as hash functions. In this talk, we will first survey the known results, and identify the crucial bottleneck contributing to their lack of ability to provide speedups. We will then formalize a relaxation of universal hashing, called concentrated hashing, and establish a novel and beautiful connection between concentration measures of these hash functions and isoperimetric inequalities on boolean hypercubes. This allows us to obtain tight bounds on variance as well as the dispersion index and show that logarithmically sized XORs suffices for the design of sparse hash functions belonging concentrated hash family. Finally, we use sparse hash functions belonging to this concentrated hash family to develop new approximate counting algorithms. Our comprehensive experimental evaluation of our algorithm on 1896 benchmarks with computational effort of over 20,000 computational hours demonstrates speedup compared to existing approaches. To the best of our knowledge, this work is the first study to demonstrate runtime improvement of approximate model counting algorithms through the usage of sparse hash functions, while still retaining strong theoretical guarantees. (Based on Joint work with S. Akshay, D. Agarwal, and Bhavishya; The corresponding papers were published at SAT-20 and LICS-20) Bio: Kuldeep Meel is Sung Kah Kay Assistant Professor of Computer Science in School of Computing at the National University of Singapore. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay. He is a recipient of 2019 NRF Fellowship for AI. His research interests lie at the intersection of Artificial Intelligence and Formal Methods. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 201 Wednesday, 2.12.2020, 10:30 https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel https://www.unravel.rwth-aachen.de/go/id/kfsgc Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

3 Jahre, 7 Monate

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