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

Einladung: Informatik Oberseminar Markus Hoehnerbach
by Markus Hoehnerbach 19 Mar '20

19 Mar '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 23. Maerz 2020, 10.00 Uhr Ort: Raum 115, Rogowski-Gebaeude, Referent: Markus Hoehnerbach M.Sc. High-Performance and Automatic Computing Thema: A Framework for the Vectorization of Molecular Dynamics Kernels Abstract: We introduce a domain-specific language (DSL) for many-body potentials, which are used in molecular dynamics (MD) simulations in the area of materials science. We also introduce a compiler to translate the DSL into high-performance code suitable for modern supercomputers. We begin by studying ways to speedup up potentials on supercomputers using two case studies: The Tersoff and the AIREBO potentials. In both case studies, we identify a number of optimizations, both domain-specific and general, to achieve speedups of up to 5x; we also introduce a method to keep the resulting code performance portable. During the AIREBO case study, we also discover that the existing code contains a number of errors. This experience motivates us to include the derivation step, the most error-prone step in manual optimization, in our automation effort. After having identified beneficial optimization techniques, we create a ``potential compiler'', short PotC, which generates fully-usable performance-portable potential implementations from specifications written in our DSL. DSL code is significantly shorter (20x to 30x) than a manual code, reducing both manual work and opportunities to introduce bugs. We present performance results on five different platforms: Three CPU platforms (Broadwell, Knights Landing, and Skylake) and two GPU platforms (Pascal and Volta). While the performance in some cases remains far below that of hand-written code, it also manages to match or exceed manually written implementations in other cases. For these cases, we achieve speedups of up to 9x compared to non-vectorized code. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 1

Einladung: Informatik-Oberseminar Sandra Kiefer
by Sandra Kiefer 03 Mar '20

03 Mar '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 16. März 2020, 10:45 Uhr Ort: Raum 9222, Gebäude E3, Ahornstr. 55 Referentin: Sandra Kiefer, M.Sc. Lehrstuhl Informatik 7 Thema: Power and Limits of the Weisfeiler-Leman Algorithm Abstract: The Weisfeiler-Leman (WL) algorithm is a fundamental combinatorial procedure used to classify graphs and other relational structures. Through its connections to many research areas such as logics and machine learning, surprising characterisations of the algorithm have been discovered. We combine some of these to obtain powerful proof techniques. For every k, the k-dimensional version of the algorithm (k-WL) iteratively computes a stable colouring of the vertex k-tuples of the input graph. The larger k, the more powerful k-WL becomes with respect to the distinguishability of graphs. We have studied two central parameters of the algorithm, its number of iterations until stabilisation and its dimension. The results enable a precise understanding of 1-WL, namely we have determined its iteration number and have developed a complete characterisation of the graphs for which 1-WL correctly decides isomorphism. In higher dimensions, however, the situation is different. For example, it is often not clear at all how to decide if k-WL distinguishes two particular graphs. By our results, 3-WL identifies every planar graph, which drastically improves upon all previously known bounds. Generalising this insight, we obtain the first explicit parametrisation of the WL dimension by the Euler genus of the input graph. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Einladung: Informatik-Oberseminar Gereon Kremer
by Gereon Kremer 28 Feb '20

28 Feb '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 12. März 2020, 10:45 Uhr Ort: Raum 9222, Gebäude E3, Ahornstr. 55 Referent: Gereon Kremer, M.Sc. Theory of Hybrid Systems Thema: Cylindrical Algebraic Decomposition for Nonlinear Arithmetic Problems Abstract: We explore the usage of the cylindrical algebraic decomposition method for satisfiability modulo theories solving, both theoretically and experimentally. This method is commonly understood as an almost atomic procedure that gathers information about an algebraic problem and then allows to answer all kinds of questions about this algebraic problem afterwards. We essentially break up this method into smaller components that we can then process in varying order to derive the particular piece of information – whether the problem is satisfiable or unsatisfiable – allowing to avoid some amount of computations. As this method often exhibits doubly exponential running time, these savings can be very significant in practice. We then turn to an alternative approach to satisfiability modulo theories solving commonly called model-constructing satisfiability calculus. The core idea of this framework is to integrate the theory reasoning, in particular the construction of a theory model, very tightly with the Boolean reasoning. The main theory reasoning engine is again based on the cylindrical algebraic decomposition method, though we focus more on the overall framework here. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Gereon Kremer Lehr- und Forschungsgebiet Theorie Hybrider Systeme RWTH Aachen Tel: +49 241 80 21243

1 0

Einladung Informatik-Oberseminar Christian Corsten
by Corsten, Christian 25 Feb '20

25 Feb '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 10. März 2020, 9.30 Uhr Ort: Raum 2222, Ahornstr. 55 (Informatikgebäude, Hauptbau; Seminarraum i10) Referent: Christian Corsten, M.Sc. Lehrstuhl Informatik 10 Thema: Use the Force: How Force Touch Improves Input on Handheld Touchscreens Abstract: Handheld devices, such as smartphones, have become essential tools in our everyday life. We use them, e.g., to contact people, browse the web, or take pictures. For whatever use, to interact with the handheld device, we hold it with one or two hands and touch with our fingers on the built-in touchscreen. However, this interaction is often constrained to simple contact between the finger and the flat display glass, although touch offers further, richer properties. One such rich property is the intensity of a touch, i.e., its force, that the user applies with every tap to the touchscreen. Incorporating this property into the user’s interaction with the handheld device enables her to become more expressive with every single touch. In this thesis, we present a series of interaction techniques that take advantage of force touch input to make handheld interaction more efficient: When holding the device with two hands in landscape orientation, most of the fingers are unavailable for interaction, since they rest at the back of the device (BoD), holding it in place. Using BoD force input, we can make efficient use of these fingers without sacrificing stability of the device grip. Our technique, BackXPress, enables quick access to shortcuts and menus to augment users’ touch interaction with the frontal screen. For single-handed device use, users can only use their thumb to interact with the frontal touchscreen but cannot reach everywhere without re-grasping the device. Our virtual thumb extension, ForceRay, lets the user cast a ray at unreachable targets and control a cursor on that ray that moves closer to these targets the more force is applied. The technique is ergonomic for the thumb and enables users to maintain a steady device grip. Targets located at the screen edges, like menus and navigation buttons, are acquired quickly. Selection of values from long ordered lists, such as picking a date or time, can also be sped up when using force input. With our Force Picker, users scroll through the value range at various speeds, with the speed being coupled to the force exerted by the thumb. Prior rolling of the thumb to the left or right sets the scrolling direction. Compared to touch-based pickers, our Force Picker not only makes selection faster, but also only consumes little screen space since the gesture footprint for force input is much smaller. While controlling force via fingers requires practice, we show that with training and algorithmic optimizations, users become quickly familiar with force input and gain the benefits of the added expressiveness for handheld interaction. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Einladung: Informatik Oberseminar Daniel Zielasko (Raumänderung!)
by Daniel Zielasko 17 Feb '20

17 Feb '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 21. Februar 2020, 9.00 Uhr Ort: IT Center Erweiterungsbau, Kopernikusstraße 6, S003 Referent: Daniel Zielasko, M.Sc. Lehrstuhl Informatik 12 Thema: DeskVR: Seamless Integration of Virtual Reality into Desk-based Data Analysis Workflows Abstract: In this work, we are looking into the possibilities and unique challenges virtual reality today offers for (office) desk-based scenarios, as they are ubiquitous in data analysis. We characterize the scenario, introduce the term deskVR, and name the technical challenges that come with it. Furthermore, we tackle specific demands in two pillars of interaction in virtual reality, selection & manipulation, and navigation. Then, we investigate passive and active methods to prevent and reduce cybersickness, as for us, tackling cybersickness is one if not the most critical tasks that have to be solved to integrate virtual reality into everyday life successfully. Finally, we apply the methods and findings made in this thesis to a prototypical application framework for immersive 3D graph exploration, serving as proof of concept for the integrability of virtual reality into desk-based working scenarios. In the graph visualization domain, we then also propose new vertex positioning and edge bundling methods that address challenges arising with the performed up-projection into 3D interactive space. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Einladung zum Informatik-Oberseminar, "Feature-aware and feature-driven editing of 3D surface meshes", Referentin Ellen Dekkers
by Ellen Dekkers 20 Jan '20

20 Jan '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 11. Februar 2020, 14.00 Uhr Ort: Ahornstraße 55, E3, Raum 118 (Seminar-Raum I8) Referentin: Dipl.-Inform. Ellen Dekkers Lehrstuhl Informatik 8 Thema: Feature-aware and feature-driven editing of 3D surface meshes Abstract: Feature-aware and feature-driven editing of three-dimensional surface meshes is a very prominent task in Computer Graphics and Geometry Processing applications. Existing methods can be roughly classified into general elastic approaches, which aim at the preservation of a manifold surface's differential properties and hence of local, low-level geometric surface detail, and structure-aware editing techniques focusing on the preservation of high-level surface structures such as feature curves or regular patterns. In this talk, we review both research fields and discuss the respective approaches with a particular focus on their capabilities in preserving various types of surface features. We then present a novel approach to feature-aware mesh editing that combines elastic Laplacian deformation with discrete plastic topology modifications by transferring the concept of seam carving from the image retargeting to the mesh deformation scenario. During editing, a precomputed set of triangle strips, or geometry seams, can be dynamically deleted or inserted in low saliency mesh regions, thereby distributing the deformation distortion non-homogeneously over the model which yields a much better preservation of salient surface features compared to standard elastic deformation. Finally, we remove the manifold restriction and address feature curve driven editing of non-manifold meshes. First, we propose a semi-automatic approach to efficiently and robustly recover characteristic feature curves from free-form surfaces. We then present two practical applications of this technique, the first of which exploits the curves' shape-defining properties and employs them as intuitive modeling handles for editing non-manifold surfaces. In our second application, we turn to a practical scenario in reverse engineering and consider the problem of generating a statistical shape model for car bodies. The crucial step of establishing proper feature correspondences between a large number of input models that exhibit significant shape variations is essentially guided by characteristic feature curves. These curves furthermore serve as modeling metaphors for intuitive exploration of the shape space spanned by the input models, thereby enabling the generation of semantically meaningful, novel car bodies. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Einladung: Informatik Oberseminar Sebastian Junges
by Junges, Sebastian 15 Jan '20

15 Jan '20

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 13. Februar 2019, 13.00 Uhr Ort: Informatikzentrum, E3, Raum 9222 Referent: Sebastian Junges, M.Sc. Lehrstuhl für Informatik 2 (Software Modeling and Verification) Thema: Parameter Synthesis in Markov Models Abstract: Markov models comprise states with probabilistic transitions. The analysis of these models is ubiquitous and studied in, among others, reliability engineering, artificial intelligence, systems biology, and formal methods. Naturally, their analysis crucially depends on the transition probabilities. Often, these probabilities are approximations based on data or reflect configurable parts of a modelled system. To represent the uncertainty about the probabilities, we study parametric Markov models, in which the probabilities are symbolic expressions rather than concrete values. More precisely, we consider parametric Markov decision processes (pMDPs) and parametric Markov chains (pMCs) as special case. Substitution of the parameters yields classical, parameter-free Markov decision processes (MDPs) and Markov chains (MCs). A pMDP thus induces uncountably many MDPs. Each MDP may satisfy reachability and reward properties, such as "the maximal probability that the system reaches an `offline' state is less than 0.01%", or "the maximal expected energy consumption is less than 20 kWh." Lifting these properties to pMDPs yields fundamental problems asking: - "Is there an induced MDP satisfying the property?" (feasibility), its dual - "Do all induced MDPs satisfy the property?" (validity), and advanced problems such as "What is a concise representation for all induced MCs satisfying the property?" We study these problems on a conceptual level, and design and implement both improved and novel algorithms. On the conceptual side, a thorough discussion of the feasibility problem yields new results, such as: (1) that answering various variants of the feasibility problem is — in terms of complexity — as hard as finding roots of a multivariate polynomial, and (2) that these problems are tightly connected to the analysis of memoryless strategies in partially observable MDPs, a famous model in artificial intelligence. Additionally, we introduce family MCs (fMCs), a subclass of pMCs with finitely many induced MCs. Among others, fMCs define fundamental problems underlying the quantitative analysis of software product lines and sketching of probabilistic programs. On the algorithmic side, (1) we present and analyse improved but previously known approaches, and combine them to meet practical needs. Their analysis inspired (2) three new and orthogonal approaches, utilising advances in convex optimisation, as well as adapting prominent ideas such as inductive synthesis and abstraction-refinement to the particular setting. All methods efficiently exploit advances in the off-the-shelf analysis of MDPs. On the empirical side, the new methods improve the state-of-the-art considerably, handling hundreds of parameters and millions of states. All the approaches we present have been implemented in open-source tools. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Vortrag Dr. Markus Freitag (heute)
by Christian Herold 14 Jan '20

14 Jan '20

Sehr geehrte Damen und Herren, wir möchten Sie freundlich auf den Vortrag (s.u.) von Herr Dr. Markus Freitag heute Nachmittag um 14:30 hinweisen. Bitte entschuldigen Sie die kurzfristige Ankündigung. Mit freundlichen Grüßen, Christian Herold *********************************************************************** * * * Einladung zum Gastvortrag * * * ************************************************************************ Zeit: Dienstag, 14. Januar 2020, 14:30 Ort: Ahornstraße 55, E2, Raum 5056 Referent: Dr. Markus Freitag Titel: (Some) Research Happening at Google Translate Abstract: Machine Translation is one of the most appealing research topics in Natural Language Processing and Machine Learning. In this talk, you will be given an overview of some of the current research efforts happening at Google Translate. We will start with a project with the end-goal of training a single model to translate between all languages supported by Google Translate. Neural models can be trained to perform several tasks simultaneously as exemplified by multilingual NMT using a single model to translate between multiple languages. Apart from reducing operational costs, multilingual models improve performance on low and zero-resource language pairs due to joint training. We attempt to study multilingual neural machine translation, using a massive open-domain dataset containing over 25 billion parallel sentences in 103 languages. In the second half of the talk, we focus on translationese, a term that refers to artifacts present in text that was translated into a given language that distinguish it from text originally written in that language. These artifacts include lexical and word order choices that are influenced by the source language as well as the use of more explicit and simpler constructions. Machine translation has an undesirable propensity to produce translationese artifacts, which can lead to higher BLEU scores while being liked less by human raters. First, we train an Automatic Post Editing (APE) model that convert the translationese output into a more natural text. We use this model as a tool to reveal systematic problems with reference translations. Second, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? To sum up, we will discuss why a loss in BLEU score does not always mean lower translation quality. -- Christian Herold Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel: +49 241 80 21613 Fax: +49 241 80 22219 herold(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

1 0

Erinnerung: Informatik-Kolloquium Michael Schaub (08.01.2020 **morgen**, 14:00)
by Joost-Pieter Katoen 07 Jan '20

07 Jan '20

*********************************************************************** * * * Einladung * * * Informatik-Kolloquium * * *********************************************************************** Zeit: Mittwoch, 8. Januar 2020, 14:00 Uhr Ort: Raum 9222, Gebäude E3, Informatikzentrum Referent: Michael Schaub University of Oxford, UK and MIT, USA Thema: Data Science for Networks Abstract: Networks have become a widely adopted model for a range of systems, cutting across Science and Engineering. However, our theoretical understanding of many fundamental phenomena that arise in complex networks and networked systems is still limited. My vision is to develop a data science for networks and dynamical systems that will contribute to addressing this challenge, by combining data-driven and model-based approaches, using the language of graphs and networks. In this talk, we will give a brief overview of such a Data Science for Networks. We first discuss how networks appear naturally within models in different research domains and illustrate the underlying scientific questions via examples drawn from applications. We then examine in some detail the problem of feature learning from graphs with unobserved edges, in which we aim to learn certain aspects of a graph solely from dynamical observations on its nodes, without knowledge of the edge-set of the graph. We conclude with a brief outlook on future challenges and open problems. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0

Einladung: Informatik-Kolloquium Michael Schaub (08.01.2020, 14:00)
by Thomas Noll 13 Dec '19

13 Dec '19

*********************************************************************** * * * Einladung * * * Informatik-Kolloquium * * *********************************************************************** Zeit: Mittwoch, 8. Januar 2020, 14:00 Uhr Ort: Raum 9222, Gebäude E3, Informatikzentrum Referent: Michael Schaub University of Oxford, UK and MIT, USA Thema: Data Science for Networks Abstract: Networks have become a widely adopted model for a range of systems, cutting across Science and Engineering. However, our theoretical understanding of many fundamental phenomena that arise in complex networks and networked systems is still limited. My vision is to develop a data science for networks and dynamical systems that will contribute to addressing this challenge, by combining data-driven and model-based approaches, using the language of graphs and networks. In this talk, we will give a brief overview of such a Data Science for Networks. We first discuss how networks appear naturally within models in different research domains and illustrate the underlying scientific questions via examples drawn from applications. We then examine in some detail the problem of feature learning from graphs with unobserved edges, in which we aim to learn certain aspects of a graph solely from dynamical observations on its nodes, without knowledge of the edge-set of the graph. We conclude with a brief outlook on future challenges and open problems. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 0