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Mai 2016


Informatik-Kolloquium: Das Human - Brain - Projekt HBP der EU – Stand und Ausblick

01.06.2016, 15:00 Uhr (Rotunde des Jülich Supercomputer Centre am FZJ)

Referent: Prof. Dr-Ing. habil. Alois Knoll, Lehrstuhl Echtzeitsysteme, TU München

Das Brain-Projekt mit seinem außergewöhnlichen Etat von ca. 1.2 Milliarden € und seiner visionären Zielsetzung wird Ihnen schon zu Ohren gekommen sein. Prof. Knoll koordiniert ist das Teilprojekt Neurorobotics und ist damit einer der führenden Personen des Brain Projekts. Er trägt am 1. Juni vor, s. beiliegendes Plakat.

Der Vortrag findet am FZJ in Jülich statt und ist gleichzeitig ein Baustein der Initiative des Clusters Informatik der Region Aachen, bestehend aus Regionalgruppe Informatik Aachen der Gesellschaft für Informatik (RIA), des Regionalen Industrieclubs Informatik Aachen (Regina) und der Fachgruppe Informatik der RWTH. Die von dem Cluster veranstalteten Vorträge zielen darauf ab, neue Ansätze und Probleme aus der Informatik einem breiteren Publikum zu präsentieren.

Weitere Informationen

24.05.2016, mbr

Informatik-Oberseminar: Improvements in Language and Translation Modeling

02.06.2016, 15:30 Uhr (Informatik-Zentrum, Raum 9220, E3, Ahornstr. 55)

Referent: Dipl.-Inform. Martin Sundermeyer


Virtually any modern speech recognition system relies on count-based language models. In this work, such models are investigated, and potential improvements are analyzed with respect to optimized discounting methods, where empirical count statistics are adjusted such that they better match unseen held-out data. Neural networks have recently emerged as a promising alternative to count-based approaches. This work introduces the recurrent long short-term memory neural network into the field of language modeling, and provides an in-depth comparison with other neural network technologies as well as count-based approaches. Due to the high computational complexity, neural network models are combined with several speed-up techniques. Furthermore, novel approximations for direct word error minimization in lattice rescoring are proposed, too. Finally, long short-term memory neural network language models are generalized to the translation modeling problem of statistical machine translation. Two novel methods are presented, preserving word order of source and target language dependences. Both approaches allow the integration in state-of-the-art machine translation systems, complementing the improvements obtained by neural network language models.

Es laden ein: Die Dozenten der Informatik

11.04.2016, sts

Informatik-Oberseminar: Solving the Differential Peak Calling Problem in ChIP-seq Data

07.06.2016, 16:00 Uhr (Informatik-Zentrum, Raum 5053.2 (ggü. AH 6) , Ahornstr. 55)

Referent: Manuel Allhoff, M. Sc.


Gene expression is the process of selectively reading genetic information and it describes a life-essential mechanism in all known living organisms. Key players in the regulation of gene expression are proteins that interact with DNA. DNA-protein interaction sites are nowadays analyzed in a genome wide manner with chromatin immunoprecipitation followed by sequencing (ChIP-seq). The study of changes in protein-DNA interactions measured by ChIP-seq on dynamic systems, such as cell differentiation, response to treatments or the comparison of healthy and diseased individuals, is still an open challenge. There are few computational methods comparing changes in repliacted ChIP-seq signals. Moreover, none of these previous approaches addresses ChIP-seq specific experimental artifacts. In this thesis, we propose ODIN and THOR, HMM-based methods to determine changes of protein-DNA complexes for distinct cellular conditions in ChIP-seq experiments without and with replicates. We furthermore propose a novel signal normalization approach based on housekeeping genes to deal with cases where replicates have distinct signal-to-noise ratios. To evaluate differential peak calling methods, we delineate a methodology comprising the use of both biological and simulated data. In particular, we evaluate THOR and its competing methods on data sets with distinct characteristics from in vitro studies with technical replicates to clinical studies of cancer patients. Our evaluation analysis of 12 differential peak calling problems indicates that THOR performs best in all scenarios.

Es laden ein: Die Dozenten der Informatik

04.05.2016, sts

PromotionsCafé: Der akademische Werdegang: Chancen und Hürden auf dem Weg zur Professorin und zum Professor

08.06.2016, 16:00-17:30 Uhr (Informatikzentrum, Raum 9220, E3, Ahornstraße 55)

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19.05.2016, mbr

Informatik-Kolloquium: Learning Behaviour Models of Discrete Event Production Systems from Observing Input/Output Signals to support the Evolution Management by Semi-Automated Requirement Verification

13.07.2016, 16:00 Uhr (Informatik-Zentrum, AH 4, Ahornstr. 55)

Referent: Univ.-Prof. Dr.-Ing. Alexander Fay Helmut-Schmidt-Universität, Hamburg


Production plants are usually kept in operation for several decades. During this long operational phase, operation requirements and other production conditions change frequently. Accordingly, the plants have to be adjusted in behavior and/or structure by adapting software and physics of the plant to avoid degeneration. Unfortunately, in industrial practice, changes, especially smaller ones, are often performed ad-hoc without appropriate adaptation of formal models or documentation. As a consequence, knowledge about the process is only implicitly available and an evaluation of performed changes is often omitted, resulting in sub-optimal production performance.

The approach presented here is based on learning models from observation of input / output signals of the production plant's control system. Semantics are added by using a priori information modelling which is less tedious compared to modelling the process itself.

Learning behavior models out of event traces has been tackled in a wide variety of scientific projects and publications. Usually the resulting models are used for fault detection, reengineering, and analysis. But in practical applications, like monitoring, learned models can show high complexity and permissivity which makes it difficult to use these models and results tend to be ambiguous.

In our approach, we focused on the automatic creation of so-called Machine State Petri Nets (MSPN) and Material Flow Petri Nets (MFPN). In combination, these two can reveal interesting properties of the monitored production system.

The learned models are used to automatically detect changes by continuously comparing their behavior with real plant behavior during operation (and, thus, to continuously verify the fulfillment of (non-functional) requirements) as well as to evaluate performed changes. An analysis of the models results in high-level property values such as key performance indicators or flexibility measures of the production system. In an example application to a Pick and Place unit, the concept has been applied together with an anomaly detection method to support the operator during the evolution process by constantly providing information regarding requirement fulfillment.

Es laden ein: Die Dozenten der Informatik

13.05.2016, sts

Aachen 2025 - Digitalen Wandel erleben

23.09.2016 - 25.09.2016

Die Digitalisierung des Alltags schreitet voran. Neue Technologien, die unser Leben beeinflussen und verändern, entstehen im Stundentakt. Wie nehmen diese Technologien Einfluss auf mein Leben? Wie wird Aachen in 10 Jahren sein – im Jahr 2025?

Zeit, sich mit den Chancen, auch mit den Herausforderungen, die daraus erwachsen, auf spannende, unterhaltsame und mitreißende Art zu beschäftigen.

Aachen 2025 wird unterstützt von Prof. Stefan Kowalewski und Prof. Manfred Nagl.

Weitere Informationen: www.aachen2025.de

14.12.2015, mbr