November 2017 - ag-beuchler@ins.uni-bonn.de

[Ag-beuchler] [Ins-mitarbeiter] Fwd: Upcoming courses Nov 2017 - Feb 2018
by Babette Weisskopf 03 Nov '17

03 Nov '17

-------- Weitergeleitete Nachricht -------- Betreff: Upcoming courses Nov 2017 - Feb 2018 Datum: Thu, 2 Nov 2017 14:51:19 +0100 (CET) Von: Rolf Rabenseifner <rabenseifner(a)hlrs.de> Antwort an: Rolf Rabenseifner <rabenseifner(a)hlrs.de>, Lucienne Dettki <dettki(a)hlrs.de> An: contact(a)ins.uni-bonn.de Dear Madam or Sir / Sehr geehrte Damen und Herren, there are still free slots in our visualization course for some short term registrations. Here a list of upcoming courses with open registration: - Scientific Visualization (Nov 06-07) Stuttgart [English] http://www.hlrs.de/training/2017/VIS2 - Parallelization with MPI (Nov 20-22) Vienna [English] http://www.hlrs.de/training/2017/VSC3 - Shared memory parallelization with OpenMP (Nov 23-24) Vienna [Eng.] http://www.hlrs.de/training/2017/VSC4 - Fortran for Scientific Computing (Nov 27-Dec 01) Stuttgart [Ger.] http://www.hlrs.de/training/2017/FTN2 - Introduction to Hybrid Programming in HPC (Jan 18) Garching [Eng] http://www.hlrs.de/training/2018/HY-G - Parallel Programming with MPI, OpenMP, and Tools (Feb 12-16) Dresden [Ger.] http://www.hlrs.de/training/2018/ZIH Further courses, see http://www.hlrs.de/training/ Please, can you pass this course announcement also to interested colleagues. / Es waere schoen, wenn Sie diese Ankuendigung auch an interessierte Kollegen weitergeben koennten. Kind regards / Mit freundlichen Gruessen Rolf Rabenseifner & Lucienne Dettki --------------------------------------------------------------------- HLRS Online Courses: http://www.hlrs.de/training/par-prog-ws/ As a member of the HLRS course mailing list, you should have received the password with an email titled "Get your access to HLRS online courses". --------------------------------------------------------------------- --------------------------------------------------------------------- We would appreciate if you could forward this email (without the following personal subscription-paragraph) to interested colleagues. --------------------------------------------------------------------- --------------------------------------------------------------------- Based on an upcoming law change, as mentioned in my prior email from Sep. 14, 2017, we kindly ask you to follow this link http://java.hlrs.de/subscription/elist?addr=contact@ins.uni-bonn.de&chk=208… to acknowledge your interest in continuing to receive mailings from HLRS. Please also note that if you do not acknowledge your agreement, it will no longer be possible for you to access our online course recordings. If you receive double postings or you want to stop my postings, then please unsubscribe at any time through visiting http://java.hlrs.de/subscription/elist?addr=contact@ins.uni-bonn.de&chk=208… --------------------------------------------------------------------- --------------------------------------------------------------------- Dr. Rolf Rabenseifner .. . . . . . . . . . email rabenseifner(a)hlrs.de Lucienne Dettki . . . .. . . . . . . . . . . . . . . . dettki(a)hlrs.de High Performance Computing Center (HLRS) . phone ++49(0)711/685-65530 University of Stuttgart .. . . . . . . . . and : ++49(0)711/685-63894 Nobelstr. 19, D-70569 Stuttgart, Germany --------------------------------------------------------------------- _______________________________________________ Ins-mitarbeiter mailing list Ins-mitarbeiter(a)ins.uni-bonn.de https://mail.ins.uni-bonn.de/mailman/listinfo/ins-mitarbeiter

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[Ag-beuchler] [Ins-mitarbeiter] Fwd: Fwd: Re: Hausdorff Forum, 03 November 2017, 14:15h
by Babette Weißkopf 03 Nov '17

03 Nov '17

-------- Weitergeleitete Nachricht -------- Betreff: Fwd: Re: Hausdorff Forum, 03 November 2017, 14:15h Datum: Fri, 3 Nov 2017 08:55:15 +0100 Von: Gunder Sievert <gunder-lily.sievert(a)hcm.uni-bonn.de> An: weisskopf(a)ins.uni-bonn.de Liebe Frau Weisskopf, bitte informieren Sie doch Ihre Mitarbeiter über u.s. Veranstaltung. Herzlichen Dank Gunder Sievert ______________________________________________ Gunder-Lily Sievert Hausdorff Center for Mathematics Rheinische Friedrich-Wilhelms-Universität Bonn Endenicher Allee 60, room 3.026 53115 Bonn Phone: +49(0)228 7362358 www.hcm.uni-bonn.de > English Version Below > > ------------------------------------------------------------------------------------------------------------------------------------------------ > > Sehr geehrte Damen und Herren, > > wir möchten Sie an das heutige Hausdorff Forum, im Mathematik-Zentrum > der Universität Bonn, Lipschitz Saal, Endenicher Allee 60 hinweisen. > > 14:15h Jürgen Gall, Universität Bonn: Analyzing Human Behavior in > Video Sequences > > Abstract: Analyzing the behavior of humans in continuous video > recordings requires to solve several tasks. This includes the > estimation and tracking of poses of multiple persons or the temporal > detection of activities. In this talk, I will describe some of our > recent works for this two tasks. The first task requires to solve > jointly the problem of person association over time and the pose > estimation for each person. This problem can be formulated as a graph > partitioning problem where a spatio-temporal graph is constructed from > detected body joints in a video. For the second task, temporal models > like recurrent neural networks are usually trained on videos that are > annotated at a frame-level. Acquiring such annotations, however, is > very time consuming and strong temporal models require large amounts > of annotated training data. Weaker forms of supervision like > transcripts are therefore investigated to learn temporal models. > > 15:15-15:45 Teepause > > > 15:15 h William A. P. Smith, University of York, UK: Model-based > analysis of faces > > Abstract: The quest to understand and model "face space" dates back to > the 1980s, though the variability and uniqueness of faces has > fascinated scholars, artists and scientists since antiquity. To learn > a face space from a sample of face data requires the factors that are > intrinsic to the face to be disentangled from extrinsic factors > related to the imaging environment. A model-based approach to analysis > of face images uses explicit models of the geometric and photometric > image formation processes in order to explain an image in terms of > factors such as shape, lighting and skin reflectance properties. This > is in contrast to learning-based approaches where a black box (usually > a convolutional neural network) is trained to directly classify or > regress some property of interest from an image. In this talk, I will > present a variety of work on model-based analysis of faces, describe > current work to try to integrate explicit models into black box > learning, present some applications including evaluation of > craniofacial surgical outcomes and discuss a collaboration with > psychologists to try to uncover the models and representations used in > human perception of faces. > > Mit freundlichen Grüßen > > Gunder-Lily Sievert > > ------------------------------------------------------------------------------------------------------------------------------------------------ > > > Dear Ladies and Gentlemen, > > > We are pleased to give you advance notice of today's Hausdorff Forum. > > Location: Lipschitz Hall, Endenicher Allee 60 > > > 14:15h Jürgen Gall, Universität Bonn: Analyzing Human Behavior in > Video Sequences > > Abstract: Analyzing the behavior of humans in continuous video > recordings requires to solve several tasks. This includes the > estimation and tracking of poses of multiple persons or the temporal > detection of activities. In this talk, I will describe some of our > recent works for this two tasks. The first task requires to solve > jointly the problem of person association over time and the pose > estimation for each person. This problem can be formulated as a graph > partitioning problem where a spatio-temporal graph is constructed from > detected body joints in a video. For the second task, temporal models > like recurrent neural networks are usually trained on videos that are > annotated at a frame-level. Acquiring such annotations, however, is > very time consuming and strong temporal models require large amounts > of annotated training data. Weaker forms of supervision like > transcripts are therefore investigated to learn temporal models. > > 15:15-15:45 Teepause > > 15:15 h William A. P. Smith, University of York, UK: Model-based > analysis of faces > > Abstract: The quest to understand and model "face space" dates back to > the 1980s, though the variability and uniqueness of faces has > fascinated scholars, artists and scientists since antiquity. To learn > a face space from a sample of face data requires the factors that are > intrinsic to the face to be disentangled from extrinsic factors > related to the imaging environment. A model-based approach to analysis > of face images uses explicit models of the geometric and photometric > image formation processes in order to explain an image in terms of > factors such as shape, lighting and skin reflectance properties. This > is in contrast to learning-based approaches where a black box (usually > a convolutional neural network) is trained to directly classify or > regress some property of interest from an image. In this talk, I will > present a variety of work on model-based analysis of faces, describe > current work to try to integrate explicit models into black box > learning, present some applications including evaluation of > craniofacial surgical outcomes and discuss a collaboration with > psychologists to try to uncover the models and representations used in > human perception of faces. > > > Best regards, > Gunder-Lily Sievert > -- > > ______________________________________________ > > Gunder-Lily Sievert > > Hausdorff Center for Mathematics > > Rheinische Friedrich-Wilhelms-Universität Bonn > > Endenicher Allee 60, room 3.026 > > 53115 Bonn > > Phone: +49(0)228 7362358 > > www.hcm.uni-bonn.de > > _______________________________________________ Ins-mitarbeiter mailing list Ins-mitarbeiter(a)ins.uni-bonn.de https://mail.ins.uni-bonn.de/mailman/listinfo/ins-mitarbeiter

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[Ag-beuchler] [Ins-wegelerstrasse] Abschlussvortrag zur Masterarbeit 3.11.
by garcke.ins.uni-bonn.de 01 Nov '17

01 Nov '17

Hallo zusammen, jetzt mit richtigen Subject. am Freitag, 3.11. findet um 10 Uhr (c.t.) der Abschlussvortrag zur Masterarbeit von Timm Ruland statt. Raum ist 6.020, Titel ist Quadrature Appeoximation for Feature Maps in Kernel Methods Interessenten sind gerne willkommen. Viele Grüße, Jochen (Garcke) -- Prof. Dr. Jochen Garcke garcke(a)ins.uni-bonn.de jochen.garcke(a)scai.fraunhofer.de phone: +49-228 73 60451 phone: +49-2241 14 2286 http://garcke.ins.uni-bonn.de http://scai.fraunhofer.de/ndv Universität Bonn Fraunhofer-Institut SCAI Institute for Numerical Simulation Numerical Data-Driven Prediction Wegelerstr. 6 Schloss Birlinghoven 53115 Bonn, Germany 53754 Sankt Augustin, Germany _______________________________________________ Ins-wegelerstrasse mailing list Ins-wegelerstrasse(a)ins.uni-bonn.de https://mail.ins.uni-bonn.de/mailman/listinfo/ins-wegelerstrasse

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[Ag-beuchler] [Ins-wegelerstrasse] Abschlussvortrag zur Masterarbeit 23.10.
by garcke.ins.uni-bonn.de 01 Nov '17

01 Nov '17

Hallo zusammen, am Freitag, 3.11. findet um 10 Uhr (c.t.) der Abschlussvortrag zur Masterarbeit von Timm Ruland statt. Raum ist 6.020, Titel ist Quadrature Appeoximation for Feature Maps in Kernel Methods Interessenten sind gerne willkommen. Viele Grüße, Jochen (Garcke) -- Prof. Dr. Jochen Garcke garcke(a)ins.uni-bonn.de jochen.garcke(a)scai.fraunhofer.de phone: +49-228 73 60451 phone: +49-2241 14 2286 http://garcke.ins.uni-bonn.de http://scai.fraunhofer.de/ndv Universität Bonn Fraunhofer-Institut SCAI Institute for Numerical Simulation Numerical Data-Driven Prediction Wegelerstr. 6 Schloss Birlinghoven 53115 Bonn, Germany 53754 Sankt Augustin, Germany _______________________________________________ Ins-wegelerstrasse mailing list Ins-wegelerstrasse(a)ins.uni-bonn.de https://mail.ins.uni-bonn.de/mailman/listinfo/ins-wegelerstrasse

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