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1,516 results on '"Zimmermann, Hans"'

1. Virtual Reality for Home-Based Citizen Participation in Urban Planning – An Exploratory User Study

Author

Guler, Martin, Bekiri, Valmir, Baldauf, Matthias, Zimmermann, Hans-Dieter, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Nah, Fiona Fui-Hoon, editor, and Siau, Keng Leng, editor

Published
2024
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2. On Error Correction Neural Networks for Economic Forecasting

Author

Mvubu, Mhlasakululeka, Kabuga, Emmanuel, Plitz, Christian, Bah, Bubacarr, Becker, Ronnie, and Zimmermann, Hans Georg

Subjects
Computer Science - Machine Learning, Mathematics - Dynamical Systems, Statistics - Machine Learning, 37M10, 62M10, 91B84, I.2.6, I.5.1, I.5.4
Abstract

Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data. In practice all the external variables driving such systems are not known a priori, especially in economical forecasting. A class of RNNs called Error Correction Neural Networks (ECNNs) was designed to compensate for missing input variables. It does this by feeding back in the current step the error made in the previous step. The ECNN is implemented in Python by the computation of the appropriate gradients and it is tested on stock market predictions. As expected it out performed the simple RNN and LSTM and other hybrid models which involve a de-noising pre-processing step. The intuition for the latter is that de-noising may lead to loss of information., Comment: 13 pages, 4 figures, 11 tables

Published
2020

4. XIV. Wissen, Kultur, Gesellschaft, Medien

Author

D’Aprile, Iwan-Michelangelo, primary, Sinn, Christian, additional, Hoffmann, Nora, additional, Stauffer, Isabelle, additional, Stüssel, Kerstin, additional, Adams, Barbara Helena, additional, Patrut, Iulia-Karin, additional, Arasteh-Roodsary, Sona, additional, Igl, Natalia, additional, Parr, Rolf, additional, Moser, Natalie, additional, Reisener, Marius, additional, Brechenmacher, Thomas, additional, Fischer, Hubertus, additional, Podewski, Madleen, additional, Murnane, Barry, additional, Gerber, Jutta, additional, Wege, Sophia, additional, Zimmermann, Hans Dieter, additional, Gretz, Daniela, additional, Menzel, Julia, additional, Voß, Torsten, additional, Breyer, Till, additional, and Eulig, Emely Sophia, additional

Published
2023
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12. Towards Conversational E-Government : An Experts’ Perspective on Requirements and Opportunities of Voice-Based Citizen Services

Author

Baldauf, Matthias, Zimmermann, Hans-Dieter, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nah, Fiona Fui-Hoon, editor, and Siau, Keng, editor

Published
2020
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13. Introducing ProsperNN—a Python package for forecasting with neural networks.

Author

Beck, Nico, Schemm, Julia, Ehrig, Claudia, Sonnleitner, Benedikt, Neumann, Ursula, and Zimmermann, Hans Georg

Subjects
ECONOMIC forecasting, RECURRENT neural networks, BUSINESS forecasting, PYTHON programming language, RESEARCH personnel
Abstract

We present the package prosper_nn, that provides four neural network architectures dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn contains the first sensitivity analysis suitable for recurrent neural networks (RNN) and a heatmap to visualize forecasting uncertainty, which was previously only available in Java. These models and methods have successfully been in use in industry for two decades and were used and referenced in several scientific publications. However, only now we make them publicly available on GitHub, allowing researchers and practitioners to benchmark and further develop them. The package is designed to make the models easily accessible, thereby enabling research and application in various fields like demand and macroeconomic forecasting. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Evaluation of an eParticipation Project Against eParticipation Success Factors

Author

Zimmermann, Hans-Dieter, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Chugunov, Andrei, editor, Misnikov, Yuri, editor, Roshchin, Evgeny, editor, and Trutnev, Dmitrii, editor

Published
2019
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17. The ROSAT All-Sky Survey Bright Source Catalogue

Author

Voges, Wolfgang, Aschenbach, Bernd, Boller, Thomas, Braeuninger, Heinrich, Briel, Ulrich, Burkert, Wolfgang, Dennerl, Konrad, Englhauser, Jakob, Gruber, Rainer, Haberl, Frank, Hartner, Gisela, Hasinger, Guenther, Pfeffermann, Elmar, Pietsch, Wolfgang, Predehl, Peter, Rosso, Cristina, Schmitt, Juergen H. M. M., Truemper, Joachim, and Zimmermann, Hans-Ulrich

Subjects
Astrophysics
Abstract

We present the ROSAT All-Sky Survey Bright Source Catalogue (RASS-BSC, revision 1RXS) derived from the all-sky survey performed during the first half year (1990/91) of the ROSAT mission. 18,811 sources are catalogued (i) down to a limiting ROSAT PSPC count-rate of 0.05 cts/s in the 0.1-2.4 keV energy band, (ii) with a detection likelihood of at least 15 and (iii) at least 15 source counts. The 18,811 sources underwent both an automatic validation and an interactive visual verification process in which for 94% of the sources the results of the standard processing were confirmed. The remaining 6% have been analyzed using interactive methods and these sources have been flagged. Flags are given for (i) nearby sources; (ii) sources with positional errors; (iii) extended sources; (iv) sources showing complex emission structures; and (v) sources which are missed by the standard analysis software. Broad band (0.1-2.4 keV) images are available for sources flagged by (ii), (iii) and (iv). For each source the ROSAT name, position in equatorial coordinates, positional error, source count-rate and error, background count-rate, exposure time, two hardness-ratios and errors, extent and likelihood of extent, likelihood of detection, and the source extraction radius are provided. At a brightness limit of 0.1 cts/s (8,547 sources) the catalogue represents a sky coverage of 92%. The RASS-BSC, the table of possible identification candidates, and the broad band images are available in electronic form (Voges et al. 1996a) via http://wave.xray.mpe.mpg.de/rosat/catalogues/rass-bsc ., Comment: 17 pages, 18 figures, corrected fig. 3b, to appear in A&A 349, 389-405 (1999), version with colour plots available from http://wave.xray.mpe.mpg.de/rosat/catalogues/rass-bsc/pub/rbsc-paper.ps.gz

Published
1999

22. Digitales Entwicklungsmodell smarter Städte

Author

Meier, Andreas, Zimmermann, Hans-Dieter, Fröschle, Hans-Peter, Section editor, Hofmann, Josephine, Series editor, Knoll, Matthias, Series editor, Meier, Andreas, Series editor, Meinhard, Stefan, Series editor, Reinheimer, Stefan, Series editor, Robra-Bissantz, Susanne, Series editor, Strahringer, Susanne, Series editor, and Portmann, Edy, editor

Published
2016
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