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@article{KluBra2024a,
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%1 KluBra2024a
%A Kluger, Florian
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%A Yang, Michael Ying
%A Rosenhahn, Bodo
%D 2024
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%T Robust Shape Fitting for 3D Scene Abstraction - BibTeXEndNoteBibSonomyMaier, H. B., Neyazi, A., Bundies, G. L., Meyer-Bockenkamp, F., Bleich, S., Pathak, H., Ziert, Y., Neuhaus, B., M{ü}ller, F.-J., Pollmann, I., Illig, T., M{ü}cke, S., M{ü}ller, M., M{ö}ller, B. K., Oeltze-Jafra, S., Kacprowski, T., Voges, J., M{ü}ntefering, F., Scheiber, J., Reif, A., Aichholzer, M., Reif-Leonhard, C., Schmidt-Kassow, M., Hegerl, U., Reich, H., Unterecker, S., Weber, H., Deckert, J., B{ö}ssel-Debbert, N., Grabe, H. J., Lucht, M., and Frieling, H. (2024)Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial, Trials 25.
@article{MaiNey2024a,
author = {Maier, Hannah Benedictine and Neyazi, Alexandra and Bundies, Gabriel L. and Meyer-Bockenkamp, Fiona and Bleich, Stefan and Pathak, Hansi and Ziert, Yvonne and Neuhaus, Barbara and M{ü}ller, Franz-Josef and Pollmann, Iris and Illig, Thomas and M{ü}cke, Stefanie and M{ü}ller, Meike and M{ö}ller, Brinja Kira and Oeltze-Jafra, Steffen and Kacprowski, Tim and Voges, Jan and M{ü}ntefering, Fabian and Scheiber, Josef and Reif, Andreas and Aichholzer, Mareike and Reif-Leonhard, Christine and Schmidt-Kassow, Maren and Hegerl, Ulrich and Reich, Hanna and Unterecker, Stefan and Weber, Heike and Deckert, J{ü}rgen and B{ö}ssel-Debbert, Nicole and Grabe, Hans J. and Lucht, Michael and Frieling, Helge},
journal = {Trials},
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%1 MaiNey2024a
%A Maier, Hannah Benedictine
%A Neyazi, Alexandra
%A Bundies, Gabriel L.
%A Meyer-Bockenkamp, Fiona
%A Bleich, Stefan
%A Pathak, Hansi
%A Ziert, Yvonne
%A Neuhaus, Barbara
%A M{ü}ller, Franz-Josef
%A Pollmann, Iris
%A Illig, Thomas
%A M{ü}cke, Stefanie
%A M{ü}ller, Meike
%A M{ö}ller, Brinja Kira
%A Oeltze-Jafra, Steffen
%A Kacprowski, Tim
%A Voges, Jan
%A M{ü}ntefering, Fabian
%A Scheiber, Josef
%A Reif, Andreas
%A Aichholzer, Mareike
%A Reif-Leonhard, Christine
%A Schmidt-Kassow, Maren
%A Hegerl, Ulrich
%A Reich, Hanna
%A Unterecker, Stefan
%A Weber, Heike
%A Deckert, J{ü}rgen
%A B{ö}ssel-Debbert, Nicole
%A Grabe, Hans J.
%A Lucht, Michael
%A Frieling, Helge
%D 2024
%J Trials
%N 247
%R 10.1186/s13063-024-08061-5
%T Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial
%V 25 - BibTeXEndNoteBibSonomyChen, Y.-H., Cheng, C.-W., Benjak, M., and Peng, W.-H. (2024)Progress report on MaskCRT, 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66976.
@article{CheChe2024,
author = {Chen, Yi-Hsin and Cheng, Chen-Wei and Benjak, Martin and Peng, Wen-Hsiao},
journal = {15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66976},
keywords = {MaskCRT},
month = {04},
title = {Progress report on MaskCRT},
year = 2024
}%0 Journal Article
%1 CheChe2024
%A Chen, Yi-Hsin
%A Cheng, Chen-Wei
%A Benjak, Martin
%A Peng, Wen-Hsiao
%D 2024
%J 15th Meeting of ISO/IEC JTC 1/SC 29/AG 5 Document m66976
%T Progress report on MaskCRT - BibTeXEndNoteBibSonomyKluger, F., and Rosenhahn, B. (2024)PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus. In AAAI.
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author = {Kluger, Florian and Rosenhahn, Bodo},
booktitle = {AAAI},
keywords = {PARSAC},
month = {02},
title = {PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus},
year = 2024
}%0 Conference Paper
%1 KluRos2024a
%A Kluger, Florian
%A Rosenhahn, Bodo
%B AAAI
%D 2024
%T PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus - URLBibTeXEndNoteBibSonomyTang, M., Antić, Željko, Fardzadeh, P., Pietzsch, S., Schröder, C., Eberhardt, A., van Bömmel, A., Escherich, G., Hofmann, W., Horstmann, M. A., Illig, T., McCrary, J. M., Lentes, J., Metzler, M., Nejdl, W., Schlegelberger, B., Schrappe, M., Zimmermann, M., Miarka-Walczyk, K., Patsorczak, A., Cario, G., Renard, B. Y., Stanulla, M., and Bergmann, A. K. (2024)An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia, eBioMedicine, Elsevier BV 104, 105171.
@article{Tang_2024,
author = {Tang, Ming and Antić, Željko and Fardzadeh, Pedram and Pietzsch, Stefan and Schröder, Charlotte and Eberhardt, Adrian and van Bömmel, Alena and Escherich, Gabriele and Hofmann, Winfried and Horstmann, Martin A. and Illig, Thomas and McCrary, J. Matt and Lentes, Jana and Metzler, Markus and Nejdl, Wolfgang and Schlegelberger, Brigitte and Schrappe, Martin and Zimmermann, Martin and Miarka-Walczyk, Karolina and Patsorczak, Agata and Cario, Gunnar and Renard, Bernhard Y. and Stanulla, Martin and Bergmann, Anke Katharina},
journal = {eBioMedicine},
keywords = {l3s},
month = {06},
pages = 105171,
publisher = {Elsevier BV},
title = {An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia},
volume = 104,
year = 2024
}%0 Journal Article
%1 Tang_2024
%A Tang, Ming
%A Antić, Željko
%A Fardzadeh, Pedram
%A Pietzsch, Stefan
%A Schröder, Charlotte
%A Eberhardt, Adrian
%A van Bömmel, Alena
%A Escherich, Gabriele
%A Hofmann, Winfried
%A Horstmann, Martin A.
%A Illig, Thomas
%A McCrary, J. Matt
%A Lentes, Jana
%A Metzler, Markus
%A Nejdl, Wolfgang
%A Schlegelberger, Brigitte
%A Schrappe, Martin
%A Zimmermann, Martin
%A Miarka-Walczyk, Karolina
%A Patsorczak, Agata
%A Cario, Gunnar
%A Renard, Bernhard Y.
%A Stanulla, Martin
%A Bergmann, Anke Katharina
%D 2024
%I Elsevier BV
%J eBioMedicine
%P 105171
%R 10.1016/j.ebiom.2024.105171
%T An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia
%U http://dx.doi.org/10.1016/j.ebiom.2024.105171
%V 104 - BibTeXEndNoteBibSonomyLee, C.-S., Wang, M.-H., Chen, C.-Y., Yang, F.-J., and Dockhorn, A. (2023)Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience. In 2023 IEEE Congress on Evolutionary Computation, pp. 1–8.
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author = {Lee, Chang-Shing and Wang, Mei-Hui and Chen, Chih-Yu and Yang, Fu-Jie and Dockhorn, Alexander},
booktitle = {2023 IEEE Congress on Evolutionary Computation},
keywords = {Student},
note = {(to be published)},
pages = {1-8},
title = {Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience},
year = 2023
}%0 Conference Paper
%1 LeeWan2023
%A Lee, Chang-Shing
%A Wang, Mei-Hui
%A Chen, Chih-Yu
%A Yang, Fu-Jie
%A Dockhorn, Alexander
%B 2023 IEEE Congress on Evolutionary Computation
%D 2023
%P 1-8
%T Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience - BibTeXEndNoteBibSonomyKuhnke, F., and Ostermann, J. (2023)Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency, IEEE Transactions on Biometrics, Behavior, and Identity Science.
@article{KuhOst2023,
author = {Kuhnke, Felix and Ostermann, J{ö}rn},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
keywords = {Adaptation},
title = {Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency},
year = 2023
}%0 Journal Article
%1 KuhOst2023
%A Kuhnke, Felix
%A Ostermann, J{ö}rn
%D 2023
%J IEEE Transactions on Biometrics, Behavior, and Identity Science
%R 10.1109/TBIOM.2023.3237039
%T Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency - BibTeXEndNoteBibSonomyKaiser, T., Reinders, C., and Rosenhahn, B. (2023)Compensation Learning in Semantic Segmentation. In Computer Vision and Pattern Recognition Workshops (CVPRW).
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author = {Kaiser, Timo and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {Computer Vision and Pattern Recognition Workshops (CVPRW)},
keywords = {Segmention},
month = {06},
title = {Compensation Learning in Semantic Segmentation},
year = 2023
}%0 Conference Paper
%1 KaiRei2023a
%A Kaiser, Timo
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B Computer Vision and Pattern Recognition Workshops (CVPRW)
%D 2023
%T Compensation Learning in Semantic Segmentation - BibTeXEndNoteBibSonomyAdhisantoso, Y. G., and Voges, J. (2023)Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree, ISO/IEC JTC 1/SC 29/WG 8.
@article{AdhVog2023a,
author = {Adhisantoso, Yeremia Gunawan and Voges, Jan},
journal = {ISO/IEC JTC 1/SC 29/WG 8},
keywords = {M62859},
month = {04},
title = {Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree},
year = 2023
}%0 Journal Article
%1 AdhVog2023a
%A Adhisantoso, Yeremia Gunawan
%A Voges, Jan
%D 2023
%J ISO/IEC JTC 1/SC 29/WG 8
%T Cross-check of M62859 Results on Updated CE Results for Annotation Data Indexing Using B-Tree - URLBibTeXEndNoteBibSonomyBenjamins, C., Eimer, T., Schubert, F., Mohan, A., D{ö}hler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2023)Contextualize Me - The Case for Context in Reinforcement Learning, Transactions on Machine Learning Research.
@article{BenEim2023,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and D{ö}hler, Sebastian and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Transactions on Machine Learning Research},
keywords = {reinforcement},
month = {06},
title = {Contextualize Me - The Case for Context in Reinforcement Learning},
year = 2023
}%0 Journal Article
%1 BenEim2023
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A D{ö}hler, Sebastian
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2023
%J Transactions on Machine Learning Research
%T Contextualize Me - The Case for Context in Reinforcement Learning
%U https://arxiv.org/abs/2202.04500 - AbstractBibTeXEndNoteBibSonomyRoy, S., Wallat, J., Sundaram, S. S., Nejdl, W., and Ganguly, N. (2023)GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning. In Frontiers in Artificial Intelligence and Applications, pp. 2002–2009.Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
@inproceedings{noauthororeditor,
abstract = {Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.},
author = {Roy, Soumyadeep and Wallat, Jonas and Sundaram, Sowmya S and Nejdl, Wolfgang and Ganguly, Niloy},
keywords = {l3s},
pages = {2002-2009},
series = {Frontiers in Artificial Intelligence and Applications},
title = {GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning},
volume = 372,
year = 2023
}%0 Conference Paper
%1 noauthororeditor
%A Roy, Soumyadeep
%A Wallat, Jonas
%A Sundaram, Sowmya S
%A Nejdl, Wolfgang
%A Ganguly, Niloy
%B Frontiers in Artificial Intelligence and Applications
%D 2023
%P 2002-2009
%R 10.3233/FAIA230492
%T GENEMASK: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning
%V 372
%X Large-scale language models such as DNABert and LOGO aim to learn optimal gene representations and are trained on the entire Human Reference Genome. However, standard tokenization schemes involve a simple sliding window of tokens like k-mers that do not leverage any gene-based semantics and thus may lead to (trivial) masking of easily predictable sequences, and subsequently inefficient Masked Language Modeling (MLM) training. Therefore, we propose a novel masking algorithm, GENEMASK, for MLM training of gene sequences, where we randomly identify positions in a gene sequence as mask centers and locally select the span around the mask center with the highest Normalized Pointwise Mutual Information (NPMI) to mask. We observe that in the absence of human-understandable semantics in the genomics domain (in contrast, semantic units like words and phrases are inherently available in NLP), GENEMASK-based models substantially outperform the SOTA models (DNABert and LOGO) over four benchmark gene sequence classification datasets in five few-shot settings (10 to 1000-shot). More significantly, the GENEMASK-based DNABert model is trained for less than one-tenth of the number of epochs of the original SOTA model. We also observe a strong correlation between top-ranked PMI tokens and conserved DNA sequence motifs, which may indicate the incorporation of latent genomic information. The codes (including trained models) and datasets are made publicly available at unmapped: uri https://github.com/roysoumya/GeneMask.
%@ 978-1-64368-437-6 - BibTeXEndNoteBibSonomySafikhani, P., and Broneske, D. (2023)Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch., International Conference on Machine Learning Techniques and NLP 13.
@article{SafBro2023,
author = {Safikhani, Parisa and Broneske, David},
journal = {International Conference on Machine Learning Techniques and NLP},
keywords = {BERT},
month = {09},
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title = {Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.},
volume = 13,
year = 2023
}%0 Journal Article
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%A Broneske, David
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%J International Conference on Machine Learning Techniques and NLP
%N 16
%T Enhancing AutoNLP with fine-tuned BERT models: An evaluation of text representation methods for AutoPyTorch.
%V 13
%@ 978-1-923107-04-5 - BibTeXEndNoteBibSonomyDockhorn, A., Eberhardinger, M., Loiacono, D., Liebana, D. P., and Veltkamp, R. (2023)Pokegen, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 39–42.
@article{DocEbe2023a,
author = {Dockhorn, Alexander and Eberhardinger, Manuel and Loiacono, Daniele and Liebana, Diego Perez and Veltkamp, Remco},
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}%0 Journal Article
%1 DocEbe2023a
%A Dockhorn, Alexander
%A Eberhardinger, Manuel
%A Loiacono, Daniele
%A Liebana, Diego Perez
%A Veltkamp, Remco
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 39-42
%R 10.4230/DagRep.12.6.28
%T Pokegen
%V 12 - BibTeXEndNoteBibSonomyAwiszus, M., Dockhorn, A., Hoover, A. K., Liapis, A., Lucas, S. M., Eladhari, M. P., Schrum, J., and Volz, V. (2023)Language Models for Procedural Content Generation, Human-Game AI Interaction (Dagstuhl Seminar 22251) 12, 34–37.
@article{AwiDoc2023,
author = {Awiszus, Maren and Dockhorn, Alexander and Hoover, Amy K. and Liapis, Antonios and Lucas, Simon M. and Eladhari, Mirjam Palosaari and Schrum, Jacob and Volz, Vanessa},
journal = {Human-Game AI Interaction (Dagstuhl Seminar 22251)},
keywords = {Language},
month = {01},
number = 6,
pages = {34-37},
title = {Language Models for Procedural Content Generation},
volume = 12,
year = 2023
}%0 Journal Article
%1 AwiDoc2023
%A Awiszus, Maren
%A Dockhorn, Alexander
%A Hoover, Amy K.
%A Liapis, Antonios
%A Lucas, Simon M.
%A Eladhari, Mirjam Palosaari
%A Schrum, Jacob
%A Volz, Vanessa
%D 2023
%J Human-Game AI Interaction (Dagstuhl Seminar 22251)
%N 6
%P 34-37
%R 10.4230/DagRep.12.6.28
%T Language Models for Procedural Content Generation
%V 12 - URLBibTeXEndNoteBibSonomyPoker, Y., von Hardenberg, S., Hofmann, W., Tang, M., Baumann, U., Schwerk, N., Wetzke, M., Lindenthal, V., Auber, B., Schlegelberger, B., Ott, H., von Bismarck, P., Viemann, D., Dressler, F., Klemann, C., and Bergmann, A. K. (2023)Systematic genetic analysis of pediatric patients with autoinflammatory diseases, Frontiers in Genetics, Frontiers Media {SA} 14.
@article{Poker_2023,
author = {Poker, Yvonne and von Hardenberg, Sandra and Hofmann, Winfried and Tang, Ming and Baumann, Ulrich and Schwerk, Nicolaus and Wetzke, Martin and Lindenthal, Viola and Auber, Bernd and Schlegelberger, Brigitte and Ott, Hagen and von Bismarck, Philipp and Viemann, Dorothee and Dressler, Frank and Klemann, Christian and Bergmann, Anke Katharina},
journal = {Frontiers in Genetics},
keywords = {l3s},
month = {01},
publisher = {Frontiers Media {SA}},
title = {Systematic genetic analysis of pediatric patients with autoinflammatory diseases},
volume = 14,
year = 2023
}%0 Journal Article
%1 Poker_2023
%A Poker, Yvonne
%A von Hardenberg, Sandra
%A Hofmann, Winfried
%A Tang, Ming
%A Baumann, Ulrich
%A Schwerk, Nicolaus
%A Wetzke, Martin
%A Lindenthal, Viola
%A Auber, Bernd
%A Schlegelberger, Brigitte
%A Ott, Hagen
%A von Bismarck, Philipp
%A Viemann, Dorothee
%A Dressler, Frank
%A Klemann, Christian
%A Bergmann, Anke Katharina
%D 2023
%I Frontiers Media {SA}
%J Frontiers in Genetics
%R 10.3389/fgene.2023.1065907
%T Systematic genetic analysis of pediatric patients with autoinflammatory diseases
%U https://doi.org/10.3389%2Ffgene.2023.1065907
%V 14 - BibTeXEndNoteBibSonomyPestel-Schiller, U., and Ostermann, J. (2022)Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images. In 14th European Conference on Synthetic Aperture Radar.
@inproceedings{PesOst2022,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {14th European Conference on Synthetic Aperture Radar},
keywords = {of},
month = {07},
title = {Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images},
year = 2022
}%0 Conference Paper
%1 PesOst2022
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 14th European Conference on Synthetic Aperture Radar
%D 2022
%T Impact of Spatial Resolution and Zoom on Interpreter-Based Evaluation of Compressed SAR Images - URLBibTeXEndNoteBibSonomyNayak, T., Sharma, S., Butala, Y., Dasgupta, K., Goyal, P., and Ganguly, N. (2022)A Generative Approach for Financial Causality Extraction. In Companion Proceedings of the Web Conference 2022, {ACM}.
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author = {Nayak, Tapas and Sharma, Soumya and Butala, Yash and Dasgupta, Koustuv and Goyal, Pawan and Ganguly, Niloy},
booktitle = {Companion Proceedings of the Web Conference 2022},
keywords = {"sys:relevantfor:l3s"},
month = {04},
publisher = {{ACM}},
title = {A Generative Approach for Financial Causality Extraction},
year = 2022
}%0 Conference Paper
%1 Nayak_2022
%A Nayak, Tapas
%A Sharma, Soumya
%A Butala, Yash
%A Dasgupta, Koustuv
%A Goyal, Pawan
%A Ganguly, Niloy
%B Companion Proceedings of the Web Conference 2022
%D 2022
%I {ACM}
%R 10.1145/3487553.3524633
%T A Generative Approach for Financial Causality Extraction
%U https://doi.org/10.1145%2F3487553.3524633 - URLBibTeXEndNoteBibSonomyGrimm, E., Kuhnke, F., Gajdt, A., Ostermann, J., and Knoche, M. (2022)Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis, Horticulturae 8.
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author = {Grimm, Eckhard and Kuhnke, Felix and Gajdt, Anna and Ostermann, J{ö}rn and Knoche, Moritz},
journal = {Horticulturae},
keywords = {Flesh},
month = {01},
number = 2,
title = {Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis},
volume = 8,
year = 2022
}%0 Journal Article
%1 GriKuh2022
%A Grimm, Eckhard
%A Kuhnke, Felix
%A Gajdt, Anna
%A Ostermann, J{ö}rn
%A Knoche, Moritz
%D 2022
%J Horticulturae
%N 2
%R https://doi.org/10.3390/horticulturae8020145
%T Accurate Quantification of Anthocyanin in Red Flesh Apples Using Digital Photography and Image Analysis
%U https://www.mdpi.com/2311-7524/8/2/145
%V 8 - BibTeXEndNoteBibSonomyReinders, C., Schubert, F., and Rosenhahn, B. (2022)ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.
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booktitle = {Arxiv Preprint},
keywords = {Feature},
month = {03},
title = {ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing},
year = 2022
}%0 Conference Paper
%1 ReiSch2022
%A Reinders, Christoph
%A Schubert, Frederik
%A Rosenhahn, Bodo
%B Arxiv Preprint
%D 2022
%T ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing - URLBibTeXEndNoteBibSonomyMast, M., Marschollek, M., Jack, T., Wulff, A., and Elise Study, G. (2022)Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data, Stud Health Technol Inform 289, 228–231.
@article{RN11,
author = {Mast, M. and Marschollek, M. and Jack, T. and Wulff, A. and Elise Study, Group},
journal = {Stud Health Technol Inform},
keywords = {l3s},
pages = {228-231},
title = {Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data},
type = {Journal Article},
volume = 289,
year = 2022
}%0 Journal Article
%1 RN11
%A Mast, M.
%A Marschollek, M.
%A Jack, T.
%A Wulff, A.
%A Elise Study, Group
%D 2022
%J Stud Health Technol Inform
%P 228-231
%R 10.3233/SHTI210901
%T Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data
%U https://www.ncbi.nlm.nih.gov/pubmed/35062134
%V 289 - BibTeXEndNoteBibSonomyParker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., and Lindauer, M. (2022)Automated Reinforcement Learning (AutoRL): A Survey and Open Problems, Journal of Artificial Intelligence Research 74 (2022) 517–568.
@article{2201.03916,
author = {Parker-Holder, Jack and Rajan, Raghu and Song, Xingyou and Biedenkapp, André and Miao, Yingjie and Eimer, Theresa and Zhang, Baohe and Nguyen, Vu and Calandra, Roberto and Faust, Aleksandra and Hutter, Frank and Lindauer, Marius},
journal = {Journal of Artificial Intelligence Research 74 (2022)},
keywords = {AutoML},
pages = {517-568},
title = {Automated Reinforcement Learning (AutoRL): A Survey and Open Problems},
year = 2022
}%0 Journal Article
%1 2201.03916
%A Parker-Holder, Jack
%A Rajan, Raghu
%A Song, Xingyou
%A Biedenkapp, André
%A Miao, Yingjie
%A Eimer, Theresa
%A Zhang, Baohe
%A Nguyen, Vu
%A Calandra, Roberto
%A Faust, Aleksandra
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%J Journal of Artificial Intelligence Research 74 (2022)
%P 517-568
%R 10.1613/jair.1.13596
%T Automated Reinforcement Learning (AutoRL): A Survey and Open Problems - BibTeXEndNoteBibSonomyAlshomary, M., El Baff, R., Gurcke, T., and Wachsmuth, H. (2022)The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 8782–8797.
@inproceedings{alshomary2022moral,
author = {Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
keywords = {leibnizailab},
pages = {8782–8797},
title = {The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments},
year = 2022
}%0 Conference Paper
%1 alshomary2022moral
%A Alshomary, Milad
%A El Baff, Roxanne
%A Gurcke, Timon
%A Wachsmuth, Henning
%B Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
%D 2022
%P 8782–8797
%T The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments - BibTeXEndNoteBibSonomyBiedenkapp, A., Speck, D., Sievers, S., Hutter, F., Lindauer, M., and Seipp, J. (2022)Learning Domain-Independent Policies for Open List Selection. In Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), pp. 1–9.
@inproceedings{BieSpe2022,
author = {Biedenkapp, André and Speck, David and Sievers, Silvan and Hutter, Frank and Lindauer, Marius and Seipp, Jendrik},
booktitle = {Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)},
keywords = {leibnizailab},
pages = {1-9},
title = {Learning Domain-Independent Policies for Open List Selection},
year = 2022
}%0 Conference Paper
%1 BieSpe2022
%A Biedenkapp, André
%A Speck, David
%A Sievers, Silvan
%A Hutter, Frank
%A Lindauer, Marius
%A Seipp, Jendrik
%B Proceedings of the 3rd ICAPS workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
%D 2022
%P 1-9
%T Learning Domain-Independent Policies for Open List Selection - BibTeXEndNoteBibSonomyChen, W.-F., Chen, M.-H., Mudgal, G., and Wachsmuth, H. (2022)Analyzing Culture-Specific Argument Structures in Learner Essays. In Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), pp. 51–61.
@inproceedings{Chen_Chen_Mudgal_Wachsmuth_2022,
author = {Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning},
booktitle = {Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)},
keywords = {leibnizailab},
pages = {51–61},
title = {Analyzing Culture-Specific Argument Structures in Learner Essays},
year = 2022
}%0 Conference Paper
%1 Chen_Chen_Mudgal_Wachsmuth_2022
%A Chen, Wei-Fan
%A Chen, Mei-Hua
%A Mudgal, Garima
%A Wachsmuth, Henning
%B Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)
%D 2022
%P 51–61
%T Analyzing Culture-Specific Argument Structures in Learner Essays - URLBibTeXEndNoteBibSonomyBenjamins, C., Jankovic, A., Raponi, E., van der Blom, K., Lindauer, M., and Doerr, C. (2022)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In Workshop on Meta-Learning (MetaLearn 2022).
@inproceedings{benjamins2022towards,
author = {Benjamins, Carolin and Jankovic, Anja and Raponi, Elena and van der Blom, Koen and Lindauer, Marius and Doerr, Carola},
booktitle = {Workshop on Meta-Learning (MetaLearn 2022)},
keywords = {leibnizailab},
title = {Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis},
year = 2022
}%0 Conference Paper
%1 benjamins2022towards
%A Benjamins, Carolin
%A Jankovic, Anja
%A Raponi, Elena
%A van der Blom, Koen
%A Lindauer, Marius
%A Doerr, Carola
%B Workshop on Meta-Learning (MetaLearn 2022)
%D 2022
%T Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
%U https://www.ai.uni-hannover.de/de/forschung/publikationen/publikationen-detailansicht?tx_t3luhpublications_publications%5Baction%5D=show&tx_t3luhpublications_publications%5Bcontroller%5D=Publication&tx_t3luhpublications_publications%5Bpublication%5D=7783&cHash=04b8ffd50e56727ae4181c8e2a2261f1 - BibTeXEndNoteBibSonomyRumberg, L., Gebauer, C., Ehlert, H., L{ü}dtke, U., and Ostermann, J. (2022)Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding. In Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association.
@inproceedings{RumGeb2022b,
author = {Rumberg, Lars and Gebauer, Christopher and Ehlert, Hanna and L{ü}dtke, Ulrike and Ostermann, J{ö}rn},
booktitle = {Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association},
keywords = {Improving},
month = {09},
title = {Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding},
year = 2022
}%0 Conference Paper
%1 RumGeb2022b
%A Rumberg, Lars
%A Gebauer, Christopher
%A Ehlert, Hanna
%A L{ü}dtke, Ulrike
%A Ostermann, J{ö}rn
%B Proceedings INTERSPEECH 2022 – 23rd Annual Conference of the International Speech Communication Association
%D 2022
%T Improving Phonetic Transcriptions of Children’s Speech by Pronunciation Modelling with Constrained CTC-Decoding - URLBibTeXEndNoteBibSonomySass, R., Bergman, E., Biedenkapp, A., Hutter, F., and Lindauer, M. (2022)DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML), arXiv.
@inproceedings{https://doi.org/10.48550/arxiv.2206.03493,
author = {Sass, René and Bergman, Eddie and Biedenkapp, André and Hutter, Frank and Lindauer, Marius},
booktitle = {ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)},
keywords = {leibnizailab},
publisher = {arXiv},
title = {DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning},
year = 2022
}%0 Conference Paper
%1 https://doi.org/10.48550/arxiv.2206.03493
%A Sass, René
%A Bergman, Eddie
%A Biedenkapp, André
%A Hutter, Frank
%A Lindauer, Marius
%B ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML)
%D 2022
%I arXiv
%R 10.48550/ARXIV.2206.03493
%T DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
%U https://arxiv.org/abs/2206.03493 - BibTeXEndNoteBibSonomyHinrichs, R., Liang, K., Lu, Z., and Ostermann, J. (2022)Improved Compression of Artificial Neural Networks through Curvature-Aware Training. In Proceedings of the IEEE World Congress on Computational Intelligence.
@inproceedings{HinLia2022,
author = {Hinrichs, Reemt and Liang, Kai and Lu, Ze and Ostermann, J{ö}rn},
booktitle = {Proceedings of the IEEE World Congress on Computational Intelligence},
keywords = {Curvature-Aware},
month = {07},
title = {Improved Compression of Artificial Neural Networks through Curvature-Aware Training},
year = 2022
}%0 Conference Paper
%1 HinLia2022
%A Hinrichs, Reemt
%A Liang, Kai
%A Lu, Ze
%A Ostermann, J{ö}rn
%B Proceedings of the IEEE World Congress on Computational Intelligence
%D 2022
%T Improved Compression of Artificial Neural Networks through Curvature-Aware Training - URLBibTeXEndNoteBibSonomyBenjamins, C., Eimer, T., Schubert, F., Mohan, A., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2022)Contextualize Me -- The Case for Context in Reinforcement Learning, Arxiv Preprint, arXiv.
@article{https://doi.org/10.48550/arxiv.2202.04500,
author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Mohan, Aditya and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
journal = {Arxiv Preprint},
keywords = {leibnizailab},
publisher = {arXiv},
title = {Contextualize Me -- The Case for Context in Reinforcement Learning},
year = 2022
}%0 Journal Article
%1 https://doi.org/10.48550/arxiv.2202.04500
%A Benjamins, Carolin
%A Eimer, Theresa
%A Schubert, Frederik
%A Mohan, Aditya
%A Biedenkapp, André
%A Rosenhahn, Bodo
%A Hutter, Frank
%A Lindauer, Marius
%D 2022
%I arXiv
%J Arxiv Preprint
%R 10.48550/ARXIV.2202.04500
%T Contextualize Me -- The Case for Context in Reinforcement Learning
%U https://arxiv.org/abs/2202.04500 - URLBibTeXEndNoteBibSonomyDockhorn, A., and Kruse, R. (2022)Balancing Exploration and Exploitation in Forward Model Learning, Advances in Intelligent Systems Research and Innovation 1–19.
@article{DocKru2022,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {Advances in Intelligent Systems Research and Innovation},
keywords = {Exploration},
pages = {1--19},
title = {Balancing Exploration and Exploitation in Forward Model Learning},
year = 2022
}%0 Journal Article
%1 DocKru2022
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2022
%J Advances in Intelligent Systems Research and Innovation
%P 1--19
%R 10.1007/978-3-030-78124-8_1
%T Balancing Exploration and Exploitation in Forward Model Learning
%U https://doi.org/10.1007/978-3-030-78124-8_1
%@ 978-3-030-78124-8 - URLBibTeXEndNoteBibSonomyDockhorn, A., Kirst, M., Mostaghim, S., Wieczorek, M., and Zille, H. (2022)Evolutionary Algorithm for Parameter Optimization of Context Steering Agents, IEEE Transactions on Games 1–12.
@article{DocKir2022,
author = {Dockhorn, Alexander and Kirst, Martin and Mostaghim, Sanaz and Wieczorek, Martin and Zille, Heiner},
journal = {IEEE Transactions on Games},
keywords = {Algorithm},
pages = {1-12},
title = {Evolutionary Algorithm for Parameter Optimization of Context Steering Agents},
year = 2022
}%0 Journal Article
%1 DocKir2022
%A Dockhorn, Alexander
%A Kirst, Martin
%A Mostaghim, Sanaz
%A Wieczorek, Martin
%A Zille, Heiner
%D 2022
%J IEEE Transactions on Games
%P 1-12
%R 10.1109/TG.2022.3157247
%T Evolutionary Algorithm for Parameter Optimization of Context Steering Agents
%U https://ieeexplore.ieee.org/document/9729529 - URLBibTeXEndNoteBibSonomyXu, L., Hurtado-Grueso, J., Jeurissen, D., Liebana, D. P., and Dockhorn, A. (2022)Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing. In 2022 IEEE Conference on Games (CoG).
@inproceedings{XuHur2022,
author = {Xu, Linjie and Hurtado-Grueso, Jorge and Jeurissen, Dominic and Liebana, Diego Perez and Dockhorn, Alexander},
booktitle = {2022 IEEE Conference on Games (CoG)},
keywords = {Monte},
title = {Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing},
year = 2022
}%0 Conference Paper
%1 XuHur2022
%A Xu, Linjie
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominic
%A Liebana, Diego Perez
%A Dockhorn, Alexander
%B 2022 IEEE Conference on Games (CoG)
%D 2022
%T Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing
%U https://arxiv.org/abs/2205.15126 - URLBibTeXEndNoteBibSonomySchier, M., Reinders, C., and Rosenhahn, B. (2022)Constrained Mean Shift Clustering. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).
@inproceedings{SchRei2022a,
author = {Schier, Maximilian and Reinders, Christoph and Rosenhahn, Bodo},
booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
keywords = {Clustering},
month = {04},
title = {Constrained Mean Shift Clustering},
year = 2022
}%0 Conference Paper
%1 SchRei2022a
%A Schier, Maximilian
%A Reinders, Christoph
%A Rosenhahn, Bodo
%B Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)
%D 2022
%T Constrained Mean Shift Clustering
%U https://github.com/m-schier/cms - AbstractURLBibTeXEndNoteBibSonomyZhao, B., van der Aa, H., Nguyen, T. T., Nguyen, Q. V. H., and Weidlich, M. (2021){EIRES}: Efficient Integration of Remote Data in Event Stream Processing. In Proceedings of the 2021 International Conference on Management of Data, {ACM}.To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.
@inproceedings{Zhao_2021,
abstract = {To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data.},
author = {Zhao, Bo and van der Aa, Han and Nguyen, Thanh Tam and Nguyen, Quoc Viet Hung and Weidlich, Matthias},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
keywords = {l3s},
month = {06},
publisher = {{ACM}},
title = {{EIRES}: Efficient Integration of Remote Data in Event Stream Processing},
year = 2021
}%0 Conference Paper
%1 Zhao_2021
%A Zhao, Bo
%A van der Aa, Han
%A Nguyen, Thanh Tam
%A Nguyen, Quoc Viet Hung
%A Weidlich, Matthias
%B Proceedings of the 2021 International Conference on Management of Data
%D 2021
%I {ACM}
%R 10.1145/3448016.3457304
%T {EIRES}: Efficient Integration of Remote Data in Event Stream Processing
%U https://doi.org/10.1145%2F3448016.3457304
%X To support reactive and predictive applications, complex event processing (CEP) systems detect patterns in event streams based on predefined queries. To determine the events that constitute a query match, their payload data may need to be assessed together with data from remote sources. Such dependencies are problematic, since waiting for remote data to be fetched interrupts the processing of the stream. Yet, without event selection based on remote data, the query state to maintain may grow exponentially. In either case, the performance of the CEP system degrades drastically. To tackle these issues, we present EIRES, a framework for efficient integration of static data from remote sources in CEP. It employs a cost-model to determine when to fetch certain remote data elements and how long to keep them in a cache for future use. EIRES combines strategies for (i) prefetching that queries remote data based on anticipated use and (ii) lazy evaluation that postpones the event selection based on remote data without interrupting the stream processing. Our experiments indicate that the combination of these strategies improves the latency of query evaluation by up to 3,725x for synthetic data and 47x for real-world data. - BibTeXEndNoteBibSonomyIlyas, Z., Sharif, N., Schousboe, J. T., Lewis, J. R., Suter, D., and Gilani, S. Z. (2021)GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images. In 2021 Digital Image Computing: Techniques and Applications (DICTA), pp. 01–07.
@inproceedings{9647067,
author = {Ilyas, Zaid and Sharif, Naeha and Schousboe, John T. and Lewis, Joshua R. and Suter, David and Gilani, Syed Zulqarnain},
booktitle = {2021 Digital Image Computing: Techniques and Applications (DICTA)},
keywords = {leibnizailab},
pages = {01-07},
title = {GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images},
year = 2021
}%0 Conference Paper
%1 9647067
%A Ilyas, Zaid
%A Sharif, Naeha
%A Schousboe, John T.
%A Lewis, Joshua R.
%A Suter, David
%A Gilani, Syed Zulqarnain
%B 2021 Digital Image Computing: Techniques and Applications (DICTA)
%D 2021
%P 01-07
%R 10.1109/DICTA52665.2021.9647067
%T GuideNet: Learning Inter- Vertebral Guides in DXA Lateral Spine Images - URLBibTeXEndNoteBibSonomyDockhorn, A., and Kruse, R. (2021)Modelheuristics for efficient forward model learning, At-Automatisierungstechnik.
@article{DocKru2021a,
author = {Dockhorn, Alexander and Kruse, Rudolf},
journal = {At-Automatisierungstechnik},
keywords = {for},
month = 10,
title = {Modelheuristics for efficient forward model learning},
year = 2021
}%0 Journal Article
%1 DocKru2021a
%A Dockhorn, Alexander
%A Kruse, Rudolf
%D 2021
%J At-Automatisierungstechnik
%R 10.1515/auto-2021-0037
%T Modelheuristics for efficient forward model learning
%U https://www.degruyter.com/document/doi/10.1515/auto-2021-0037/html - AbstractURLBibTeXEndNoteBibSonomyHolzapfel, C., Sag, S., Graf-Schindler, J., Fischer, M., Drabsch, T., Illig, T., Grallert, H., Stecher, L., Strack, C., Caterson, I., Jebb, S., Hauner, H., and Baessler, A. (2021)Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis, Nutrients, MDPI 13.Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.
@article{holzapfel2021association,
abstract = {Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs.},
author = {Holzapfel, C and Sag, S and Graf-Schindler, J and Fischer, M and Drabsch, T and Illig, T and Grallert, H and Stecher, L and Strack, C and Caterson, ID and Jebb, SA and Hauner, H and Baessler, A},
journal = {Nutrients},
keywords = {l3s},
number = 3,
publisher = {MDPI},
title = {Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis},
type = {Publication},
volume = 13,
year = 2021
}%0 Journal Article
%1 holzapfel2021association
%A Holzapfel, C
%A Sag, S
%A Graf-Schindler, J
%A Fischer, M
%A Drabsch, T
%A Illig, T
%A Grallert, H
%A Stecher, L
%A Strack, C
%A Caterson, ID
%A Jebb, SA
%A Hauner, H
%A Baessler, A
%D 2021
%I MDPI
%J Nutrients
%N 3
%R 10.3390/nu13030819
%T Association between single nucleotide polymorphisms and weight reduction in behavioural interventions—a pooled analysis
%U https://ora.ox.ac.uk/objects/uuid:98987391-ba79-4686-a819-5fbe2a79ff45
%V 13
%X Knowledge of the association between single nucleotide polymorphisms (SNPs) and weight loss is limited. The aim was to analyse whether selected obesity-associated SNPs within the fat mass and obesity-associated (FTO), transmembrane protein 18 (TMEM18), melanocortin-4 receptor (MC4R), SEC16 homolog B (SEC16B), and brain-derived neurotrophic factor (BDNF) gene are associated with anthropometric changes during behavioural intervention for weight loss. genetic and anthropometric data from 576 individuals with overweight and obesity from four lifestyle interventions were obtained. A genetic predisposition score (GPS) was calculated. Our results show that study participants had a mean age of 48.2 ± 12.6 years and a mean baseline body mass index of 33.9 ± 6.4 kg/m2. Mean weight reduction after 12 months was −7.7 ± 10.9 kg. After 12 months of intervention, the MC4R SNPs rs571312 and rs17782313 were significantly associated with a greater decrease in body weight and BMI (p = 0.012, p = 0.011, respectively). The investigated SNPs within the other four genetic loci showed no statistically significant association with changes in anthropometric parameters. The GPS showed no statistically significant association with weight reduction. In conclusion there was no consistent evidence for statistically significant associations of SNPs with anthropometric changes during a behavioural intervention. It seems that other factors play a more significant in weight management than the investigated SNPs. - AbstractURLBibTeXEndNoteBibSonomyWu, W., Li, B., Luo, C., and Nejdl, W. (2021)Hashing-Accelerated Graph Neural Networks for Link Prediction. In .Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.
@conference{wu2021hashingaccelerated,
abstract = {Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.},
author = {Wu, Wei and Li, Bin and Luo, Chuan and Nejdl, Wolfgang},
keywords = {l3s},
note = {cite arxiv:2105.14280},
title = {Hashing-Accelerated Graph Neural Networks for Link Prediction},
year = 2021
}%0 Generic
%1 wu2021hashingaccelerated
%A Wu, Wei
%A Li, Bin
%A Luo, Chuan
%A Nejdl, Wolfgang
%D 2021
%R 10.1145/3442381.3449884
%T Hashing-Accelerated Graph Neural Networks for Link Prediction
%U http://arxiv.org/abs/2105.14280
%X Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources. - AbstractBibTeXEndNoteBibSonomyKaushal, A., Saha, A., and Ganguly, N. (2021)tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets, pp. 3879–3889.The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.
@proceedings{J,
abstract = {The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets.},
author = {Kaushal, Ayush and Saha, Avirup and Ganguly, Niloy},
keywords = {leibnizailab},
month = {06},
pages = {3879-3889},
title = {tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets},
year = 2021
}%0 Conference Proceedings
%1 J
%A Kaushal, Ayush
%A Saha, Avirup
%A Ganguly, Niloy
%D 2021
%P 3879-3889
%T tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets
%X The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT–WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets. - BibTeXEndNoteBibSonomyPestel-Schiller, U., and Ostermann, J. (2021)Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability. In 13th European Conference on Synthetic Aperture Radar.
@inproceedings{PesOst2021a,
author = {Pestel-Schiller, Ulrike and Ostermann, J{ö}rn},
booktitle = {13th European Conference on Synthetic Aperture Radar},
keywords = {Compressed},
month = {03},
title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
year = 2021
}%0 Conference Paper
%1 PesOst2021a
%A Pestel-Schiller, Ulrike
%A Ostermann, J{ö}rn
%B 13th European Conference on Synthetic Aperture Radar
%D 2021
%T Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability - URLBibTeXEndNoteBibSonomyDockhorn, A., Hurtado-Grueso, J., Jeurissen, D., Xu, L., and Perez-Liebana, D. (2021)Portfolio Search and Optimization for General Strategy Game-Playing. In 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 2085–2092.
@inproceedings{DocHur2021,
author = {Dockhorn, Alexander and Hurtado-Grueso, Jorge and Jeurissen, Dominik and Xu, Linjie and Perez-Liebana, Diego},
booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)},
keywords = {Search},
pages = {2085-2092},
title = {Portfolio Search and Optimization for General Strategy Game-Playing},
year = 2021
}%0 Conference Paper
%1 DocHur2021
%A Dockhorn, Alexander
%A Hurtado-Grueso, Jorge
%A Jeurissen, Dominik
%A Xu, Linjie
%A Perez-Liebana, Diego
%B 2021 IEEE Congress on Evolutionary Computation (CEC)
%D 2021
%P 2085-2092
%R 10.1109/CEC45853.2021.9504824
%T Portfolio Search and Optimization for General Strategy Game-Playing
%U https://ieeexplore.ieee.org/document/9504824 - BibTeXEndNoteBibSonomyTennakoon, R., Suter, D., Zhang, E., Chin, T.-J., and Bab-Hadiashar, A. (2021)Consensus Maximisation Using Influences of Monotone Boolean Functions. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2865–2874.
@inproceedings{BMFcvpr2021,
author = {Tennakoon, Ruwan and Suter, David and Zhang, Erchuan and Chin, Tat-Jun and Bab-Hadiashar, Alireza},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {leibnizailab},
note = {Oral Presentation (17\% of accepted papers, roughly 3\% of sumitted papers)},
pages = {2865-2874},
title = {Consensus Maximisation Using Influences of Monotone Boolean Functions},
year = 2021
}%0 Conference Paper
%1 BMFcvpr2021
%A Tennakoon, Ruwan
%A Suter, David
%A Zhang, Erchuan
%A Chin, Tat-Jun
%A Bab-Hadiashar, Alireza
%B 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
%D 2021
%P 2865-2874
%R 10.1109/CVPR46437.2021.00289
%T Consensus Maximisation Using Influences of Monotone Boolean Functions - URLBibTeXEndNoteBibSonomyHu, T., Iosifidis, V., Liao, W., Zhang, H., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020)FairNN - Conjoint Learning of Fair Representations for Fair Decisions.. In Discovery Science, pp. 581–595, Springer International Publishing.
@incollection{Hu_2020,
author = {Hu, Tongxin and Iosifidis, Vasileios and Liao, Wentong and Zhang, Hang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
booktitle = {Discovery Science},
keywords = {l3s},
pages = {581-595},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions.},
type = {Publication},
volume = 12323,
year = 2020
}%0 Book Section
%1 Hu_2020
%A Hu, Tongxin
%A Iosifidis, Vasileios
%A Liao, Wentong
%A Zhang, Hang
%A Yang, Michael Ying
%A Ntoutsi, Eirini
%A Rosenhahn, Bodo
%B Discovery Science
%D 2020
%I Springer International Publishing
%P 581-595
%R 10.1007/978-3-030-61527-7_38
%T FairNN - Conjoint Learning of Fair Representations for Fair Decisions.
%U https://doi.org/10.1007%2F978-3-030-61527-7_38
%V 12323 - BibTeXEndNoteBibSonomyTan, D., Maybery, M., Gilani, S. Z., Alvares, G., Mian, A., Suter, D., and Whitehouse, A. (2020)A broad autism phenotype expressed in facial morphology, Translational Psychiatry 10.
@article{TanPsych2019,
author = {Tan, Diana and Maybery, Murray and Gilani, Syed Zulqarnain and Alvares, Gail and Mian, Ajmal and Suter, David and Whitehouse, Andrew},
journal = {Translational Psychiatry},
keywords = {leibnizailab},
number = 1,
title = {A broad autism phenotype expressed in facial morphology},
volume = 10,
year = 2020
}%0 Journal Article
%1 TanPsych2019
%A Tan, Diana
%A Maybery, Murray
%A Gilani, Syed Zulqarnain
%A Alvares, Gail
%A Mian, Ajmal
%A Suter, David
%A Whitehouse, Andrew
%D 2020
%J Translational Psychiatry
%N 1
%R 10.1038/s41398-020-0695-z
%T A broad autism phenotype expressed in facial morphology
%V 10 - BibTeXEndNoteBibSonomyMinh, C. N. D., Gilani, S. Z., Islam, S., and Suter, D. (2020)Learning Affordance Segmentation: An Investigative Study. In DICTA2020.
@inproceedings{chauDICTA2020,
author = {Minh, Chau Nguyen Duc and Gilani, Syed Zulqarnain and Islam, Syed and Suter, David},
booktitle = {DICTA2020},
keywords = {leibnizailab},
title = {Learning Affordance Segmentation: An Investigative Study},
year = 2020
}%0 Conference Paper
%1 chauDICTA2020
%A Minh, Chau Nguyen Duc
%A Gilani, Syed Zulqarnain
%A Islam, Syed
%A Suter, David
%B DICTA2020
%D 2020
%R 10.1109/DICTA51227.2020.9363390
%T Learning Affordance Segmentation: An Investigative Study - URLBibTeXEndNoteBibSonomyDockhorn, A., Grueso, J. H., Jeurissen, D., and Perez-Liebana, D. (2020)“Stratega”: A General Strategy Games Framework. In Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games, pp. 1–7.
@inproceedings{DocGru2020,
author = {Dockhorn, Alexander and Grueso, Jorge Hurtado and Jeurissen, Dominik and Perez-Liebana, Diego},
booktitle = {Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games},
keywords = {“Stratega”},
pages = {1--7},
title = {“Stratega”: A General Strategy Games Framework},
year = 2020
}%0 Conference Paper
%1 DocGru2020
%A Dockhorn, Alexander
%A Grueso, Jorge Hurtado
%A Jeurissen, Dominik
%A Perez-Liebana, Diego
%B Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games
%D 2020
%P 1--7
%T “Stratega”: A General Strategy Games Framework
%U http://ceur-ws.org/Vol-2862/ - URLBibTeXEndNoteBibSonomyDockhorn, A. (2020)Vorhersagebasierte Suche f{ü}r autonomes Spielen, pp. 69–78, GI.
@book{Doc2020,
author = {Dockhorn, Alexander},
keywords = {Vorhersagebasierte},
pages = {69-78},
publisher = {GI},
title = {Vorhersagebasierte Suche f{ü}r autonomes Spielen},
year = 2020
}%0 Book
%1 Doc2020
%A Dockhorn, Alexander
%D 2020
%I GI
%P 69-78
%R 20.500.12116/37928
%T Vorhersagebasierte Suche f{ü}r autonomes Spielen
%U https://dl.gi.de/20.500.12116/37928
%@ 978-3-88579-775-3 - URLBibTeXEndNoteBibSonomyDockhorn, A., and Kruse, R. (2020)Forward Model Learning for Motion Control Tasks. In 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 1–5.
@inproceedings{DocKru2020b,
author = {Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2020 IEEE 10th International Conference on Intelligent Systems (IS)},
keywords = {Model},
month = {09},
pages = {1--5},
title = {Forward Model Learning for Motion Control Tasks},
year = 2020
}%0 Conference Paper
%1 DocKru2020b
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2020 IEEE 10th International Conference on Intelligent Systems (IS)
%D 2020
%P 1--5
%R 10.1109/IS48319.2020.9199978
%T Forward Model Learning for Motion Control Tasks
%U https://ieeexplore.ieee.org/document/9199978
%@ 9781728154565 - URLBibTeXEndNoteBibSonomyDockhorn, A., Schwensfeier, T., and Kruse, R. (2019)Fuzzy Multiset Clustering for Metagame Analysis. In Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), pp. 536–543.
@inproceedings{DocSch2019a,
author = {Dockhorn, Alexander and Schwensfeier, Tony and Kruse, Rudolf},
booktitle = {Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)},
keywords = {Multiset},
month = {08},
pages = {536-543},
title = {Fuzzy Multiset Clustering for Metagame Analysis},
year = 2019
}%0 Conference Paper
%1 DocSch2019a
%A Dockhorn, Alexander
%A Schwensfeier, Tony
%A Kruse, Rudolf
%B Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
%D 2019
%P 536-543
%R 10.2991/eusflat-19.2019.74
%T Fuzzy Multiset Clustering for Metagame Analysis
%U https://www.atlantis-press.com/proceedings/eusflat-19/125914844
%@ 978-94-6252-770-6 - URLBibTeXEndNoteBibSonomyHeld, P., Dockhorn, A., and Kruse, R. (2014)On Merging and Dividing of Barabasi-Albert-graphs. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).
@inproceedings{HelDoc2014,
author = {Held, Pascal and Dockhorn, Alexander and Kruse, Rudolf},
booktitle = {2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)},
keywords = {On},
title = {On Merging and Dividing of Barabasi-Albert-graphs},
volume = 444,
year = 2014
}%0 Conference Paper
%1 HelDoc2014
%A Held, Pascal
%A Dockhorn, Alexander
%A Kruse, Rudolf
%B 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)
%D 2014
%R 10.1109/EALS.2014.7009499
%T On Merging and Dividing of Barabasi-Albert-graphs
%U https://ieeexplore.ieee.org/document/7009499
%V 444
%@ 978-1-4799-4494-1