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

Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 25 Jun '25

25 Jun '25

Dear all, We invite you to the next UnRAVeL survey lecture talk Tomorrow (26.06.) 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Michael Schaub about "Oversmoothing in graph neural networks“! -------------------- Titel: Oversmoothing in graph neural networks Abstract: Graph neural networks (GNNs) have emerged as powerful tools for processing relational data in applications. However, GNNs suffer from the problem of oversmoothing, the property that the features of all nodes exponentially converge to the same vector over layers, prohibiting the design of deep GNNs. In this work we study oversmoothing in graph convolutional networks (GCNs) from different perspectives and demonstrate that, contrary to what is often claimed in the literature, GNNs do not have to oversmooth. -------------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 26.06.2025 - Michael Schaub - Oversmoothing in Graph Neural Networks 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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Einladung: Informatik-Oberseminar Markus Dahlmanns
by Dahlmanns, Markus 24 Jun '25

24 Jun '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 2. Juli 2025, 10.00 Uhr Ort: Raum 9007, Gebäude E3, Ahornstr. 55 (COMSYS Seminarraum [1]) Referent: Markus Dahlmanns, M.Sc. Lehrstuhl für Informatik 4 (COMSYS) Thema: Identifying Security Issues in the Industrial Internet of Things Abstract: The Industrial Internet of Things (IIoT) allows the collection and communication of data from production processes as well as sending and receiving commands for interaction and actuation to, e.g., increase the efficiency of processes. However, a secure and safe operation is essential to protect, e.g., workers laboring in the operating space of machines and the environment, which were shown to suffer from the misoperation of critical IIoT deployments. As a foundation for such a secure and safe operation IIoT protocols nowadays include security features. However, it is unclear whether these developments of protocol specifications lead to a secure IIoT in practice. In this talk, we address the open research gap of the current uncertainty on IIoT security. To this end, we first set out to assess the security of today's IIoT. Second, we analyze the pitfalls that hinder operators from operating securely despite the existence of secure protocols. Last, we give an overview on novel approaches that help operators secure their deployments. Our first three contributions show from various angles that the majority of Internet-exposed IIoT deployments are insecurely configured, independently of their potential deployment date and the protocol used being either secure-by-design or retrofitted. In our fourth contribution, we trace this problem back to modern technologies like containerization that ease deployment processes but also disguise security issues. Our last two contributions propose two novel mechanisms that can increase the security of the IoT in the future. Our contributions underpin the vast number of issues in IIoT security despite the existence of strong security features in today's protocol specifications and sketch countermeasures to tackle the identified pitfalls that operators tap into when configuring their deployments. Overall, our results encourage shifting from secure-by-design to secure-by-default protocols. Es laden ein: die Dozentinnen und Dozenten der Informatik [1] https://www.comsys.rwth-aachen.de/contact/how-to-get-to-comsys.pdf -- Markus Dahlmanns, M.Sc., Ph.D. Student Chair of Communication and Distributed Systems RWTH Aachen University, Germany tel: +49 241 80 21425 web: https://www.comsys.rwth-aachen.de/team/markus-dahlmanns/

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Research Talk Prof. Romaric Duvignau, 25. Juni 2025, 10:00 Uhr
by Jan Pennekamp 18 Jun '25

18 Jun '25

Dear all, COMSYS is hosting Prof. Romaric Duvignau next week. As part of this visit, he will also give a research talk. More details below. **Title:** Data-Driven and Efficient Algorithms for Enhanced Communication at the Edge **When:** Wednesday, June 25th 10:00-11:00 **Where:** At COMSYS' Seminar Room – Building E3, Room 9007 **Abstract:** Communication is the cornerstone element of many distributed, cyber-physical, or IoT systems (e.g., vehicular networks, smart grid, sensor networks). Transmitting data in the most efficient manner is often a requirement for many applications to reduce costs and boost performance. What is needed in this context are dedicated (i.e., data- or use-case-driven) and efficient algorithms that must be designed, analyzed, and experimentally validated, taking into account both the communication network and the distribution and continuous aspect of the generated data. My research centers on the design, study, and evaluation of those distributed and application-driven algorithms to perform optimization on a continuous basis and decrease computational burden and/or communication costs. In this short presentation, I will focus on recent advancements we have for two specific applications: (1) sharing energy resources among households at the smart grid’s edge and (2) improving the privacy of video streaming. **Speaker Bio:** Romaric Duvignau is an Associate Professor in the Distributed Computing and Systems research group at Chalmers University of Technology, Gothenburg, Sweden. His research focuses on designing data-driven algorithms for distributed applications, addressing challenges in fault tolerance, cybersecurity, privacy, big data and streaming analytics. His current interests include the efficient monitoring of large-scale distributed systems, intelligent data management and decision making in smart grids, federated learning, programmable networks, and network traffic classification. Prof. Duvignau teaches courses on computer networking, distributed systems, and data-driven support for cyber-physical systems and was awarded the Chalmers Pedagogic Prize 2023. You are all cordially invited. Best regards, Jan Pennekamp -- Dr. rer. nat. Jan Pennekamp Communication and Distributed Systems RWTH Aachen University, Germany tel: +49 241 80 21411 web: https://www.comsys.rwth-aachen.de/team/jan-pennekamp/

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Einladung Informatik-Oberseminar Tobias Schulte to Brinke
by Abigail Morrison 12 Jun '25

12 Jun '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 16. Juni 2025, 15.00 Uhr Ort: Raum 9222, Gebäude E3, Informatik-Zentrum Referent: Tobias Schulte to Brinke, MSc. Lehrstuhl Informatik 3 Thema: Analysis of Information Processing and Memory Prerequisites for Temporal Difference Learning in Cortical Neural Network Models Abstract: This doctoral thesis delves into the computational intricacies of the human brain, exploring the capabilities of cortical microcircuit models, the extent of their information processing capacity, and their role in memory and temporal difference learning through the use of cortico-striatal populations. Central to this exploration is the study of spiking neural networks (SNN). This research aims to gain a deeper understanding of the structural and neuronal influences on information processing in these networks and at the same time to provide a guideline for their analysis. In the first part, a network model of a cortical column introduced in a previous paper is reproduced and extended. These analyses show that the specific, data-based connectivity improves computational performance by sharpening the clarity of internal representations rather than increasing the duration of information retention as previously described. Moving beyond traditional task-based evaluations, the second part introduces a novel application of the information processing capacity (IPC) metric to SNN. This approach provides a comprehensive profile of the functions computed by SNN, encompassing memory and nonlinear processing. The study methodically examines various encoding mechanisms and their impact on the IPC and shows that the metric is indicative of the performance in tasks with different demands of nonlinear processing and memory. This exploration not only extends the utility of the IPC metric to more complex neural networks but also offers a deeper insight into their computational capabilities. The third part of the thesis tests a hypothesis about the computation of temporal difference errors in the brain, focusing on two distinct populations of cortical layer 5 neurons: the crossed corticostriatal (CCS) and corticopontine (CPn0 cells. By implementing network models based on these populations and evaluating their memory capabilities through the lens of the IPC, the research supports, at least for continuous rate networks, the proposed role of these neurons in the computation of temporal difference errors. However, the spiking network models pose a greater challenge and exhibit little ability to memorize previous inputs in our experiments. In summary, this work not only confirms and extends existing research results, but also develops new methods for analyzing SNN. It lays a solid foundation for future studies of the brain's computational processes and enriches the field of computational neuroscience with advanced tools and methods for exploring the intricate workings of biologically inspired neural network models. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Prof. Dr. Abigail Morrison Institute for Advanced Simulation (IAS-6) Jülich Research Center & Computer Science 3 - Software Engineering RWTH Aachen http://www.fz-juelich.de/inm/inm-6 http://www.se-rwth.de Office: +49 2428 8097504 Fax # : +49 2461 61-9460 Pronouns: she/her ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Dr. Stephanie Bauer (stellv. Vorsitzende), Prof. Dr. Ir. Pieter Jansens, Prof. Dr. Laurens Kuipers ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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[sekretariate] Einladung: Informatik-Oberseminar Lutz Klinkenberg
by Elke Ohlenforst 05 Jun '25

05 Jun '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 20. Juni 2025, 09:00 Uhr Ort: Raum 9222 (Informatikzentrum E3, Ahornstraße 55) Referent: Lutz Klinkenberg, M.Sc. Lehrstuhl für Informatik 2 Thema: Analysis of Probabilistic Programs Using Generating Functions Abstract: Probabilistic programming languages have emerged as a powerful tool for modeling complex and uncertain systems. Widely applied in fields like artificial intelligence, machine learning, robotics but also in non-technical domains as, e.g., healthcare, social and climate sciences, economic and psychology, these languages allow us to succinctly represent probabilistic processes, including those involving loops, recursion, and other intricate structures. However, reasoning about such programs, particularly those with infinite loops or non- terminating behavior, poses significant challenges. This dissertation introduces a novel approach to address these challenges by developing a denotational probability generating function (PGF) semantics for discrete probabilistic programs with loops. This semantics provides an exact and compact representation of program behaviors, enabling exact Bayesian inference where prior methods often relied on approximations. A central contribution of this work is the introduction of invariant-based reasoning techniques, which facilitate the systematic derivation of posterior distributions and hence also properties like expectations and higher-order moments, even for possibly unbounded loops. Furthermore, we present a syntactic characterization of a class of proba- bilistic programs for which the distributions they describe can be expressed as rational closed-form generating functions. This characterization ensures that exact inference is preserved within this class, allowing for efficient alge- braic manipulation and direct computation of quantities such as tail bounds and moments. By linking program syntax to the algebraic tractability of their semantics, this work establishes a foundational framework for exact reasoning in probabilistic programming. This framework not only enhances the capacity for exact Bayesian inference enabling decision-making under uncertainty but also broadens the applicability of probabilistic programming to domains where formal verification is inevitable. It combines theoretical rigor with practical utility, offering a unified perspective on the structure and behavior of discrete probabilistic programs. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 22 May '25

22 May '25

Dear all, This is a quick reminder on the next UnRAVeL survey lecture talk Today 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Britta Peis about "On Train Routing and Length-bounded Disjoint Paths“! -------------------- Titel: On Train Routing and Length-bounded Disjoint Paths Abstract: In train routing, the headway is the minimum distance that must be maintained between successive trains for safety and robustness. We introduce a model for train routing that requires a fixed headway to be maintained between trains, and study the problem of minimizing the makespan, i.e., the arrival time of the last train, in a single-source single-sink network. For this problem, we first show that there exists an optimal solution where trains move in convoys---that is, the optimal paths for any two trains are either the same or are arc-disjoint. Via this insight, we are able to reduce the approximability of our train routing problem to that of the min-max disjoint paths problem, which asks for a collection of disjoint paths where the maximum length of any path in the collection is as small as possible. While min-max disjoint paths inherits a strong inapproximability result on directed acyclic graphs from the multi-level bottleneck assignment problem, we show that a natural greedy composition approach yields a logarithmic approximation in the number of disjoint paths for series-parallel graphs. We also present an alternative analysis of this approach that yields a guarantee depending on how often the decomposition tree of the series-parallel graph alternates between series and parallel compositions on any root-leaf path. (Joint work with Umang Bhaskar, Katharina Eickhoff, Lennart Kauther, Jannik Matuschke, and Laura Vargas Koch) -------------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 22.05.2025 - Britta Peis - On Train Routing and Length-bounded Disjoint Paths 26.06.2025 - Michael Schaub - Oversmoothing in Graph Neural Networks 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 15 May '25

15 May '25

Dear all, We invite you to the next UnRAVeL survey lecture talk Today 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Joost-Pieter Katoen about "Verification Conquers Fault Tree Analysis: From Theory to Start-Up“! -------------------- Titel: Verification Conquers Fault Tree Analysis: From Theory to Start-Up Abstract: Fault trees are a key element in reliability engineering. The notion of static fault trees goes back to the 1950s. Various extensions have been developed since then such as dynamic fault trees by Joanne Dugan. We will present their formal semantics and will provide some techniques to generate succinct (and partial) state spaces. We will then present the elementary model-checking techniques for the reliability analysis of dynamic fault trees, provide some case studies (a.o. from railway engineering conducted within UnRAVeL) that illustrate the feasibility of the approach. We finally report on how the developed (partially UnRAVeL) techniques have culminated in a state-of-the-art reliability analysis tool that is currently maintained and further developed by a spin-off company. -------------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 15.05.2025 - Joost-Pieter Katoen - Verification Conquers Fault Tree Analysis: From Theory to Start-Up 22.05.2025 - Britta Peis - On Train Routing and Length-bounded Disjoint Paths 26.06.2025 - Michael Schaub - Oversmoothing in Graph Neural Networks 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 08 May '25

08 May '25

Dear all, We invite you to the next UnRAVeL survey lecture talk Today 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Nils Nießen about "Uncertainty in Railways“! -------------------- Titel: Uncertainty in Railways Abstract: Uncertainties and randomness play a major role on the railway and contribute to the operational performance. We provide an overview of where uncertainties occur in the railway system and how they can be modelled. Subsequently, we present methods and algorithms that were developed within the framework of UnRAVeL. Finally, we explain why the railways are still delayed. -------------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 08.05.2025 - Nils Nießen - Uncertainty in Railways 15.05.2025 - Joost-Pieter Katoen - Verification Conquers Fault Tree Analysis: From Theory to Start-Up 22.05.2025 - Britta Peis - On Train Routing and Length-bounded Disjoint Paths 26.06.2025 - Michael Schaub - Oversmoothing in Graph Neural Networks 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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Einladung Informatik-Oberseminar
by Martin Bellgardt 29 Apr '25

29 Apr '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 30. April, 13.00 Uhr Ort: Seminarraum 003, Kopernikusstraße 6 Referent: Martin Bellgardt M.Sc. Lehr- und Forschungsgebiet Informatik 12 Thema: Increasing Immersion in Machine Learning Pipelines for Mechanical Engineering Abstract: Machine learning has become a vital tool in the toolbox of any discipline that regularly interacts with data. This holds especially true for mechanical engineering, where the demand for data driven approaches to engineering challenges has recently increased drastically. While promising results have already been achieved by applying machine learning models in this field, the opaqueness of modern machine learning models, such as artificial neural networks, brings its own challenges. A lack of trust in these models often prevents their application and a lack of intuition for their inner workings prevents them from facilitating scientific discoveries about manufacturing processes. This thesis aims to approach these challenges from the unique angle of increasing the amount of immersion used when interacting with models and data throughout the whole machine learning pipeline. Immersion in virtual environments has the ability to facilitate a phenomenon called presence, which can make users perceive the presented stimuli as more real and engage with them much more directly. However, research regarding the effect of immersion on abstract data visualization is lacking. It is argued that a similar phenomenon, in this thesis called data presence, can be achieved with proper immersive visualization tools. Hence, multiple demonstrators were developed that showcase the possibility to facilitate better understanding of data, models, and the underlying processes using increased degrees of immersion. First, general considerations are presented on developing immersive applications for use in everyday work. Then, multiple immersive applications are presented for data labeling, data understanding and model visualization. Each of these applications is evaluated, showing indications that they can indeed lead to benefits for their users. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Survey Lecture - Highlights of UnRAVeL
by Kassing, Jan-Christoph 23 Apr '25

23 Apr '25

Dear all, We invite you to the next UnRAVeL survey lecture talk on Thursdays 12:30 to 14:00, Computer Science Center, Building E2, ground floor, B-IT room 5053.2, given by Jürgen Giesl about "Termination and Complexity Analysis: From Classical to Probabilistic Programs“! -------------------- Titel: Termination and Complexity Analysis: From Classical to Probabilistic Programs Abstract: We give an overview on our results obtained within UnRAVeL. Before the start of the research training group, we had already developed several techniques for automated termination and complexity analysis for different kinds of "classical" programs. We show how these approaches were extended to probabilistic programs due to our research within UnRAVeL. -------------------- Part of the programme of the research training group UnRAVeL is a series of lectures on the topics of UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is given by one of the researchers involved in UnRAVeL. This years topic is "Highlights of UnRAVeL!". As UnRAVeL nears its conclusion, we take this opportunity to reflect on seven years of excellent research. Through nine talks, we invite you to experience the scientific highlights of our RTG, along with its numerous successes, developments, and collaborations. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2025 and engage in discussions with researchers and doctoral students. The overall remaining schedule is as following: 24.04.2025 - Jürgen Giesl - Termination and Complexity Analysis: From Classical to Probabilistic Programs 08.05.2025 - Nils Nießen - Uncertainty in Railways 15.05.2025 - Joost-Pieter Katoen - Verification Conquers Fault Tree Analysis: From Theory to Start-Up 22.05.2025 - Britta Peis - On Train Routing and Length-bounded Disjoint Paths 26.06.2025 - Michael Schaub - Oversmoothing in Graph Neural Networks 03.07.2025 - Sebastian Trimpe - Uncertainty in Learning and Control 10.07.2025 - Christopher Morris - Exploring the Power of Graph Neural Networks in Solving Optimization Problems 17.07.2025 - Christina Büsing - Dealing with Uncertainty Inspired by Health Care – a Robust Optimization Approach Kind regards, Jan-Christoph for the organisation committee

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