Research

2022

• 1.

  Hvarfner, C., Stoll, D., Souza, A., Nardi, L., Lindauer, M., and Hutter, F. (2022) piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In 10th International Conference on Learning Representations, ICLR’22, pp. 1–30.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{HvaSto2022a,
  author = {Hvarfner, Carl and Stoll, Danny and Souza, Artur and Nardi, Luigi and Lindauer, Marius and Hutter, Frank},
  booktitle = {10th International Conference on Learning Representations, ICLR'22},
  keywords = {Acquisition Augmenting Functions leibnizailab myown},
  month = {apr},
  pages = {1-30},
  title = {piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization},
  year = 2022
  }

  %0 Conference Paper
  %1 HvaSto2022a
  %A Hvarfner, Carl
  %A Stoll, Danny
  %A Souza, Artur
  %A Nardi, Luigi
  %A Lindauer, Marius
  %A Hutter, Frank
  %B 10th International Conference on Learning Representations, ICLR'22
  %D 2022
  %P 1-30
  %T piBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
  %U https://openreview.net/pdf/1ce81b811a1cac6ed2405793a93e8512b1b50005.pdf
• 1.

  Grimm, 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.

  URLBibTeXEndNoteBibSonomy

  @article{GriKuh2022,
  author = {Grimm, Eckhard and Kuhnke, Felix and Gajdt, Anna and Ostermann, Jörn and Knoche, Moritz},
  journal = {Horticulturae},
  keywords = {Anthocyanin Apples Flesh Quantification Red in leibnizailab myown of},
  month = {jan},
  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, Jörn
  %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
• 1.

  Chang, Y., Jing, X., Ren, Z., and Schuller, B. W. (2022) CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds, Frontiers in Digital Health (Hochheiser, H., Ed.) 3, 1–11.

  BibTeXEndNoteBibSonomy

  @article{chang2022covnet,
  author = {Chang, Yi and Jing, Xin and Ren, Zhao and Schuller, Björn W.},
  editor = {Hochheiser, Harry},
  journal = {Frontiers in Digital Health},
  keywords = {leibnizailab},
  month = {Jan.},
  number = 799067,
  pages = {1--11},
  title = {CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds},
  volume = 3,
  year = 2022
  }

  %0 Journal Article
  %1 chang2022covnet
  %A Chang, Yi
  %A Jing, Xin
  %A Ren, Zhao
  %A Schuller, Björn W.
  %D 2022
  %E Hochheiser, Harry
  %J Frontiers in Digital Health
  %N 799067
  %P 1--11
  %R https://doi.org/10.3389/fdgth.2021.799067
  %T CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds
  %V 3
• 1.

  Geisler, S., Vidal, M.-E., Cappiello, C., Loscio, B. F., Gal, A., Jarke, M., Lenzerini, M., Missier, P., Otto, B., Paja, E., Pernici, B., and Rehof, J. (2022) Knowledge-Driven Data Ecosystems Toward Data Transparency, Journal of Data and Information Quality, Association for Computing Machinery (ACM) 14, 1–12.

  URLBibTeXEndNoteBibSonomy

  @article{Geisler_2022,
  author = {Geisler, Sandra and Vidal, Maria-Esther and Cappiello, Cinzia and Loscio, Bernadette Farias and Gal, Avigdor and Jarke, Matthias and Lenzerini, Maurizio and Missier, Paolo and Otto, Boris and Paja, Elda and Pernici, Barbara and Rehof, Jakob},
  journal = {Journal of Data and Information Quality},
  keywords = {entitylinking knowledge-graphs leibnizailab myown semanticdataintegration},
  month = {mar},
  number = 1,
  pages = {1--12},
  publisher = {Association for Computing Machinery (ACM)},
  title = {Knowledge-Driven Data Ecosystems Toward Data Transparency},
  volume = 14,
  year = 2022
  }

  %0 Journal Article
  %1 Geisler_2022
  %A Geisler, Sandra
  %A Vidal, Maria-Esther
  %A Cappiello, Cinzia
  %A Loscio, Bernadette Farias
  %A Gal, Avigdor
  %A Jarke, Matthias
  %A Lenzerini, Maurizio
  %A Missier, Paolo
  %A Otto, Boris
  %A Paja, Elda
  %A Pernici, Barbara
  %A Rehof, Jakob
  %D 2022
  %I Association for Computing Machinery (ACM)
  %J Journal of Data and Information Quality
  %N 1
  %P 1--12
  %R 10.1145/3467022
  %T Knowledge-Driven Data Ecosystems Toward Data Transparency
  %U https://doi.org/10.1145%2F3467022
  %V 14
• 1.

  Reinders, C., Schubert, F., and Rosenhahn, B. (2022) ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing. In Arxiv Preprint.

  BibTeXEndNoteBibSonomy

  @inproceedings{ReiSch2022,
  author = {Reinders, Christoph and Schubert, Frederik and Rosenhahn, Bodo},
  booktitle = {Arxiv Preprint},
  keywords = {Classification Feature Image Masked Mixing leibnizailab myown},
  month = {mar},
  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
• 1.

  Awiszus, M., Schubert, F., and Rosenhahn, B. (2022) Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft, IEEE Transactions on Games.

  BibTeXEndNoteBibSonomy

  @article{AwiSch2022,
  author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  journal = {IEEE Transactions on Games},
  keywords = {Based Language Minecraft PCG in leibnizailab myown},
  month = {feb},
  note = {11 pages, 10 figures.},
  title = {Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft},
  year = 2022
  }

  %0 Journal Article
  %1 AwiSch2022
  %A Awiszus, Maren
  %A Schubert, Frederik
  %A Rosenhahn, Bodo
  %D 2022
  %J IEEE Transactions on Games
  %T Wor(l)d-GAN: Towards Natural Language Based PCG in Minecraft
• 1.

  Benjamins, 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.

  URLBibTeXEndNoteBibSonomy

  @article{BenEim2022a,
  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 = {Case Context Learning Reinforcement for in leibnizailab myown},
  month = {feb},
  title = {Contextualize Me - The Case for Context in Reinforcement Learning},
  year = 2022
  }

  %0 Journal Article
  %1 BenEim2022a
  %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
  %J ArXiv Preprint
  %T Contextualize Me - The Case for Context in Reinforcement Learning
  %U https://arxiv.org/abs/2202.04500
• 1.

  Hinrichs, R., Gerkens, K., Lange, A., and Ostermann, J. (2022) Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks. In EvoMUSART 2022.

  BibTeXEndNoteBibSonomy

  @inproceedings{HinGer2022,
  author = {Hinrichs, Reemt and Gerkens, Kevin and Lange, Alexander and Ostermann, Jörn},
  booktitle = {EvoMUSART 2022},
  keywords = {Classification Effects Extraction Guitar and leibnizailab myown of},
  title = {Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks},
  year = 2022
  }

  %0 Conference Paper
  %1 HinGer2022
  %A Hinrichs, Reemt
  %A Gerkens, Kevin
  %A Lange, Alexander
  %A Ostermann, Jörn
  %B EvoMUSART 2022
  %D 2022
  %T Classification of Guitar Effects and Extraction of their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks
• 1.

  Hinrichs, R., Jiang, N., Beltran, R., Krause, T., Käding, M., Lange, A., Schmidt, B., Ostermann, J., and Marx, S. (2022) Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator. In 20th World Conference on Non-Destructive Testing (WCNDT 2020).

  BibTeXEndNoteBibSonomy

  @inproceedings{HinJia2022,
  author = {Hinrichs, Reemt and Jiang, Nan and Beltran, Raul and Krause, Thomas and Käding, Max and Lange, Alexander and Schmidt, Boso and Ostermann, Jörn and Marx, Steffen},
  booktitle = {20th World Conference on Non-Destructive Testing (WCNDT 2020)},
  keywords = {Break Lead Pencil Repeatability leibnizailab myown of the},
  title = {Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator},
  year = 2022
  }

  %0 Conference Paper
  %1 HinJia2022
  %A Hinrichs, Reemt
  %A Jiang, Nan
  %A Beltran, Raul
  %A Krause, Thomas
  %A Käding, Max
  %A Lange, Alexander
  %A Schmidt, Boso
  %A Ostermann, Jörn
  %A Marx, Steffen
  %B 20th World Conference on Non-Destructive Testing (WCNDT 2020)
  %D 2022
  %T Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator
• 1.

  Mukherjee, R., Vishnu, U., Peruri, H. C., Bhattacharya, S., Rudra, K., Goyal, P., and Ganguly, N. (2022) MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 755–763, Association for Computing Machinery, Virtual Event, AZ, USA.

  AbstractURLBibTeXEndNoteBibSonomy

  Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.

  @inproceedings{10.1145/3488560.3498536,
  abstract = {Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.},
  address = {New York, NY, USA},
  author = {Mukherjee, Rajdeep and Vishnu, Uppada and Peruri, Hari Chandana and Bhattacharya, Sourangshu and Rudra, Koustav and Goyal, Pawan and Ganguly, Niloy},
  booktitle = {Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
  keywords = {leibnizailab myown},
  pages = {755–763},
  publisher = {Association for Computing Machinery},
  series = {WSDM '22},
  title = {MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs},
  year = 2022
  }

  %0 Conference Paper
  %1 10.1145/3488560.3498536
  %A Mukherjee, Rajdeep
  %A Vishnu, Uppada
  %A Peruri, Hari Chandana
  %A Bhattacharya, Sourangshu
  %A Rudra, Koustav
  %A Goyal, Pawan
  %A Ganguly, Niloy
  %B Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
  %C New York, NY, USA
  %D 2022
  %I Association for Computing Machinery
  %P 755–763
  %R 10.1145/3488560.3498536
  %T MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
  %U https://doi.org/10.1145/3488560.3498536
  %X Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization.
  %@ 9781450391320

2021, 2020

• 1.

  Luo, C., Zhao, P., Chen, C., Qiao, B., Du, C., Zhang, H., Wu, W., Cai, S., He, B., Rajmohan, S., and Lin, Q. (2020) PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. In , pp. 8784–8792.

  AbstractURLBibTeXEndNoteBibSonomy

  Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.

  @inproceedings{luo20212020pulns,
  abstract = {Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.},
  author = {Luo, Chuan and Zhao, Pu and Chen, Chen and Qiao, Bo and Du, Chao and Zhang, Hongyu and Wu, Wei and Cai, Shaowei and He, Bing and Rajmohan, Saravanakumar and Lin, Qingwei},
  keywords = {l3s learning leibnizailab},
  number = 10,
  pages = {8784-8792},
  title = {PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector},
  type = {Publication},
  volume = 35,
  year = {2021, 2020}
  }

  %0 Conference Paper
  %1 luo20212020pulns
  %A Luo, Chuan
  %A Zhao, Pu
  %A Chen, Chen
  %A Qiao, Bo
  %A Du, Chao
  %A Zhang, Hongyu
  %A Wu, Wei
  %A Cai, Shaowei
  %A He, Bing
  %A Rajmohan, Saravanakumar
  %A Lin, Qingwei
  %D 2021, 2020
  %N 10
  %P 8784-8792
  %T PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector
  %U https://ojs.aaai.org/index.php/AAAI/article/view/17064
  %V 35
  %X Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.

2021

• 1.

  Singh, J., Wang, Z., Khosla, M., and Anand, A. (2021) Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models. In International Conference on the Theory of Information Retrieval.

  BibTeXEndNoteBibSonomy

  @inproceedings{noauthororeditor2021extracting,
  author = {Singh, Jaspreet and Wang, Zhenye and Khosla, Megha and Anand, Avishek},
  booktitle = {International Conference on the Theory of Information Retrieval},
  keywords = {leibnizailab myown},
  title = {Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models},
  year = 2021
  }

  %0 Conference Paper
  %1 noauthororeditor2021extracting
  %A Singh, Jaspreet
  %A Wang, Zhenye
  %A Khosla, Megha
  %A Anand, Avishek
  %B International Conference on the Theory of Information Retrieval
  %D 2021
  %T Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models
• 1.

  Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2021) Designing an Experience Sampling Method for Smartphone Based Emotion Detection, IEEE Transactions on Affective Computing 12, 913–927.

  AbstractBibTeXEndNoteBibSonomy

  Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider - probing frequency, timely self-report collection, and notifying at opportune moment to ensure high response quality. We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.

  @article{8668435,
  abstract = {Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider - probing frequency, timely self-report collection, and notifying at opportune moment to ensure high response quality. We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.},
  author = {Ghosh, Surjya and Ganguly, Niloy and Mitra, Bivas and De, Pradipta},
  journal = {IEEE Transactions on Affective Computing},
  keywords = {leibnizailab myown},
  month = {oct},
  number = 4,
  pages = {913-927},
  title = {Designing an Experience Sampling Method for Smartphone Based Emotion Detection},
  volume = 12,
  year = 2021
  }

  %0 Journal Article
  %1 8668435
  %A Ghosh, Surjya
  %A Ganguly, Niloy
  %A Mitra, Bivas
  %A De, Pradipta
  %D 2021
  %J IEEE Transactions on Affective Computing
  %N 4
  %P 913-927
  %R 10.1109/TAFFC.2019.2905561
  %T Designing an Experience Sampling Method for Smartphone Based Emotion Detection
  %V 12
  %X Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider - probing frequency, timely self-report collection, and notifying at opportune moment to ensure high response quality. We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.
• 1.

  Rudolph, M., Wandt, B., and Rosenhahn, B. (2021) Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows. In Winter Conference on Applications of Computer Vision (WACV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{RudWan2021a,
  author = {Rudolph, Marco and Wandt, Bastian and Rosenhahn, Bodo},
  booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
  keywords = {Defect Detection Semi-Supervised leibnizailab myown},
  month = {jan},
  title = {Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows},
  year = 2021
  }

  %0 Conference Paper
  %1 RudWan2021a
  %A Rudolph, Marco
  %A Wandt, Bastian
  %A Rosenhahn, Bodo
  %B Winter Conference on Applications of Computer Vision (WACV)
  %D 2021
  %T Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
  %U /brokenurl#arxiv, GitHub, YouTube
• 1.

  Hartmann, F., Sommer, A., Pestel-Schiller, U., and Osterman, J. (2021) A scheme for stabilizing the image generation for VideoSAR. In 13th European Conference on Synthetic Aperture Radar.

  BibTeXEndNoteBibSonomy

  @inproceedings{HarSom2021a,
  author = {Hartmann, Fabian and Sommer, Aron and Pestel-Schiller, Ulrike and Osterman, Jörn},
  booktitle = {13th European Conference on Synthetic Aperture Radar},
  keywords = {VideoSAR leibnizailab myown},
  month = {mar},
  title = {A scheme for stabilizing the image generation for VideoSAR},
  year = 2021
  }

  %0 Conference Paper
  %1 HarSom2021a
  %A Hartmann, Fabian
  %A Sommer, Aron
  %A Pestel-Schiller, Ulrike
  %A Osterman, Jörn
  %B 13th European Conference on Synthetic Aperture Radar
  %D 2021
  %T A scheme for stabilizing the image generation for VideoSAR
• 1.

  Truong, G., Le, H., Suter, D., Zhang, E., and Gilani, S. Z. (2021) Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10343–10352.

  BibTeXEndNoteBibSonomy

  @inproceedings{giang_cvpr2021,
  author = {Truong, Giang and Le, Huu and Suter, David and Zhang, Erchuan and Gilani, Syed Zulqarnain},
  booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  keywords = {leibnizailab myown},
  pages = {10343-10352},
  title = {Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach},
  year = 2021
  }

  %0 Conference Paper
  %1 giang_cvpr2021
  %A Truong, Giang
  %A Le, Huu
  %A Suter, David
  %A Zhang, Erchuan
  %A Gilani, Syed Zulqarnain
  %B 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  %D 2021
  %P 10343-10352
  %R 10.1109/CVPR46437.2021.01021
  %T Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach
• 1.

  Tennakoon, 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.

  BibTeXEndNoteBibSonomy

  @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 myown},
  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
• 1.

  Ilyas, 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.

  BibTeXEndNoteBibSonomy

  @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 myown},
  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
• 1.

  Das, S., Patibandla, H., Bhattacharya, S., Bera, K., Ganguly, N., and Bhattacharya, S. (2021) TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving. In ICCV.

  BibTeXEndNoteBibSonomy

  @inproceedings{das2021tmcoss,
  author = {Das, Soumi and Patibandla, Harikrishna and Bhattacharya, Suparna and Bera, Kshounis and Ganguly, Niloy and Bhattacharya, Sourangshu},
  booktitle = {ICCV},
  keywords = {leibnizailab myown},
  title = {TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving},
  year = 2021
  }

  %0 Conference Paper
  %1 das2021tmcoss
  %A Das, Soumi
  %A Patibandla, Harikrishna
  %A Bhattacharya, Suparna
  %A Bera, Kshounis
  %A Ganguly, Niloy
  %A Bhattacharya, Sourangshu
  %B ICCV
  %D 2021
  %T TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving
• 1.

  Kaushal, 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.

  AbstractBibTeXEndNoteBibSonomy

  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 myown},
  month = {jun},
  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.
• 1.

  Samanta, B., Agrawal, M., and Ganguly, N. (2021) A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow, pp. 2405–2415, Association for Computational Linguistics.

  AbstractURLBibTeXEndNoteBibSonomy

  In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.

  @proceedings{samanta2021hierarchical,
  abstract = {In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.},
  address = {Association for Computational Linguistics},
  author = {Samanta, Bidisha and Agrawal, Mohit and Ganguly, NIloy},
  howpublished = {Online},
  keywords = {leibnizailab myown},
  pages = {2405-2415},
  title = {A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow},
  year = 2021
  }

  %0 Conference Proceedings
  %1 samanta2021hierarchical
  %A Samanta, Bidisha
  %A Agrawal, Mohit
  %A Ganguly, NIloy
  %C Association for Computational Linguistics
  %D 2021
  %P 2405-2415
  %T A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow
  %U https://aclanthology.org/2021.acl-long.187
  %X In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.
• 1.

  Tan, D. W., Gilani, S. Z., Boutrus, M., Alvares, G. A., Whitehouse, A. J., Mian, A., Suter, D., and Maybery, M. T. (2021) Facial asymmetry in parents of children on the autism spectrum, Autism Research.

  BibTeXEndNoteBibSonomy

  @article{autism:2021,
  author = {Tan, D W and Gilani, S Z and Boutrus, M and Alvares, G A. and Whitehouse, A J.O. and Mian, A and Suter, D and Maybery, M T.},
  journal = {Autism Research},
  keywords = {leibnizailab myown},
  title = {Facial asymmetry in parents of children on the autism spectrum},
  year = 2021
  }

  %0 Journal Article
  %1 autism:2021
  %A Tan, D W
  %A Gilani, S Z
  %A Boutrus, M
  %A Alvares, G A.
  %A Whitehouse, A J.O.
  %A Mian, A
  %A Suter, D
  %A Maybery, M T.
  %D 2021
  %J Autism Research
  %R 10.1002/aur.2612
  %T Facial asymmetry in parents of children on the autism spectrum
• 1.

  Mukherjee, A., Mallick, M., Chakraborty, S., and Ganguly, N. (2021) Unsupervised Topology Assessment in Smart Homes. In 8th ACM IKDD CODS and 26th COMAD, pp. 193–197, Association for Computing Machinery, Bangalore, India.

  AbstractURLBibTeXEndNoteBibSonomy

  Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.

  @inproceedings{10.1145/3430984.3431028,
  abstract = {Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.},
  address = {New York, NY, USA},
  author = {Mukherjee, Avirup and Mallick, Madhumita and Chakraborty, Sandip and Ganguly, Niloy},
  booktitle = {8th ACM IKDD CODS and 26th COMAD},
  keywords = {leibnizailab myown},
  pages = {193–197},
  publisher = {Association for Computing Machinery},
  series = {CODS COMAD 2021},
  title = {Unsupervised Topology Assessment in Smart Homes},
  year = 2021
  }

  %0 Conference Paper
  %1 10.1145/3430984.3431028
  %A Mukherjee, Avirup
  %A Mallick, Madhumita
  %A Chakraborty, Sandip
  %A Ganguly, Niloy
  %B 8th ACM IKDD CODS and 26th COMAD
  %C New York, NY, USA
  %D 2021
  %I Association for Computing Machinery
  %P 193–197
  %R 10.1145/3430984.3431028
  %T Unsupervised Topology Assessment in Smart Homes
  %U https://doi.org/10.1145/3430984.3431028
  %X Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.
  %@ 9781450388177
• 1.

  Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021) Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework, pp. 4600–4609, Association for Computational Linguistics.

  AbstractURLBibTeXEndNoteBibSonomy

  Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.

  @proceedings{nandy2021question,
  abstract = {Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.},
  address = {Association for Computational Linguistics},
  author = {Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy},
  howpublished = {Punta Cana, Dominican Republic},
  keywords = {leibnizailab myown},
  pages = {4600-4609},
  title = {Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework},
  year = 2021
  }

  %0 Conference Proceedings
  %1 nandy2021question
  %A Nandy, Abhilash
  %A Sharma, Soumya
  %A Maddhashiya, Shubham
  %A Sachdeva, Kapil
  %A Goyal, Pawan
  %A Ganguly, NIloy
  %C Association for Computational Linguistics
  %D 2021
  %P 4600-4609
  %T Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
  %U https://aclanthology.org/2021.findings-emnlp.392
  %X Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.
• 1.

  Sheshadri, S., Saha, A., Patel, P., Datta, S., and Ganguly, N. (2021) Graph-based semi-supervised learning through the lens of safety. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (de Campos, C., and Maathuis, M. H., Eds.), pp. 1576–1586, PMLR.

  AbstractURLBibTeXEndNoteBibSonomy

  Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.

  @inproceedings{pmlr-v161-sheshadri21a,
  abstract = {Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.},
  author = {Sheshadri, Shreyas and Saha, Avirup and Patel, Priyank and Datta, Samik and Ganguly, Niloy},
  booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
  editor = {de Campos, Cassio and Maathuis, Marloes H.},
  keywords = {leibnizailab myown},
  month = {27--30 Jul},
  pages = {1576--1586},
  publisher = {PMLR},
  series = {Proceedings of Machine Learning Research},
  title = {Graph-based semi-supervised learning through the lens of safety},
  volume = 161,
  year = 2021
  }

  %0 Conference Paper
  %1 pmlr-v161-sheshadri21a
  %A Sheshadri, Shreyas
  %A Saha, Avirup
  %A Patel, Priyank
  %A Datta, Samik
  %A Ganguly, Niloy
  %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
  %D 2021
  %E de Campos, Cassio
  %E Maathuis, Marloes H.
  %I PMLR
  %P 1576--1586
  %T Graph-based semi-supervised learning through the lens of safety
  %U https://proceedings.mlr.press/v161/sheshadri21a.html
  %V 161
  %X Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.
• 1.

  Gritzner, D., Hinrichs, H., Stetter, C., Wielert, H., Breitner, M. H., and Ostermann, J. (2021) Wind Turbine Localization in Satellite and Aerial Images. In Proceedings of the Wind Energy Science Conference 2021, pp. 40–41.

  BibTeXEndNoteBibSonomy

  @inproceedings{GriHin2021,
  author = {Gritzner, Daniel and Hinrichs, Hauke and Stetter, Chris and Wielert, Henrik and Breitner, Michael H. and Ostermann, Jörn},
  booktitle = {Proceedings of the Wind Energy Science Conference 2021},
  keywords = {Aerial Images Satellite Turbine Wind leibnizailab myown},
  number = 10,
  pages = {40-41},
  title = {Wind Turbine Localization in Satellite and Aerial Images},
  year = 2021
  }

  %0 Conference Paper
  %1 GriHin2021
  %A Gritzner, Daniel
  %A Hinrichs, Hauke
  %A Stetter, Chris
  %A Wielert, Henrik
  %A Breitner, Michael H.
  %A Ostermann, Jörn
  %B Proceedings of the Wind Energy Science Conference 2021
  %D 2021
  %N 10
  %P 40-41
  %T Wind Turbine Localization in Satellite and Aerial Images
• 1.

  Roy, S., Chakraborty, S., Mandal, A., Balde, G., Sharma, P., Natarajan, A., Khosla, M., Sural, S., and Ganguly, N. (2021) Knowledge-Aware Neural Networks for Medical Forum Question Classification. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3398–3402, Association for Computing Machinery, New York, NY, USA.

  AbstractURLBibTeXEndNoteBibSonomy

  Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.

  @inbook{10.1145/3459637.3482128,
  abstract = {Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.},
  address = {New York, NY, USA},
  author = {Roy, Soumyadeep and Chakraborty, Sudip and Mandal, Aishik and Balde, Gunjan and Sharma, Prakhar and Natarajan, Anandhavelu and Khosla, Megha and Sural, Shamik and Ganguly, Niloy},
  booktitle = {Proceedings of the 30th ACM International Conference on Information & Knowledge Management},
  keywords = {leibnizailab myown},
  pages = {3398–3402},
  publisher = {Association for Computing Machinery},
  title = {Knowledge-Aware Neural Networks for Medical Forum Question Classification},
  year = 2021
  }

  %0 Book Section
  %1 10.1145/3459637.3482128
  %A Roy, Soumyadeep
  %A Chakraborty, Sudip
  %A Mandal, Aishik
  %A Balde, Gunjan
  %A Sharma, Prakhar
  %A Natarajan, Anandhavelu
  %A Khosla, Megha
  %A Sural, Shamik
  %A Ganguly, Niloy
  %B Proceedings of the 30th ACM International Conference on Information & Knowledge Management
  %C New York, NY, USA
  %D 2021
  %I Association for Computing Machinery
  %P 3398–3402
  %T Knowledge-Aware Neural Networks for Medical Forum Question Classification
  %U https://doi.org/10.1145/3459637.3482128
  %X Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
  %@ 9781450384469
• 1.

  Nandy, A., Sharma, S., Maddhashiya, S., Sachdeva, K., Goyal, P., and Ganguly, N. (2021) Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework. In Findings of the Association for Computational Linguistics: EMNLP 2021, Association for Computational Linguistics.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Nandy_2021,
  author = {Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
  keywords = {leibnizailab myown},
  publisher = {Association for Computational Linguistics},
  title = {Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework},
  year = 2021
  }

  %0 Conference Paper
  %1 Nandy_2021
  %A Nandy, Abhilash
  %A Sharma, Soumya
  %A Maddhashiya, Shubham
  %A Sachdeva, Kapil
  %A Goyal, Pawan
  %A Ganguly, NIloy
  %B Findings of the Association for Computational Linguistics: EMNLP 2021
  %D 2021
  %I Association for Computational Linguistics
  %R 10.18653/v1/2021.findings-emnlp.392
  %T Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework
  %U https://doi.org/10.18653%2Fv1%2F2021.findings-emnlp.392
• 1.

  Koley, P., Saha, A., Bhattacharya, S., Ganguly, N., and De, A. (2021) Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach, ACM Trans. Knowl. Discov. Data, Association for Computing Machinery, New York, NY, USA 15.

  AbstractURLBibTeXEndNoteBibSonomy

  The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

  @article{10.1145/3449361,
  abstract = {The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.},
  address = {New York, NY, USA},
  author = {Koley, Paramita and Saha, Avirup and Bhattacharya, Sourangshu and Ganguly, Niloy and De, Abir},
  journal = {ACM Trans. Knowl. Discov. Data},
  keywords = {leibnizailab myown},
  month = {jun},
  number = 6,
  publisher = {Association for Computing Machinery},
  title = {Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach},
  volume = 15,
  year = 2021
  }

  %0 Journal Article
  %1 10.1145/3449361
  %A Koley, Paramita
  %A Saha, Avirup
  %A Bhattacharya, Sourangshu
  %A Ganguly, Niloy
  %A De, Abir
  %C New York, NY, USA
  %D 2021
  %I Association for Computing Machinery
  %J ACM Trans. Knowl. Discov. Data
  %N 6
  %R 10.1145/3449361
  %T Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
  %U https://doi.org/10.1145/3449361
  %V 15
  %X The networked opinion diffusion in online social networks is often governed by the two genres of opinions—endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news and feeds. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this article, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.
• 1.

  Cong, Y., Liao, W., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2021) Spatial-Temporal Transformer for Dynamic Scene Graph Generation. In International Conference on Computer Vision (ICCV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ConLia2021a,
  author = {Cong, Yuren and Liao, Wentong and Ackermann, Hanno and Yang, Michael Yang and Rosenhahn, Bodo},
  booktitle = {International Conference on Computer Vision (ICCV)},
  keywords = {Dynamic Generation Graph Scene leibnizailab myown},
  month = {jul},
  title = {Spatial-Temporal Transformer for Dynamic Scene Graph Generation},
  year = 2021
  }

  %0 Conference Paper
  %1 ConLia2021a
  %A Cong, Yuren
  %A Liao, Wentong
  %A Ackermann, Hanno
  %A Yang, Michael Yang
  %A Rosenhahn, Bodo
  %B International Conference on Computer Vision (ICCV)
  %D 2021
  %T Spatial-Temporal Transformer for Dynamic Scene Graph Generation
  %U https://arxiv.org/abs/2107.12309
• 1.

  Chin, T.-J., Suter, D., Ch’ng, S.-F., and Quach, J. (2021) Quantum Robust Fitting. In Computer Vision -- ACCV 2020 (Ishikawa, H., Liu, C.-L., Pajdla, T., and Shi, J., Eds.), pp. 485–499, Springer International Publishing, Cham.

  AbstractBibTeXEndNoteBibSonomy

  Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.

  @inproceedings{10.1007/978-3-030-69525-5_29,
  abstract = {Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.},
  address = {Cham},
  author = {Chin, Tat-Jun and Suter, David and Ch'ng, Shin-Fang and Quach, James},
  booktitle = {Computer Vision -- ACCV 2020},
  editor = {Ishikawa, Hiroshi and Liu, Cheng-Lin and Pajdla, Tomas and Shi, Jianbo},
  keywords = {leibnizailab myown},
  pages = {485--499},
  publisher = {Springer International Publishing},
  title = {Quantum Robust Fitting},
  year = 2021
  }

  %0 Conference Paper
  %1 10.1007/978-3-030-69525-5_29
  %A Chin, Tat-Jun
  %A Suter, David
  %A Ch'ng, Shin-Fang
  %A Quach, James
  %B Computer Vision -- ACCV 2020
  %C Cham
  %D 2021
  %E Ishikawa, Hiroshi
  %E Liu, Cheng-Lin
  %E Pajdla, Tomas
  %E Shi, Jianbo
  %I Springer International Publishing
  %P 485--499
  %R 10.1007/978-3-030-69525-5_29
  %T Quantum Robust Fitting
  %X Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.
  %@ 978-3-030-69525-5
• 1.

  Kabongo, S., D’Souza, J., and Auer, S. (2021) Automated Mining of Leaderboards for Empirical AI Research, springer, International Conference on Asian Digital Libraries ICADL 2021: Towards Open and Trustworthy Digital Societies, 453–470.

  AbstractURLBibTeXEndNoteBibSonomy

  With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.

  @article{DBLP:conf/icadl/KabongoDA21,
  abstract = {With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.},
  author = {Kabongo, Salomon and D'Souza, Jennifer and Auer, Sören},
  journal = {springer, International Conference on Asian Digital Libraries},
  keywords = {Information-extraction Knowledge-graphs Neural-machine-learning Scholarly-text-mining Table-mining leibnizailab myown},
  month = {November 2021},
  pages = {453–470},
  title = {Automated Mining of Leaderboards for Empirical AI Research},
  volume = {ICADL 2021: Towards Open and Trustworthy Digital Societies},
  year = 2021
  }

  %0 Journal Article
  %1 DBLP:conf/icadl/KabongoDA21
  %A Kabongo, Salomon
  %A D'Souza, Jennifer
  %A Auer, Sören
  %D 2021
  %J springer, International Conference on Asian Digital Libraries
  %P 453–470
  %R https://doi.org/10.1007/978-3-030-91669-5_35
  %T Automated Mining of Leaderboards for Empirical AI Research
  %U https://link.springer.com/chapter/10.1007/978-3-030-91669-5_35
  %V ICADL 2021: Towards Open and Trustworthy Digital Societies
  %X With the rapid growth of research publications, empowering scientists to keep an oversight over scientific progress is of paramount importance. In this regard, the leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PAPERSWITHCODE among others are devoted to the construction of leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress – their construction could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.
• 1.

  Hinrichs, R., Dunkel, J., and Ostermann, J. (2021) Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks. In 6th International Conference on Frontiers of Signal Processing (ICFSP 2021).

  BibTeXEndNoteBibSonomy

  @inproceedings{HinDun2021,
  author = {Hinrichs, Reemt and Dunkel, Jonas and Ostermann, Jörn},
  booktitle = {6th International Conference on Frontiers of Signal Processing (ICFSP 2021)},
  keywords = {Command Distributions Recognition Speech Time-Frequency leibnizailab myown},
  month = {sep},
  title = {Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks},
  year = 2021
  }

  %0 Conference Paper
  %1 HinDun2021
  %A Hinrichs, Reemt
  %A Dunkel, Jonas
  %A Ostermann, Jörn
  %B 6th International Conference on Frontiers of Signal Processing (ICFSP 2021)
  %D 2021
  %T Mixing Time-Frequency Distributions for Speech Command Recognition using Convolutional Neural Networks
• 1.

  Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021) Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS).

  BibTeXEndNoteBibSonomy

  @inproceedings{MooHer2021a,
  author = {Moosbauer, Julia and Herbinger, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
  booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)},
  keywords = {Dependence Hyperparameter Partial Plots leibnizailab myown},
  month = {dec},
  title = {Explaining Hyperparameter Optimization via Partial Dependence Plots},
  year = 2021
  }

  %0 Conference Paper
  %1 MooHer2021a
  %A Moosbauer, Julia
  %A Herbinger, Julia
  %A Casalicchio, Giuseppe
  %A Lindauer, Marius
  %A Bischl, Bernd
  %B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)
  %D 2021
  %T Explaining Hyperparameter Optimization via Partial Dependence Plots
• 1.

  Narisetti, N., Henke, M., Seiler, C., Junker, A., Ostermann, J., Altmann, T., and Gladilin, E. (2021) Fully-automated root image analysis (faRIA), Scientific Reports 11.

  URLBibTeXEndNoteBibSonomy

  @article{NarHen2021,
  author = {Narisetti, Narendra and Henke, Michael and Seiler, Christiane and Junker, Astrid and Ostermann, Jörn and Altmann, Thomas and Gladilin, Evgeny},
  journal = {Scientific Reports},
  keywords = {analysis image leibnizailab myown root},
  month = {aug},
  title = {Fully-automated root image analysis (faRIA)},
  volume = 11,
  year = 2021
  }

  %0 Journal Article
  %1 NarHen2021
  %A Narisetti, Narendra
  %A Henke, Michael
  %A Seiler, Christiane
  %A Junker, Astrid
  %A Ostermann, Jörn
  %A Altmann, Thomas
  %A Gladilin, Evgeny
  %D 2021
  %J Scientific Reports
  %R https://doi.org/10.1038/s41598-021-95480-y
  %T Fully-automated root image analysis (faRIA)
  %U https://www.nature.com/articles/s41598-021-95480-y
  %V 11
• 1.

  Wehrbein, T., Rudolph, M., Rosenhahn, B., and Wandt, B. (2021) Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows. In International Conference on Computer Vision (ICCV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{WehRud2021a,
  author = {Wehrbein, Tom and Rudolph, Marco and Rosenhahn, Bodo and Wandt, Bastian},
  booktitle = {International Conference on Computer Vision (ICCV)},
  keywords = {3D Human Monocular Pose leibnizailab myown},
  month = {oct},
  title = {Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows},
  year = 2021
  }

  %0 Conference Paper
  %1 WehRud2021a
  %A Wehrbein, Tom
  %A Rudolph, Marco
  %A Rosenhahn, Bodo
  %A Wandt, Bastian
  %B International Conference on Computer Vision (ICCV)
  %D 2021
  %T Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
  %U /brokenurl#arxiv, code
• 1.

  Hornakova*, A., Kaiser*, T., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021) Higher Order Multiple Object Tracking for Crowded Scenes, Computer Vision and Pattern Recognition Workshops (CVPRW).

  URLBibTeXEndNoteBibSonomy

  @article{HorKai2021a,
  author = {Hornakova*, Andrea and Kaiser*, Timo and Rosenhahn, Bodo and Swoboda, Paul and Henschel, Roberto and equal contribution), (*},
  journal = {Computer Vision and Pattern Recognition Workshops (CVPRW)},
  keywords = {Crowded Object Scenes Tracking leibnizailab myown},
  month = {jun},
  title = {Higher Order Multiple Object Tracking for Crowded Scenes},
  year = 2021
  }

  %0 Journal Article
  %1 HorKai2021a
  %A Hornakova*, Andrea
  %A Kaiser*, Timo
  %A Rosenhahn, Bodo
  %A Swoboda, Paul
  %A Henschel, Roberto
  %A equal contribution), (*
  %D 2021
  %J Computer Vision and Pattern Recognition Workshops (CVPRW)
  %T Higher Order Multiple Object Tracking for Crowded Scenes
  %U https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/workshop/papers/9/CameraReady/paper_V3.pdf
• 1.

  Hornakova*, A., Kaiser*, T., Rolinek, M., Rosenhahn, B., Swoboda, P., Henschel, R., and equal contribution), (*. (2021) Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In International Conference on Computer Vision (ICCV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{HorKai2021,
  author = {Hornakova*, Andrea and Kaiser*, Timo and Rolinek, Michal and Rosenhahn, Bodo and Swoboda, Paul and Henschel, Roberto and equal contribution), (*},
  booktitle = {International Conference on Computer Vision (ICCV)},
  keywords = {MOT Scalable leibnizailab myown},
  month = {oct},
  title = {Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths},
  year = 2021
  }

  %0 Conference Paper
  %1 HorKai2021
  %A Hornakova*, Andrea
  %A Kaiser*, Timo
  %A Rolinek, Michal
  %A Rosenhahn, Bodo
  %A Swoboda, Paul
  %A Henschel, Roberto
  %A equal contribution), (*
  %B International Conference on Computer Vision (ICCV)
  %D 2021
  %T Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
  %U https://arxiv.org/abs/2108.10606
• 1.

  Adhisantoso, Y. G., and Ostermann, J. (2021) Method for the Coding of Contact Matrix m56622, ISO/IEC JTC 1/SC 29/WG 8.

  BibTeXEndNoteBibSonomy

  @article{AdhO2021,
  author = {Adhisantoso, Yeremia Gunawan and Ostermann, Jörn},
  journal = {ISO/IEC JTC 1/SC 29/WG 8},
  keywords = {Contact Matrix Method leibnizailab myown},
  month = {apr},
  title = {Method for the Coding of Contact Matrix m56622},
  year = 2021
  }

  %0 Journal Article
  %1 AdhO2021
  %A Adhisantoso, Yeremia Gunawan
  %A Ostermann, Jörn
  %D 2021
  %J ISO/IEC JTC 1/SC 29/WG 8
  %T Method for the Coding of Contact Matrix m56622
• 1.

  Voges, J., Hernaez, M., Mattavelli, M., and Ostermann, J. (2021) An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data, Proceedings of the IEEE 109, 1607–1622.

  URLBibTeXEndNoteBibSonomy

  @article{VogHer2021a,
  author = {Voges, Jan and Hernaez, Mikel and Mattavelli, Marco and Ostermann, Jörn},
  journal = {Proceedings of the IEEE},
  keywords = {Introduction MPEG-G leibnizailab myown to},
  number = 9,
  pages = {1607-1622},
  title = {An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data},
  volume = 109,
  year = 2021
  }

  %0 Journal Article
  %1 VogHer2021a
  %A Voges, Jan
  %A Hernaez, Mikel
  %A Mattavelli, Marco
  %A Ostermann, Jörn
  %D 2021
  %J Proceedings of the IEEE
  %N 9
  %P 1607-1622
  %R 10.1109/JPROC.2021.3082027
  %T An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data
  %U https://doi.org/10.1109/JPROC.2021.3082027
  %V 109
• 1.

  Kuhnke, F., Ihler, S., and Ostermann, J. (2021) Relative Pose Consistency for Semi-Supervised Head Pose Estimation. In 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021).

  BibTeXEndNoteBibSonomy

  @inproceedings{KuhIhl2021,
  author = {Kuhnke, Felix and Ihler, Sontje and Ostermann, Jörn},
  booktitle = {16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)},
  keywords = {Consistency Estimation Head Pose leibnizailab myown},
  month = {dec},
  title = {Relative Pose Consistency for Semi-Supervised Head Pose Estimation},
  year = 2021
  }

  %0 Conference Paper
  %1 KuhIhl2021
  %A Kuhnke, Felix
  %A Ihler, Sontje
  %A Ostermann, Jörn
  %B 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
  %D 2021
  %T Relative Pose Consistency for Semi-Supervised Head Pose Estimation
• 1.

  Knura, M., Kluger, F., Zahtila, M., Schiewe, J., Rosenhahn, B., and Burghardt, D. (2021) Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden, ISPRS International Journal of Geo-Information.

  URLBibTeXEndNoteBibSonomy

  @article{KnuKlu2021,
  author = {Knura, Martin and Kluger, Florian and Zahtila, Moris and Schiewe, Jochen and Rosenhahn, Bodo and Burghardt, Dirk},
  journal = {ISPRS International Journal of Geo-Information},
  keywords = {Bicycle Dresden Infrastructure Study Urban leibnizailab myown of},
  month = {oct},
  title = {Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden},
  year = 2021
  }

  %0 Journal Article
  %1 KnuKlu2021
  %A Knura, Martin
  %A Kluger, Florian
  %A Zahtila, Moris
  %A Schiewe, Jochen
  %A Rosenhahn, Bodo
  %A Burghardt, Dirk
  %D 2021
  %J ISPRS International Journal of Geo-Information
  %R 10.3390/ijgi10110733
  %T Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
  %U https://doi.org/10.3390%2Fijgi10110733
• 1.

  Roy, S., Sural, S., Chhaya, N., Natarajan, A., and Ganguly, N. (2021) An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation., ACM Trans. Web 15, 9:1–9:25.

  URLBibTeXEndNoteBibSonomy

  @article{journals/corr/abs-2011-09754,
  author = {Roy, Soumyadeep and Sural, Shamik and Chhaya, Niyati and Natarajan, Anandhavelu and Ganguly, Niloy},
  journal = {ACM Trans. Web},
  keywords = {leibnizailab},
  number = 2,
  pages = {9:1-9:25},
  title = {An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation.},
  volume = 15,
  year = 2021
  }

  %0 Journal Article
  %1 journals/corr/abs-2011-09754
  %A Roy, Soumyadeep
  %A Sural, Shamik
  %A Chhaya, Niyati
  %A Natarajan, Anandhavelu
  %A Ganguly, Niloy
  %D 2021
  %J ACM Trans. Web
  %N 2
  %P 9:1-9:25
  %T An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation.
  %U http://dblp.uni-trier.de/db/journals/corr/corr2011.html#abs-2011-09754
  %V 15
• 1.

  Gritzner, D., and Ostermann, J. (2021) Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images. In 2021 IEEE Statistical Signal Processing Workshop (SSP), pp. 81–85.

  BibTeXEndNoteBibSonomy

  @inproceedings{GriOst2021,
  author = {Gritzner, Daniel and Ostermann, Jörn},
  booktitle = {2021 IEEE Statistical Signal Processing Workshop (SSP)},
  keywords = {Aerial Images Segmentation Semantic leibnizailab myown of},
  pages = {81--85},
  title = {Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images},
  year = 2021
  }

  %0 Conference Paper
  %1 GriOst2021
  %A Gritzner, Daniel
  %A Ostermann, Jörn
  %B 2021 IEEE Statistical Signal Processing Workshop (SSP)
  %D 2021
  %P 81--85
  %T Minimizing Manual Labeling Effort for The Semantic Segmentation of Aerial Images
• 1.

  Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., and Hutter, F. (2021) SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. In ArXiv: 2109.09831.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{LinEgg2021,
  author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Sass, René and Hutter, Frank},
  booktitle = {ArXiv: 2109.09831},
  keywords = {SMAC3 leibnizailab myown},
  title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
  year = 2021
  }

  %0 Conference Paper
  %1 LinEgg2021
  %A Lindauer, Marius
  %A Eggensperger, Katharina
  %A Feurer, Matthias
  %A Biedenkapp, André
  %A Deng, Difan
  %A Benjamins, Carolin
  %A Sass, René
  %A Hutter, Frank
  %B ArXiv: 2109.09831
  %D 2021
  %T SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
  %U https://arxiv.org/abs/2109.09831
• 1.

  Gritzner, D., and Ostermann, J. (2021) Semantic Segmentation of Aerial Images Using Binary Space Partitioning. In KI 2021: Advances in Artificial Intelligence, pp. 116–134.

  BibTeXEndNoteBibSonomy

  @inproceedings{GriOst2021a,
  author = {Gritzner, Daniel and Ostermann, Jörn},
  booktitle = {KI 2021: Advances in Artificial Intelligence},
  keywords = {Partitioning Segmentation Semantic Space leibnizailab myown},
  pages = {116--134},
  title = {Semantic Segmentation of Aerial Images Using Binary Space Partitioning},
  year = 2021
  }

  %0 Conference Paper
  %1 GriOst2021a
  %A Gritzner, Daniel
  %A Ostermann, Jörn
  %B KI 2021: Advances in Artificial Intelligence
  %D 2021
  %P 116--134
  %R 10.1007/978-3-030-87626-5_10
  %T Semantic Segmentation of Aerial Images Using Binary Space Partitioning
  %@ 978-3-030-87626-5
• 1.

  Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., and Lindauer, M. (2021) CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.

  BibTeXEndNoteBibSonomy

  @inproceedings{BenEim2021a,
  author = {Benjamins, Carolin and Eimer, Theresa and Schubert, Frederik and Biedenkapp, André and Rosenhahn, Bodo and Hutter, Frank and Lindauer, Marius},
  booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
  keywords = {Adaptive Contextual Learning Reinforcement and leibnizailab myown},
  month = {dec},
  title = {CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 BenEim2021a
  %A Benjamins, Carolin
  %A Eimer, Theresa
  %A Schubert, Frederik
  %A Biedenkapp, André
  %A Rosenhahn, Bodo
  %A Hutter, Frank
  %A Lindauer, Marius
  %B NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning
  %D 2021
  %T CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
• 1.

  Eimer, T., Benjamins, C., and Lindauer, M. (2021) Hyperparameters in Contextual RL are Highly Situational. In NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning.

  BibTeXEndNoteBibSonomy

  @inproceedings{EimBen2021a,
  author = {Eimer, Theresa and Benjamins, Carolin and Lindauer, Marius},
  booktitle = {NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning},
  keywords = {Contextual RL leibnizailab myown},
  month = {dec},
  title = {Hyperparameters in Contextual RL are Highly Situational},
  year = 2021
  }

  %0 Conference Paper
  %1 EimBen2021a
  %A Eimer, Theresa
  %A Benjamins, Carolin
  %A Lindauer, Marius
  %B NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning
  %D 2021
  %T Hyperparameters in Contextual RL are Highly Situational
• 1.

  Hartmann, F., and Ostermann, J. (2021) Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets. In Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference.

  BibTeXEndNoteBibSonomy

  @inproceedings{HarOst2021,
  author = {Hartmann, Fabian and Ostermann, Jörn},
  booktitle = {Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference},
  keywords = {Effect Flight Path leibnizailab myown},
  month = {nov},
  title = {Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets},
  year = 2021
  }

  %0 Conference Paper
  %1 HarOst2021
  %A Hartmann, Fabian
  %A Ostermann, Jörn
  %B Synthetic Aperture Radar (APSAR), 2021 IEEE 7th Asia-Pacific Conference
  %D 2021
  %T Investigation of the Effect of the Flight Path on the Three Dimensional Locatability of Targets
• 1.

  Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F. (2021) HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{EggMue2021,
  author = {Eggensperger, Katharina and Müller, Philipp and Mallik, Neeratyoy and Feurer, Matthias and Sass, René and Klein, Aaron and Awad, Noor and Lindauer, Marius and Hutter, Frank},
  booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)},
  keywords = {HPOBench Multi-Fidelity Reproducible leibnizailab myown},
  month = {dec},
  title = {HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO},
  year = 2021
  }

  %0 Conference Paper
  %1 EggMue2021
  %A Eggensperger, Katharina
  %A Müller, Philipp
  %A Mallik, Neeratyoy
  %A Feurer, Matthias
  %A Sass, René
  %A Klein, Aaron
  %A Awad, Noor
  %A Lindauer, Marius
  %A Hutter, Frank
  %B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track)
  %D 2021
  %T HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
  %U https://arxiv.org/abs/2109.06716
• 1.

  Mukherjee, R., Naik, A., Poddar, S., Dasgupta, S., and Ganguly, N. (2021) Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach. In SIGIR 2021.

  AbstractURLBibTeXEndNoteBibSonomy

  We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.

  @inproceedings{mukherjee2021understanding,
  abstract = {We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.},
  author = {Mukherjee, Rajdeep and Naik, Atharva and Poddar, Sriyash and Dasgupta, Soham and Ganguly, Niloy},
  booktitle = {SIGIR 2021},
  keywords = {leibnizailab myown},
  title = {Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach},
  year = 2021
  }

  %0 Conference Paper
  %1 mukherjee2021understanding
  %A Mukherjee, Rajdeep
  %A Naik, Atharva
  %A Poddar, Sriyash
  %A Dasgupta, Soham
  %A Ganguly, Niloy
  %B SIGIR 2021
  %D 2021
  %R 10.1145/3404835.3463080
  %T Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
  %U http://arxiv.org/abs/2105.03983
  %X We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.
• 1.

  Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., and Lindauer, M. (2021) Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS’21).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SpeBie2021,
  author = {Speck, David and Biedenkapp, André and Hutter, Frank and Mattmüller, Robert and Lindauer, Marius},
  booktitle = {Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21)},
  keywords = {Heuristic Selection leibnizailab myown},
  month = {aug},
  title = {Learning Heuristic Selection with Dynamic Algorithm Configuration},
  year = 2021
  }

  %0 Conference Paper
  %1 SpeBie2021
  %A Speck, David
  %A Biedenkapp, André
  %A Hutter, Frank
  %A Mattmüller, Robert
  %A Lindauer, Marius
  %B Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS'21)
  %D 2021
  %T Learning Heuristic Selection with Dynamic Algorithm Configuration
  %U https://arxiv.org/abs/2006.08246
• 1.

  Decker, M., Lammens, T., Ferster, A., Erlacher, M., Yoshimi, A., Niemeyer, C. M., Ernst, M. P. T., Raaijmakers, M. H. G. P., Duployez, N., Flaum, A., Steinemann, D., Schlegelberger, B., Illig, T., and Ripperger, T. (2021) Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies, Leukemia.

  BibTeXEndNoteBibSonomy

  @article{display:block).nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible-webkit-box-shadow:0,
  author = {Decker, Melanie and Lammens, Tim and Ferster, Alina and Erlacher, Miriam and Yoshimi, Ayami and Niemeyer, Charlotte M. and Ernst, Martijn P. T. and Raaijmakers, Marc H. G. P. and Duployez, Nicolas and Flaum, Andreas and Steinemann, Doris and Schlegelberger, Brigitte and Illig, Thomas and Ripperger, Tim},
  journal = {Leukemia},
  keywords = {l3s leibnizailab leukemia},
  month = {jul},
  title = {Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies},
  year = 2021
  }

  %0 Journal Article
  %1 display:block).nova-c-button--color-blue.nova-c-button--theme-solid:focus-visible-webkit-box-shadow:0
  %A Decker, Melanie
  %A Lammens, Tim
  %A Ferster, Alina
  %A Erlacher, Miriam
  %A Yoshimi, Ayami
  %A Niemeyer, Charlotte M.
  %A Ernst, Martijn P. T.
  %A Raaijmakers, Marc H. G. P.
  %A Duployez, Nicolas
  %A Flaum, Andreas
  %A Steinemann, Doris
  %A Schlegelberger, Brigitte
  %A Illig, Thomas
  %A Ripperger, Tim
  %D 2021
  %J Leukemia
  %T Functional classification of RUNX1 variants in familial platelet disorder with associated myeloid malignancies
• 1.

  Olatunji, I. E., Nejdl, W., and Khosla, M. (2021) Membership inference attack on graph neural networks. In IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) ).

  BibTeXEndNoteBibSonomy

  @inproceedings{olatunji2021membership,
  author = {Olatunji, Iyiola E and Nejdl, Wolfgang and Khosla, Megha},
  booktitle = {IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) )},
  keywords = {l3s leibnizailab myown},
  title = {Membership inference attack on graph neural networks},
  year = 2021
  }

  %0 Conference Paper
  %1 olatunji2021membership
  %A Olatunji, Iyiola E
  %A Nejdl, Wolfgang
  %A Khosla, Megha
  %B IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (short version presented in ICLR-21 Workshop on Distributed and Private Machine Learning (DPML) )
  %D 2021
  %T Membership inference attack on graph neural networks
• 1.

  Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2021) TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{BieRaj2021a,
  author = {Biedenkapp, Andre and Rajan, Raghu and Hutter, Frank and Lindauer, Marius},
  booktitle = {Proceedings of the international conference on machine learning (ICML)},
  keywords = {TempoRL leibnizailab myown},
  month = {jul},
  note = {To appear},
  title = {TempoRL: Learning When to Act},
  year = 2021
  }

  %0 Conference Paper
  %1 BieRaj2021a
  %A Biedenkapp, Andre
  %A Rajan, Raghu
  %A Hutter, Frank
  %A Lindauer, Marius
  %B Proceedings of the international conference on machine learning (ICML)
  %D 2021
  %T TempoRL: Learning When to Act
  %U https://arxiv.org/abs/2106.05262
• 1.

  Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2021) Bayesian Optimization with a Prior for the Optimum. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SouNar2021a,
  author = {Souza, Artur and Nardi, Luigi and Oliveira, Leonardo and Olukotun, Kunle and Lindauer, Marius and Hutter, Frank},
  booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
  keywords = {Bayesian Optimization leibnizailab myown},
  month = {sep},
  note = {To appear},
  title = {Bayesian Optimization with a Prior for the Optimum},
  year = 2021
  }

  %0 Conference Paper
  %1 SouNar2021a
  %A Souza, Artur
  %A Nardi, Luigi
  %A Oliveira, Leonardo
  %A Olukotun, Kunle
  %A Lindauer, Marius
  %A Hutter, Frank
  %B Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
  %D 2021
  %T Bayesian Optimization with a Prior for the Optimum
  %U https://arxiv.org/abs/2006.14608
• 1.

  Rumberg, L., Ehlert, H., Lüdtke, U., and Ostermann, J. (2021) Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning. In Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association.

  BibTeXEndNoteBibSonomy

  @inproceedings{RumEhl2021,
  author = {Rumberg, Lars and Ehlert, Hanna and Lüdtke, Ulrike and Ostermann, Jörn},
  booktitle = {Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association},
  keywords = {Age-Invariant Child End-to-End Recognition Speech Training leibnizailab myown},
  month = {aug},
  title = {Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 RumEhl2021
  %A Rumberg, Lars
  %A Ehlert, Hanna
  %A Lüdtke, Ulrike
  %A Ostermann, Jörn
  %B Proceedings INTERSPEECH 2021 -- 22th Annual Conference of the International Speech Communication Association
  %D 2021
  %T Age-Invariant Training for End-to-End Child Speech Recognition using Adversarial Multi-Task Learning
• 1.

  Awiszus, M., Schubert, F., and Rosenhahn, B. (2021) World-GAN: a Generative Model for Minecraft Worlds. In IEEE Conference on Games.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AwiSch2021a,
  author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  booktitle = {IEEE Conference on Games},
  keywords = {World-GAN leibnizailab myown},
  month = {aug},
  title = {World-GAN: a Generative Model for Minecraft Worlds},
  year = 2021
  }

  %0 Conference Paper
  %1 AwiSch2021a
  %A Awiszus, Maren
  %A Schubert, Frederik
  %A Rosenhahn, Bodo
  %B IEEE Conference on Games
  %D 2021
  %T World-GAN: a Generative Model for Minecraft Worlds
  %U /brokenurl#arxiv
• 1.

  Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021) Automatic Risk Adaptation in Distributional Reinforcement Learning. In Arxiv Preprint.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SchEim2021,
  author = {Schubert, Frederik and Eimer, Theresa and Rosenhahn, Bodo and Lindauer, Marius},
  booktitle = {Arxiv Preprint},
  keywords = {Adaptation Learning Reinforcement Risk leibnizailab myown},
  month = {jun},
  title = {Automatic Risk Adaptation in Distributional Reinforcement Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 SchEim2021
  %A Schubert, Frederik
  %A Eimer, Theresa
  %A Rosenhahn, Bodo
  %A Lindauer, Marius
  %B Arxiv Preprint
  %D 2021
  %T Automatic Risk Adaptation in Distributional Reinforcement Learning
  %U https://arxiv.org/abs/2106.06317
• 1.

  Benjak, M., Samayoa, Y., and Ostermann, J. (2021) Neural Network Based Error Concealment for VVC. In Proceedings of the 28th IEEE International Conference on Image Processing (ICIP).

  BibTeXEndNoteBibSonomy

  @inproceedings{BenSam2021a,
  author = {Benjak, Martin and Samayoa, Yasser and Ostermann, Jörn},
  booktitle = {Proceedings of the 28th IEEE International Conference on Image Processing (ICIP)},
  keywords = {Network Neural VVC leibnizailab myown},
  month = {sep},
  note = {accepted for publication},
  title = {Neural Network Based Error Concealment for VVC},
  year = 2021
  }

  %0 Conference Paper
  %1 BenSam2021a
  %A Benjak, Martin
  %A Samayoa, Yasser
  %A Ostermann, Jörn
  %B Proceedings of the 28th IEEE International Conference on Image Processing (ICIP)
  %D 2021
  %T Neural Network Based Error Concealment for VVC
• 1.

  Kellermann, C., Adhisantoso, Y. G., Munderloh, M., and Ostermann, J. (2021) Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted). In Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

  BibTeXEndNoteBibSonomy

  @inproceedings{KelGun2021a,
  author = {Kellermann, Christoph and Adhisantoso, Yeramia Gunawan and Munderloh, Marco and Ostermann, Jörn},
  booktitle = {Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
  keywords = {Adaptive Life Prediction Remaining Useful leibnizailab myown},
  month = {jul},
  title = {Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted)},
  year = 2021
  }

  %0 Conference Paper
  %1 KelGun2021a
  %A Kellermann, Christoph
  %A Adhisantoso, Yeramia Gunawan
  %A Munderloh, Marco
  %A Ostermann, Jörn
  %B Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
  %D 2021
  %T Introduction to an Adaptive Remaining Useful Life Prediction for forming tools (accepted)
• 1.

  Eimer, T., Biedenkapp, A., Hutter, F., and Lindauer, M. (2021) Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{EimBie2021a,
  author = {Eimer, Theresa and Biedenkapp, Andre and Hutter, Frank and Lindauer, Marius},
  booktitle = {Proceedings of the international conference on machine learning (ICML)},
  keywords = {Contextual Learning Reinforcement leibnizailab myown},
  month = {jul},
  note = {To appear},
  title = {Self-Paced Context Evaluation for Contextual Reinforcement Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 EimBie2021a
  %A Eimer, Theresa
  %A Biedenkapp, Andre
  %A Hutter, Frank
  %A Lindauer, Marius
  %B Proceedings of the international conference on machine learning (ICML)
  %D 2021
  %T Self-Paced Context Evaluation for Contextual Reinforcement Learning
  %U https://arxiv.org/abs/2106.05110
• 1.

  Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., and Hutter, F. (2021) Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML’21.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{GueHau2021a,
  author = {Guerrero-Viu, Julia and Hauns, Sven and Izquierdo, Sergio and Miotto, Guilherme and Schrodi, Simon and Biedenkapp, Andre and Elsken, Thomas and Deng, Difan and Lindauer, Marius and Hutter, Frank},
  booktitle = {Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21},
  keywords = {Bag Baselines Hyperparameter Optimization leibnizailab myown of},
  month = {jul},
  title = {Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization},
  year = 2021
  }

  %0 Conference Paper
  %1 GueHau2021a
  %A Guerrero-Viu, Julia
  %A Hauns, Sven
  %A Izquierdo, Sergio
  %A Miotto, Guilherme
  %A Schrodi, Simon
  %A Biedenkapp, Andre
  %A Elsken, Thomas
  %A Deng, Difan
  %A Lindauer, Marius
  %A Hutter, Frank
  %B Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21
  %D 2021
  %T Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
  %U https://arxiv.org/abs/2105.01015
• 1.

  Mukherjee, R., Naik, A., Poddar, S., Dasgupta, S., and Ganguly, N. (2021) Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach. In .

  AbstractURLBibTeXEndNoteBibSonomy

  We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.

  @inproceedings{mukherjee2021understanding,
  abstract = {We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.},
  author = {Mukherjee, Rajdeep and Naik, Atharva and Poddar, Sriyash and Dasgupta, Soham and Ganguly, Niloy},
  keywords = {l3s leibnizailab},
  note = {cite arxiv:2105.03983Comment: 5 pages, Short Paper accepted at SIGIR 2021},
  title = {Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach},
  year = 2021
  }

  %0 Conference Paper
  %1 mukherjee2021understanding
  %A Mukherjee, Rajdeep
  %A Naik, Atharva
  %A Poddar, Sriyash
  %A Dasgupta, Soham
  %A Ganguly, Niloy
  %D 2021
  %R 10.1145/3404835.3463080
  %T Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
  %U http://arxiv.org/abs/2105.03983
  %X We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution.
• 1.

  Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., and Lindauer, M. (2021) DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Proceedings of the international joint conference on artificial intelligence (IJCAI).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{EimBie2021b,
  author = {Eimer, Theresa and Biedenkapp, André and Reimer, Maximilian and Adriaensen, Steven and Hutter, Frank and Lindauer, Marius},
  booktitle = {Proceedings of the international joint conference on artificial intelligence (IJCAI)},
  keywords = {DACBench leibnizailab myown},
  month = {aug},
  note = {To appear.},
  title = {DACBench: A Benchmark Library for Dynamic Algorithm Configuration},
  year = 2021
  }

  %0 Conference Paper
  %1 EimBie2021b
  %A Eimer, Theresa
  %A Biedenkapp, André
  %A Reimer, Maximilian
  %A Adriaensen, Steven
  %A Hutter, Frank
  %A Lindauer, Marius
  %B Proceedings of the international joint conference on artificial intelligence (IJCAI)
  %D 2021
  %T DACBench: A Benchmark Library for Dynamic Algorithm Configuration
  %U https://arxiv.org/abs/2105.08541
• 1.

  Booth, A., Reed, A. B., Ponzo, S., Yassaee, A., Aral, M., Plans, D., Labrique, A., and Mohan, D. (2021) Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis, PLOS ONE, Public Library of Science 16, 1–30.

  AbstractURLBibTeXEndNoteBibSonomy

  Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.

  @article{10.1371/journal.pone.0247461,
  abstract = {Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.},
  author = {Booth, Adam and Reed, Angus Bruno and Ponzo, Sonia and Yassaee, Arrash and Aral, Mert and Plans, David and Labrique, Alain and Mohan, Diwakar},
  journal = {PLOS ONE},
  keywords = {l3s leibnizailab},
  month = {03},
  number = 3,
  pages = {1-30},
  publisher = {Public Library of Science},
  title = {Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis},
  volume = 16,
  year = 2021
  }

  %0 Journal Article
  %1 10.1371/journal.pone.0247461
  %A Booth, Adam
  %A Reed, Angus Bruno
  %A Ponzo, Sonia
  %A Yassaee, Arrash
  %A Aral, Mert
  %A Plans, David
  %A Labrique, Alain
  %A Mohan, Diwakar
  %D 2021
  %I Public Library of Science
  %J PLOS ONE
  %N 3
  %P 1-30
  %R 10.1371/journal.pone.0247461
  %T Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis
  %U https://doi.org/10.1371/journal.pone.0247461
  %V 16
  %X Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.
• 1.

  Hachmann, H., Krüger, B., Rosenhahn, B., and Nogueira, W. (2021) Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation. In International Symposium on Biomedical Imaging, ISBI.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{HacKru2021a,
  author = {Hachmann, Hendrik and Krüger, Benjamin and Rosenhahn, Bodo and Nogueira, Waldo},
  booktitle = {International Symposium on Biomedical Imaging, ISBI},
  keywords = {Beam Cochlear Computed Cone Electrodes Implant Tomography leibnizailab myown},
  month = {apr},
  title = {Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation},
  year = 2021
  }

  %0 Conference Paper
  %1 HacKru2021a
  %A Hachmann, Hendrik
  %A Krüger, Benjamin
  %A Rosenhahn, Bodo
  %A Nogueira, Waldo
  %B International Symposium on Biomedical Imaging, ISBI
  %D 2021
  %T Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation
  %U https://arxiv.org/abs/2103.10434
• 1.

  Hao, C., Liao, W., Tang, X., Yang, M. Y., Sester, M., and Rosenhahn, B. (2021) AMENet: Attentive Maps Encoder Network for Trajectory Prediction. In ISPRS Journal of Photogrammetry and Remote Sensing, pp. 253–266.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{CheLia2021b,
  author = {Hao, Cheng and Liao, Wentong and Tang, Xuejiao and Yang, Michael Ying and Sester, Monika and Rosenhahn, Bodo},
  booktitle = {ISPRS Journal of Photogrammetry and Remote Sensing},
  keywords = {AMENet leibnizailab myown},
  pages = {253--266},
  title = {AMENet: Attentive Maps Encoder Network for Trajectory Prediction},
  volume = 172,
  year = 2021
  }

  %0 Conference Paper
  %1 CheLia2021b
  %A Hao, Cheng
  %A Liao, Wentong
  %A Tang, Xuejiao
  %A Yang, Michael Ying
  %A Sester, Monika
  %A Rosenhahn, Bodo
  %B ISPRS Journal of Photogrammetry and Remote Sensing
  %D 2021
  %P 253--266
  %R https://doi.org/10.1016/j.isprsjprs.2020.12.004
  %T AMENet: Attentive Maps Encoder Network for Trajectory Prediction
  %U https://doi.org/10.1016/j.isprsjprs.2020.12.004
  %V 172
• 1.

  Pestel-Schiller, U., Hu, K., Gritzner, D., and Ostermann, J. (2021) Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN. In 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15.

  BibTeXEndNoteBibSonomy

  @inproceedings{PesHu2021a,
  author = {Pestel-Schiller, Ulrike and Hu, Kai and Gritzner, Daniel and Ostermann, Jörn},
  booktitle = {11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15},
  keywords = {Bands CNN Constrained Hyperspectral Relevant Spectrally leibnizailab myown},
  month = {mar},
  title = {Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN},
  year = 2021
  }

  %0 Conference Paper
  %1 PesHu2021a
  %A Pestel-Schiller, Ulrike
  %A Hu, Kai
  %A Gritzner, Daniel
  %A Ostermann, Jörn
  %B 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Paper 15
  %D 2021
  %T Determination of Relevant Hyperspectral Bands Using a Spectrally Constrained CNN
• 1.

  Xue, Y., Kudenko, D., and Khosla, M. (2021) Graph Learning based Generation of Abstractions for Reinforcement Learning. In Adaptive and Learning Agents Workshop at AAMAS 2021.

  AbstractURLBibTeXEndNoteBibSonomy

  The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP.

  @inproceedings{yuanxue2021graph,
  abstract = {The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP.},
  author = {Xue, Yuan and Kudenko, Daniel and Khosla, Megha},
  booktitle = {Adaptive and Learning Agents Workshop at AAMAS 2021},
  keywords = {leibnizailab myown},
  title = {Graph Learning based Generation of Abstractions for Reinforcement Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 yuanxue2021graph
  %A Xue, Yuan
  %A Kudenko, Daniel
  %A Khosla, Megha
  %B Adaptive and Learning Agents Workshop at AAMAS 2021
  %D 2021
  %T Graph Learning based Generation of Abstractions for Reinforcement Learning
  %U https://ala2021.vub.ac.be/papers/ALA2021_paper_57.pdf
  %X The application of Reinforcement Learning (RL) Algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions, however they assumed that the abstractions are provided by a domain expert. In this work we propose a new approach to automatically construct Abstract Markov Decision Processes (AMDPs) for Potential Based Reward Shaping to improve the sample efficiency of RL algorithms. Our approach to construct abstract states is inspired by graph representation learning methods and effectively encodes topological and reward structure of the ground level MDP. We perform large scale quantitative experiments on Flag Collection domain. We show improvements of up to 6.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDP we demonstrate the capability of our approach to preserve topological and reward structure of the ground level MDP.
• 1.

  Pestel-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.

  BibTeXEndNoteBibSonomy

  @inproceedings{PesOst2021a,
  author = {Pestel-Schiller, Ulrike and Ostermann, Jörn},
  booktitle = {13th European Conference on Synthetic Aperture Radar},
  keywords = {Compressed HEVC Images JPEG SAR and leibnizailab myown},
  month = {mar},
  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, Jörn
  %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
• 1.

  Dong, N. T., Brogden, G., Gerold, G., and Khosla, M. (2021) A multitask transfer learning framework for the prediction of virus-human protein--protein interactions, BMC Bioinformatics 22, 572.

  AbstractURLBibTeXEndNoteBibSonomy

  Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.

  @article{dong21,
  abstract = {Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.},
  author = {Dong, Ngan Thi and Brogden, Graham and Gerold, Gisa and Khosla, Megha},
  journal = {BMC Bioinformatics},
  keywords = {l3s leibnizailab myown},
  number = 1,
  pages = 572,
  title = {A multitask transfer learning framework for the prediction of virus-human protein--protein interactions},
  volume = 22,
  year = 2021
  }

  %0 Journal Article
  %1 dong21
  %A Dong, Ngan Thi
  %A Brogden, Graham
  %A Gerold, Gisa
  %A Khosla, Megha
  %D 2021
  %J BMC Bioinformatics
  %N 1
  %P 572
  %R 10.1186/s12859-021-04484-y
  %T A multitask transfer learning framework for the prediction of virus-human protein--protein interactions
  %U https://doi.org/10.1186/s12859-021-04484-y
  %V 22
  %X Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein--protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.
• 1.

  Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., and Bischl, B. (2021) Towards Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML’21.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{MooHer2021,
  author = {Moosbauer, Julia and Herbinger, Julia and Casalicchio, Giuseppe and Lindauer, Marius and Bischl, Bernd},
  booktitle = {Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21},
  keywords = {Dependence Optimization Partial Plots leibnizailab myown},
  month = {jul},
  title = {Towards Explaining Hyperparameter Optimization via Partial Dependence Plots},
  year = 2021
  }

  %0 Conference Paper
  %1 MooHer2021
  %A Moosbauer, Julia
  %A Herbinger, Julia
  %A Casalicchio, Giuseppe
  %A Lindauer, Marius
  %A Bischl, Bernd
  %B Proceedings of the international workshop on Automated Machine Learning (AutoML) at ICML'21
  %D 2021
  %T Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
  %U https://openreview.net/forum?id=lZr9s1x0mE
• 1.

  Zimmer, L., Lindauer, M., and Hutter, F. (2021) Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL, IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 3079–3090.

  URLBibTeXEndNoteBibSonomy

  @article{ZimLin2021a,
  author = {Zimmer, Lucas and Lindauer, Marius and Hutter, Frank},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  keywords = {Auto-PyTorch MetaLearning leibnizailab myown},
  month = {aug},
  number = 9,
  pages = {3079 - 3090},
  title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
  volume = 43,
  year = 2021
  }

  %0 Journal Article
  %1 ZimLin2021a
  %A Zimmer, Lucas
  %A Lindauer, Marius
  %A Hutter, Frank
  %D 2021
  %J IEEE Transactions on Pattern Analysis and Machine Intelligence
  %N 9
  %P 3079 - 3090
  %T Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
  %U /brokenurl#Arxiv, IEEE TPAMI
  %V 43
• 1.

  Schubert, F., Eimer, T., Rosenhahn, B., and Lindauer, M. (2021) Towards Automatic Risk Adaption in Distributional Reinforcement Learning. In Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SchEim2021b,
  author = {Schubert, Frederik and Eimer, Theresa and Rosenhahn, Bodo and Lindauer, Marius},
  booktitle = {Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML)},
  keywords = {Adaption Learning Reinforcement Risk leibnizailab myown},
  month = {jul},
  title = {Towards Automatic Risk Adaption in Distributional Reinforcement Learning},
  year = 2021
  }

  %0 Conference Paper
  %1 SchEim2021b
  %A Schubert, Frederik
  %A Eimer, Theresa
  %A Rosenhahn, Bodo
  %A Lindauer, Marius
  %B Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning (ICML)
  %D 2021
  %T Towards Automatic Risk Adaption in Distributional Reinforcement Learning
  %U https://arxiv.org/abs/2106.06317
• 1.

  Wu, W., Li, B., Luo, C., and Nejdl, W. (2021) Hashing-Accelerated Graph Neural Networks for Link Prediction. In .

  AbstractURLBibTeXEndNoteBibSonomy

  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 leibnizailab machinelearning},
  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.
• 1.

  Bellinghausen, C., Pletz, M. W., Rupp, J., Witzenrath, M., Welsch, C., Zeuzem, S., Trebicka, J., Rohde, G. G. U., and of the CAPNETZ study group, M. (2021) Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia, Gut 70, 221–222.

  BibTeXEndNoteBibSonomy

  @article{noauthororeditor,
  author = {Bellinghausen, Carla and Pletz, Mathias W. and Rupp, Jan and Witzenrath, Martin and Welsch, Christoph and Zeuzem, Stefan and Trebicka, Jonel and Rohde, Gernot G. U. and of the CAPNETZ study group, Members},
  journal = {Gut},
  keywords = {l3s leibnizailab},
  month = {jan},
  number = 1,
  pages = {221-222},
  title = {Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia},
  volume = 70,
  year = 2021
  }

  %0 Journal Article
  %1 noauthororeditor
  %A Bellinghausen, Carla
  %A Pletz, Mathias W.
  %A Rupp, Jan
  %A Witzenrath, Martin
  %A Welsch, Christoph
  %A Zeuzem, Stefan
  %A Trebicka, Jonel
  %A Rohde, Gernot G. U.
  %A of the CAPNETZ study group, Members
  %D 2021
  %J Gut
  %N 1
  %P 221-222
  %R doi: 10.1136/gutjnl-2020-320876
  %T Chronic liver disease negatively affects outcome in hospitalised patients with community-acquired pneumonia
  %V 70
• 1.

  Becker, M., Strengert, M., Junker, D., Kaiser, P. D., Kerrinnes, T., Traenkle, B., Dinter, H., Häring, J., Ghozzi, S., Zeck, A., Weise, F., Peter, A., Hörber, S., Fink, S., Ruoff, F., Dulovic, A., Bakchoul, T., Baillot, A., Lohse, S., Cornberg, M., Illig, T., Gottlieb, J., Smola, S., Karch, A., Berger, K., Rammensee, H.-G., Schenke-Layland, K., Nelde, A., Märklin, M., Heitmann, J. S., Walz, J. S., Templin, M., Joos, T. O., Rothbauer, U., Krause, G., and Schneiderhan-Marra, N. (2021) Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity, Nat Commun 12.

  AbstractBibTeXEndNoteBibSonomy

  The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.

  @article{noauthororeditor,
  abstract = {The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.},
  author = {Becker, Matthias and Strengert, Monika and Junker, Daniel and Kaiser, Philipp D. and Kerrinnes, Tobias and Traenkle, Bjoern and Dinter, Heiko and Häring, Julia and Ghozzi, Stéphane and Zeck, Anne and Weise, Frank and Peter, Andreas and Hörber, Sebastian and Fink, Simon and Ruoff, Felix and Dulovic, Alex and Bakchoul, Tamam and Baillot, Armin and Lohse, Stefan and Cornberg, Markus and Illig, Thomas and Gottlieb, Jens and Smola, Sigrun and Karch, André and Berger, Klaus and Rammensee, Hans-Georg and Schenke-Layland, Katja and Nelde, Annika and Märklin, Melanie and Heitmann, Jonas S. and Walz, Juliane S. and Templin, Markus and Joos, Thomas O. and Rothbauer, Ulrich and Krause, Gérard and Schneiderhan-Marra, Nicole},
  journal = {Nat Commun},
  keywords = {covid l3s leibnizailab},
  month = {feb},
  number = 1152,
  title = {Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity},
  volume = 12,
  year = 2021
  }

  %0 Journal Article
  %1 noauthororeditor
  %A Becker, Matthias
  %A Strengert, Monika
  %A Junker, Daniel
  %A Kaiser, Philipp D.
  %A Kerrinnes, Tobias
  %A Traenkle, Bjoern
  %A Dinter, Heiko
  %A Häring, Julia
  %A Ghozzi, Stéphane
  %A Zeck, Anne
  %A Weise, Frank
  %A Peter, Andreas
  %A Hörber, Sebastian
  %A Fink, Simon
  %A Ruoff, Felix
  %A Dulovic, Alex
  %A Bakchoul, Tamam
  %A Baillot, Armin
  %A Lohse, Stefan
  %A Cornberg, Markus
  %A Illig, Thomas
  %A Gottlieb, Jens
  %A Smola, Sigrun
  %A Karch, André
  %A Berger, Klaus
  %A Rammensee, Hans-Georg
  %A Schenke-Layland, Katja
  %A Nelde, Annika
  %A Märklin, Melanie
  %A Heitmann, Jonas S.
  %A Walz, Juliane S.
  %A Templin, Markus
  %A Joos, Thomas O.
  %A Rothbauer, Ulrich
  %A Krause, Gérard
  %A Schneiderhan-Marra, Nicole
  %D 2021
  %J Nat Commun
  %N 1152
  %R https://doi.org/10.1038/s41467-021-20973-3
  %T Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity
  %V 12
  %X The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.
• 1.

  Hachmann, H., Krüger, B., Rosenhahn, B., and Nogueira, W. (2021) Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 593–597.

  AbstractURLBibTeXEndNoteBibSonomy

  Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.

  @inproceedings{9433845,
  abstract = {Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.},
  author = {Hachmann, Hendrik and Krüger, Benjamin and Rosenhahn, Bodo and Nogueira, Waldo},
  booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  keywords = {cochlear l3s leibnizailab},
  month = {April},
  pages = {593-597},
  title = {Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation},
  year = 2021
  }

  %0 Conference Paper
  %1 9433845
  %A Hachmann, Hendrik
  %A Krüger, Benjamin
  %A Rosenhahn, Bodo
  %A Nogueira, Waldo
  %B 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
  %D 2021
  %P 593-597
  %R 10.1109/ISBI48211.2021.9433845
  %T Localization Of Cochlear Implant Electrodes From Cone Beam Computed Tomography Using Particle Belief Propagation
  %U https://ieeexplore.ieee.org/abstract/document/9433845
  %X Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming.We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations.We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.
• 1.

  Wandt, B., Rudolph, M., Zell, P., Rhodin, H., and Rosenhahn, B. (2021) CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild. In Computer Vision and Pattern Recognition (CVPR).

  BibTeXEndNoteBibSonomy

  @inproceedings{WanRud2021a,
  author = {Wandt, Bastian and Rudolph, Marco and Zell, Petrissa and Rhodin, Helge and Rosenhahn, Bodo},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  keywords = {l3s leibnizailab},
  month = {june},
  title = {CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild},
  year = 2021
  }

  %0 Conference Paper
  %1 WanRud2021a
  %A Wandt, Bastian
  %A Rudolph, Marco
  %A Zell, Petrissa
  %A Rhodin, Helge
  %A Rosenhahn, Bodo
  %B Computer Vision and Pattern Recognition (CVPR)
  %D 2021
  %T CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild
• 1.

  Luo, C., Lin, J., Cai, S., Chen, X., He, B., Qiao, B., Zhao, P., Lin, Q., Zhang, H., Wu, W., Rajmohan, S., and Zhang, D. (2021) AutoCCAG: An Automated Approach to Constrained Covering Array Generation. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp. 201–212.

  AbstractURLBibTeXEndNoteBibSonomy

  Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.

  @inproceedings{9402109,
  abstract = {Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.},
  author = {Luo, Chuan and Lin, Jinkun and Cai, Shaowei and Chen, Xin and He, Bing and Qiao, Bo and Zhao, Pu and Lin, Qingwei and Zhang, Hongyu and Wu, Wei and Rajmohan, Saravanakumar and Zhang, Dongmei},
  booktitle = {2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)},
  keywords = {l3s leibnizailab},
  month = {may},
  pages = {201-212},
  title = {AutoCCAG: An Automated Approach to Constrained Covering Array Generation},
  year = 2021
  }

  %0 Conference Paper
  %1 9402109
  %A Luo, Chuan
  %A Lin, Jinkun
  %A Cai, Shaowei
  %A Chen, Xin
  %A He, Bing
  %A Qiao, Bo
  %A Zhao, Pu
  %A Lin, Qingwei
  %A Zhang, Hongyu
  %A Wu, Wei
  %A Rajmohan, Saravanakumar
  %A Zhang, Dongmei
  %B 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
  %D 2021
  %P 201-212
  %R 10.1109/ICSE43902.2021.00030
  %T AutoCCAG: An Automated Approach to Constrained Covering Array Generation
  %U https://ieeexplore.ieee.org/document/9402109
  %X Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t>=4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.
• 1.

  Luo, C., Zhao, P., Qiao, B., Wu, Y., Zhang, H., Wu, W., Lu, W., Dang, Y., Rajmohan, S., Lin, Q., and Zhang, D. (2021) NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms. In Proceedings of the Web Conference 2021, ACM.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{Luo_2021,
  author = {Luo, Chuan and Zhao, Pu and Qiao, Bo and Wu, Youjiang and Zhang, Hongyu and Wu, Wei and Lu, Weihai and Dang, Yingnong and Rajmohan, Saravanakumar and Lin, Qingwei and Zhang, Dongmei},
  booktitle = {Proceedings of the Web Conference 2021},
  keywords = {cloudplatforms l3s leibnizailab},
  month = {apr},
  publisher = {ACM},
  title = {NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms},
  year = 2021
  }

  %0 Conference Paper
  %1 Luo_2021
  %A Luo, Chuan
  %A Zhao, Pu
  %A Qiao, Bo
  %A Wu, Youjiang
  %A Zhang, Hongyu
  %A Wu, Wei
  %A Lu, Weihai
  %A Dang, Yingnong
  %A Rajmohan, Saravanakumar
  %A Lin, Qingwei
  %A Zhang, Dongmei
  %B Proceedings of the Web Conference 2021
  %D 2021
  %I ACM
  %R 10.1145/3442381.3449867
  %T NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms
  %U https://doi.org/10.1145%2F3442381.3449867
• 1.

  Zhao, 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.

  AbstractURLBibTeXEndNoteBibSonomy

  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 leibnizailab},
  month = {june},
  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.
• 1.

  Holzapfel, 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.

  AbstractURLBibTeXEndNoteBibSonomy

  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 leibnizailab},
  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.
• 1.

  He, S., Liao, W., Yang, M. Y., Yang, Y., Song, Y.-Z., Rosenhahn, B., and Xiang, T. (2021) Context-Aware Layout to Image Generation with Enhanced Object Appearance. In IEEE Conference on Computer Vision and Pattern Recognition.

  BibTeXEndNoteBibSonomy

  @inproceedings{HeLia2021,
  author = {He, Sen and Liao, Wentong and Yang, Michael Ying and Yang, Yongxin and Song, Yi-Zhe and Rosenhahn, Bodo and Xiang, Tao},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
  keywords = {Context-Aware Layout leibnizailab myown},
  month = {jun},
  title = {Context-Aware Layout to Image Generation with Enhanced Object Appearance},
  year = 2021
  }

  %0 Conference Paper
  %1 HeLia2021
  %A He, Sen
  %A Liao, Wentong
  %A Yang, Michael Ying
  %A Yang, Yongxin
  %A Song, Yi-Zhe
  %A Rosenhahn, Bodo
  %A Xiang, Tao
  %B IEEE Conference on Computer Vision and Pattern Recognition
  %D 2021
  %T Context-Aware Layout to Image Generation with Enhanced Object Appearance
• 1.

  Warnstorf, D., Bawadi, R., Schienke, A., Strasser, R., Schmidt, G., Illig, T., Tauscher, M., Thol, F., Heuser, M., Steinemann, D., Davenport, C., Schlegelberger, B., Behrens, Y. L., and Göhring, G. (2021) Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype, Genes Chromosomes Cancer 60, 452–457.

  AbstractBibTeXEndNoteBibSonomy

  A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17).

  @article{unbalanced,
  abstract = {A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17).},
  author = {Warnstorf, Daria and Bawadi, Randa and Schienke, Andrea and Strasser, Renate and Schmidt, Gunnar and Illig, Thomas and Tauscher, Marcel and Thol, Felicitas and Heuser, Michael and Steinemann, Doris and Davenport, Claudia and Schlegelberger, Brigitte and Behrens, Yvonne Lisa and Göhring, Gudrun},
  journal = {Genes Chromosomes Cancer},
  keywords = {l3s leibnizailab},
  month = {jan},
  number = 6,
  pages = {452-457},
  title = {Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype},
  volume = 60,
  year = 2021
  }

  %0 Journal Article
  %1 unbalanced
  %A Warnstorf, Daria
  %A Bawadi, Randa
  %A Schienke, Andrea
  %A Strasser, Renate
  %A Schmidt, Gunnar
  %A Illig, Thomas
  %A Tauscher, Marcel
  %A Thol, Felicitas
  %A Heuser, Michael
  %A Steinemann, Doris
  %A Davenport, Claudia
  %A Schlegelberger, Brigitte
  %A Behrens, Yvonne Lisa
  %A Göhring, Gudrun
  %D 2021
  %J Genes Chromosomes Cancer
  %N 6
  %P 452-457
  %R https://doi.org/10.1002/gcc.22938
  %T Unbalanced translocation der(5;17) resulting in a TP53 loss as recurrent aberration in myelodysplastic syndrome and acute myeloid leukemia with complex karyotype
  %V 60
  %X A complex karyotype, detected in myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML), is associated with a reduced median survival. The most frequent chromosomal aberrations in complex karyotypes are deletions of 5q and 17p harboring the tumor suppressor gene TP53. The unbalanced translocation der(5;17) involving chromosome 5q and 17p is a recurrent aberration in MDS/AML, resulting in TP53 loss. We analyzed the karyotypes of 178 patients with an unbalanced translocation der(5;17) using fluorescence R−/G-banding analysis. Whenever possible, fluorescence in situ hybridization (FISH) (n = 138/141), multicolor FISH (n = 8), telomere length measurement (n = 9), targeted DNA sequencing (n = 13), array-CGH (n = 7) and targeted RNA sequencing (n = 2) were conducted. The der(5;17) aberration was accompanied with loss of genetic material in 7q (53%), −7 (27%), gain of 21q (29%), +8 (17%) and − 18 (16%) and all analyzed patients (n = 13) showed a (likely) pathogenic variant inTP53. The der(5;17) cohort showed significantly shortened telomeres in comparison to the healthy age-matched controls (P < .05), but there was no significant telomere shortening in comparison to MDS/AML patients with a complex karyotype without der(5;17). No fusion genes resulted from the unbalanced translocation. This study demonstrates that the unbalanced translocation der(5;17) is associated with a biallelic inactivation of TP53 due to a deletion of TP53 in one allele and a pathogenic variant of the second TP53 allele. Since the breakpoints are located within (near-) heterochromatic regions, alterations of DNA methylation or histone modifications may be involved in the generation of der(5;17).
• 1.

  Hinrichs, R., Schmidt, A., Koslowski, J., Ostermann, J., and Denkena, B. (2021) Analysis of the impact of data compression on condition monitoring algorithms for ball screws. In CMMO CIRP 2021.

  BibTeXEndNoteBibSonomy

  @inproceedings{HinSch2021,
  author = {Hinrichs, Reemt and Schmidt, Alexander and Koslowski, Julian and Ostermann, Jörn and Denkena, Berend},
  booktitle = {CMMO CIRP 2021},
  keywords = {algorithms ball condition for leibnizailab monitoring myown screws},
  title = {Analysis of the impact of data compression on condition monitoring algorithms for ball screws},
  year = 2021
  }

  %0 Conference Paper
  %1 HinSch2021
  %A Hinrichs, Reemt
  %A Schmidt, Alexander
  %A Koslowski, Julian
  %A Ostermann, Jörn
  %A Denkena, Berend
  %B CMMO CIRP 2021
  %D 2021
  %T Analysis of the impact of data compression on condition monitoring algorithms for ball screws
• 1.

  Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Gyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Jin, W., Wang, P., Wu, C., Youcheng, X., Zela, A., and Zhang, Y. (2021) Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence 1–18.

  BibTeXEndNoteBibSonomy

  @article{LiuPav2021a,
  author = {Liu, Zhengying and Pavao, Adrien and Xu, Zhen and Escalera, Sergio and Ferreira, Fabio and Gyon, Isabelle and Hong, Sirui and Hutter, Frank and Ji, Rongrong and Junior, Julio Jacques and Li, Ge and Lindauer, Marius and Luo, Zhipeng and Madadi, Meysam and Nierhoff, Thomas and Niu, Kangning and Pan, Chunguang and Stoll, Danny and Treguer, Sebastien and Jin, Wang and Wang, Peng and Wu, Chenglin and Youcheng, Xiong and Zela, Arber and Zhang, Yang},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  keywords = {2019 AutoDL ChaLearn challenge leibnizailab myown},
  note = {To appear},
  pages = {1-18},
  title = {Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019},
  year = 2021
  }

  %0 Journal Article
  %1 LiuPav2021a
  %A Liu, Zhengying
  %A Pavao, Adrien
  %A Xu, Zhen
  %A Escalera, Sergio
  %A Ferreira, Fabio
  %A Gyon, Isabelle
  %A Hong, Sirui
  %A Hutter, Frank
  %A Ji, Rongrong
  %A Junior, Julio Jacques
  %A Li, Ge
  %A Lindauer, Marius
  %A Luo, Zhipeng
  %A Madadi, Meysam
  %A Nierhoff, Thomas
  %A Niu, Kangning
  %A Pan, Chunguang
  %A Stoll, Danny
  %A Treguer, Sebastien
  %A Jin, Wang
  %A Wang, Peng
  %A Wu, Chenglin
  %A Youcheng, Xiong
  %A Zela, Arber
  %A Zhang, Yang
  %D 2021
  %J IEEE Transactions on Pattern Analysis and Machine Intelligence
  %P 1-18
  %T Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
• 1.

  Schubert, F., Awiszus, M., and Rosenhahn, B. (2021) TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games, IEEE Transactions on Games.

  URLBibTeXEndNoteBibSonomy

  @article{SchAwi2021,
  author = {Schubert, Frederik and Awiszus, Maren and Rosenhahn, Bodo},
  journal = {IEEE Transactions on Games},
  keywords = {TOAD-GAN leibnizailab myown},
  month = {mar},
  note = {10 pages, 14 figures.},
  title = {TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games},
  year = 2021
  }

  %0 Journal Article
  %1 SchAwi2021
  %A Schubert, Frederik
  %A Awiszus, Maren
  %A Rosenhahn, Bodo
  %D 2021
  %J IEEE Transactions on Games
  %R 10.1109/TG.2021.3069833
  %T TOAD-GAN: a Flexible Framework for Few-Shot Level Generation in Token-Based Games
  %U https://ieeexplore.ieee.org/document/9390320
• 1.

  Benjak, M., Meuel, H., Laude, T., and Ostermann, J. (2021) Enhanced Machine Learning-based Inter Coding for VVC. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021).

  BibTeXEndNoteBibSonomy

  @inproceedings{BenMeu2021,
  author = {Benjak, Martin and Meuel, Holger and Laude, Thorsten and Ostermann, Jörn},
  booktitle = {2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021)},
  keywords = {Coding VVC for leibnizailab myown},
  month = {apr},
  note = {accepted for publication},
  title = {Enhanced Machine Learning-based Inter Coding for VVC},
  year = 2021
  }

  %0 Conference Paper
  %1 BenMeu2021
  %A Benjak, Martin
  %A Meuel, Holger
  %A Laude, Thorsten
  %A Ostermann, Jörn
  %B 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (ICAIIC 2021)
  %D 2021
  %T Enhanced Machine Learning-based Inter Coding for VVC
• 1.

  Kluger, F., Ackermann, H., Brachmann, E., Yang, M. Y., and Rosenhahn, B. (2021) Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images. In CVPR.

  BibTeXEndNoteBibSonomy

  @inproceedings{KluAck2021a,
  author = {Kluger, Florian and Ackermann, Hanno and Brachmann, Eric and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle = {CVPR},
  keywords = {Cuboids Revisited leibnizailab myown},
  month = {jun},
  title = {Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images},
  year = 2021
  }

  %0 Conference Paper
  %1 KluAck2021a
  %A Kluger, Florian
  %A Ackermann, Hanno
  %A Brachmann, Eric
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %B CVPR
  %D 2021
  %T Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
• 1.

  Hinrichs, R., Gajecki, T., Ostermann, J., and Nogueira, W. (2021) A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants, Journal of the Acoustic Society of America.

  BibTeXEndNoteBibSonomy

  @article{HinGaj2021,
  author = {Hinrichs, Reemt and Gajecki, Tom and Ostermann, Jörn and Nogueira, Waldo},
  journal = {Journal of the Acoustic Society of America},
  keywords = {cochlear electrical implants leibnizailab myown patterns stimulation},
  month = {mar},
  title = {A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants},
  year = 2021
  }

  %0 Journal Article
  %1 HinGaj2021
  %A Hinrichs, Reemt
  %A Gajecki, Tom
  %A Ostermann, Jörn
  %A Nogueira, Waldo
  %D 2021
  %J Journal of the Acoustic Society of America
  %T A subjective and objective evaluation of a codec for the electrical stimulation patterns of cochlear implants
• 1.

  Hutter, F., Fuks, L., Lindauer, M., and Awad, N. (2021) Method, device and computer program for producing a strategy for a robot.

  URLBibTeXEndNoteBibSonomy

  @article{HutFuk2021,
  author = {Hutter, Frank and Fuks, Lior and Lindauer, Marius and Awad, Noor},
  keywords = {a for leibnizailab myown robot strategy},
  title = {Method, device and computer program for producing a strategy for a robot},
  year = 2021
  }

  %0 Journal Article
  %1 HutFuk2021
  %A Hutter, Frank
  %A Fuks, Lior
  %A Lindauer, Marius
  %A Awad, Noor
  %D 2021
  %T Method, device and computer program for producing a strategy for a robot
  %U https://patentimages.storage.googleapis.com/8b/43/4d/a2876517a8f945/US20210008718A1.pdf
• 1.

  Liao, W., Lan, C., Yang, M. Y., Zeng, W., and Rosenhahn, B. (2021) Target-Tailored Source-Transformation for Scene Graph Generation. In In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding.

  BibTeXEndNoteBibSonomy

  @inproceedings{LiaLan2021,
  author = {Liao, Wentong and Lan, Cuiling and Yang, Michael Ying and Zeng, Wenjung and Rosenhahn, Bodo},
  booktitle = {In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding},
  keywords = {Source-Transformation Target-Tailored leibnizailab myown},
  month = {jun},
  title = {Target-Tailored Source-Transformation for Scene Graph Generation},
  year = 2021
  }

  %0 Conference Paper
  %1 LiaLan2021
  %A Liao, Wentong
  %A Lan, Cuiling
  %A Yang, Michael Ying
  %A Zeng, Wenjung
  %A Rosenhahn, Bodo
  %B In CVPR Workshop on Multi-Sensor Fusion for Dynamic Scene Understanding
  %D 2021
  %T Target-Tailored Source-Transformation for Scene Graph Generation
• 1.

  Kadra, A., Lindauer, M., Hutter, F., and Grabocka, J. (2021) Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KadLin2021a,
  author = {Kadra, Arlind and Lindauer, Marius and Hutter, Frank and Grabocka, Josif},
  booktitle = {Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)},
  keywords = {Data Nets Neural Tabular leibnizailab myown},
  month = {dec},
  title = {Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data},
  year = 2021
  }

  %0 Conference Paper
  %1 KadLin2021a
  %A Kadra, Arlind
  %A Lindauer, Marius
  %A Hutter, Frank
  %A Grabocka, Josif
  %B Proceedings of the international conference on Neural Information Processing Systems (NeurIPS)
  %D 2021
  %T Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
  %U https://arxiv.org/abs/2106.11189

2020

• 1.

  Jürgens, H., Hinrichs, R., and Ostermann, J. (2020) Recognizing Guitar Effects and Their Parameter Settings. In Proceedings of the DAFx2020 (Vol I).

  BibTeXEndNoteBibSonomy

  @inproceedings{JueHin2020,
  author = {Jürgens, Henrik and Hinrichs, Reemt and Ostermann, Jörn},
  booktitle = {Proceedings of the DAFx2020 (Vol I)},
  keywords = {Effects Guitar Parameter Settings leibnizailab myown},
  title = {Recognizing Guitar Effects and Their Parameter Settings},
  year = 2020
  }

  %0 Conference Paper
  %1 JueHin2020
  %A Jürgens, Henrik
  %A Hinrichs, Reemt
  %A Ostermann, Jörn
  %B Proceedings of the DAFx2020 (Vol I)
  %D 2020
  %T Recognizing Guitar Effects and Their Parameter Settings
• 1.

  Benjak, M., and Ostermann, J. (2020) Applications suitable for AI-based data compression, 1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424.

  BibTeXEndNoteBibSonomy

  @article{BenOst2020,
  author = {Benjak, Martin and Ostermann, Jörn},
  journal = {1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424},
  keywords = {AI-based compression data leibnizailab myown},
  title = {Applications suitable for AI-based data compression},
  year = 2020
  }

  %0 Journal Article
  %1 BenOst2020
  %A Benjak, Martin
  %A Ostermann, Jörn
  %D 2020
  %J 1st Meeting of ISO/IEC JTC 1/SC 29/WG 2 Document m55424
  %T Applications suitable for AI-based data compression
• 1.

  Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020) Method for the coding of genotype likelihood of variant m55356, ISO/IEC JTC 1/SC 29/WG 8.

  BibTeXEndNoteBibSonomy

  @article{AdhR2020b,
  author = {Adhisantoso, Yeremia Gunawan and Rohlfing, Christian and Voges, Jan and Ostermann, Jörn},
  journal = {ISO/IEC JTC 1/SC 29/WG 8},
  keywords = {Method genotype leibnizailab likelihood myown of variant},
  month = {oct},
  title = {Method for the coding of genotype likelihood of variant m55356},
  year = 2020
  }

  %0 Journal Article
  %1 AdhR2020b
  %A Adhisantoso, Yeremia Gunawan
  %A Rohlfing, Christian
  %A Voges, Jan
  %A Ostermann, Jörn
  %D 2020
  %J ISO/IEC JTC 1/SC 29/WG 8
  %T Method for the coding of genotype likelihood of variant m55356
• 1.

  Scheffner, I., Gietzelt, M., Abeling, T., Marschollek, M., and Gwinner, W. (2020) Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis, Transplantation 104, 1095–1107.

  AbstractBibTeXEndNoteBibSonomy

  Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection.

  @article{noauthororeditor,
  abstract = {Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection.},
  author = {Scheffner, Irina and Gietzelt, Matthias and Abeling, Tanja and Marschollek, Michael and Gwinner, Wilfried},
  journal = {Transplantation},
  keywords = {l3s leibnizailab},
  month = {may},
  number = 5,
  pages = {1095-1107},
  title = {Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis},
  volume = 104,
  year = 2020
  }

  %0 Journal Article
  %1 noauthororeditor
  %A Scheffner, Irina
  %A Gietzelt, Matthias
  %A Abeling, Tanja
  %A Marschollek, Michael
  %A Gwinner, Wilfried
  %D 2020
  %J Transplantation
  %N 5
  %P 1095-1107
  %R DOI: 10.1097/TP.0000000000002922
  %T Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis
  %V 104
  %X Background: Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods: Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results: Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions: The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection.
• 1.

  Wulff, A., Mast, M., Hassler, M., Montag, S., Marschollek, M., and Jack, T. (2020) Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing, Methods Inf Med 59, e64-e78.

  AbstractURLBibTeXEndNoteBibSonomy

  BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

  @article{wulff2020designing,
  abstract = {BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.},
  author = {Wulff, A. and Mast, M. and Hassler, M. and Montag, S. and Marschollek, M. and Jack, T.},
  journal = {Methods Inf Med},
  keywords = {l3s leibnizailab},
  month = {dec},
  number = {S 02},
  pages = {e64-e78},
  title = {Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing},
  volume = 59,
  year = 2020
  }

  %0 Journal Article
  %1 wulff2020designing
  %A Wulff, A.
  %A Mast, M.
  %A Hassler, M.
  %A Montag, S.
  %A Marschollek, M.
  %A Jack, T.
  %D 2020
  %J Methods Inf Med
  %N S 02
  %P e64-e78
  %R doi: 10.1055/s-0040-1716403
  %T Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing
  %U https://www.ncbi.nlm.nih.gov/pubmed/33058101
  %V 59
  %X BACKGROUND: Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly. OBJECTIVES: The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories. METHODS: We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School. RESULTS: We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall. CONCLUSION: The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.
• 1.

  Wallat, J., Singh, J., and Anand, A. (2020) BERTnesia: Investigating the capture and forgetting of knowledge in BERT.. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 174–183, Association for Computational Linguistics, Online.

  AbstractURLBibTeXEndNoteBibSonomy

  Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT's final layers. Intermediate layers contribute a significant amount (17-60\%) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.

  @inproceedings{wallat-etal-2020-bertnesia,
  abstract = {Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT's final layers. Intermediate layers contribute a significant amount (17-60\%) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.},
  address = {Online},
  author = {Wallat, Jonas and Singh, Jaspreet and Anand, Avishek},
  booktitle = {Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
  keywords = {l3s leibnizailab},
  month = {nov},
  pages = {174--183},
  publisher = {Association for Computational Linguistics},
  title = {BERTnesia: Investigating the capture and forgetting of knowledge in BERT.},
  volume = {abs/2010.09313},
  year = 2020
  }

  %0 Conference Paper
  %1 wallat-etal-2020-bertnesia
  %A Wallat, Jonas
  %A Singh, Jaspreet
  %A Anand, Avishek
  %B Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
  %C Online
  %D 2020
  %I Association for Computational Linguistics
  %P 174--183
  %R 10.18653/v1/2020.blackboxnlp-1.17
  %T BERTnesia: Investigating the capture and forgetting of knowledge in BERT.
  %U http://dblp.uni-trier.de/db/journals/corr/corr2010.html#abs-2010-09313
  %V abs/2010.09313
  %X Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT's final layers. Intermediate layers contribute a significant amount (17-60\%) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.
• 1.

  Hu, 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.

  URLBibTeXEndNoteBibSonomy

  @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 learning leibnizailab},
  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
• 1.

  Awiszus, M., Schubert, F., and Rosenhahn, B. (2020, October) TOAD-GAN: Coherent Style Level Generation from a Single Example.

  AbstractURLBibTeXEndNoteBibSonomy

  In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.

  @misc{awiszus2020toadgan,
  abstract = {In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.},
  author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  keywords = {l3s leibnizailab},
  month = {oct},
  note = {cite arxiv:2008.01531Comment: 7 pages, 7 figures. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2020},
  title = {TOAD-GAN: Coherent Style Level Generation from a Single Example},
  year = 2020
  }

  %0 Generic
  %1 awiszus2020toadgan
  %A Awiszus, Maren
  %A Schubert, Frederik
  %A Rosenhahn, Bodo
  %D 2020
  %T TOAD-GAN: Coherent Style Level Generation from a Single Example
  %U http://arxiv.org/abs/2008.01531
  %X In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels. TOAD-GAN follows the SinGAN architecture and can be trained using only one example. We demonstrate its application for Super Mario Bros. levels and are able to generate new levels of similar style in arbitrary sizes. We achieve state-of-the-art results in modeling the patterns of the training level and provide a comparison with different baselines under several metrics. Additionally, we present an extension of the method that allows the user to control the generation process of certain token structures to ensure a coherent global level layout. We provide this tool to the community to spur further research by publishing our source code.
• 1.

  Sen, H., Wentong, L., Rezazadegan Tavakoli, H., Ying Yang, M., Rosenhahn, B., and Pugeault, N. (2020) Image Captioning through Image Transformer. In .

  BibTeXEndNoteBibSonomy

  @inproceedings{HeSLia2020,
  author = {Sen, He and Wentong, Liao and Rezazadegan Tavakoli, Hamed and Ying Yang, Michael and Rosenhahn, Bodo and Pugeault, Nicolas},
  keywords = {l3s leibnizailab},
  month = {nov},
  title = {Image Captioning through Image Transformer},
  year = 2020
  }

  %0 Conference Paper
  %1 HeSLia2020
  %A Sen, He
  %A Wentong, Liao
  %A Rezazadegan Tavakoli, Hamed
  %A Ying Yang, Michael
  %A Rosenhahn, Bodo
  %A Pugeault, Nicolas
  %D 2020
  %T Image Captioning through Image Transformer
• 1.

  Rudolph, M., Wandt, B., and Rosenhahn, B. (2020, August) Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows..

  AbstractURLBibTeXEndNoteBibSonomy

  The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.

  @misc{rudolph2020differnet,
  abstract = {The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.},
  author = {Rudolph, Marco and Wandt, Bastian and Rosenhahn, Bodo},
  keywords = {l3s leibnizailab},
  month = {aug},
  note = {cite arxiv:2008.12577},
  title = {Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows.},
  year = 2020
  }

  %0 Generic
  %1 rudolph2020differnet
  %A Rudolph, Marco
  %A Wandt, Bastian
  %A Rosenhahn, Bodo
  %D 2020
  %T Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows.
  %U http://arxiv.org/abs/2008.12577
  %X The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question. To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows. Normalizing flows are well-suited to deal with low dimensional data distributions. However, they struggle with the high dimensionality of images. Therefore, we employ a multi-scale feature extractor which enables the normalizing flow to assign meaningful likelihoods to the images. Based on these likelihoods we develop a scoring function that indicates defects. Moreover, propagating the score back to the image enables pixel-wise localization. To achieve a high robustness and performance we exploit multiple transformations in training and evaluation. In contrast to most other methods, ours does not require a large number of training samples and performs well with as low as 16 images. We demonstrate the superior performance over existing approaches on the challenging and newly proposed MVTec AD and Magnetic Tile Defects datasets.
• 1.

  Fayyazifar, N., Ahderom, S., Suter, D., Maiorana, A., and Dwivedi, G. (2020) Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals. In 2020 Computing in Cardiology, pp. 1–4.

  BibTeXEndNoteBibSonomy

  @inproceedings{9344384,
  author = {Fayyazifar, N. and Ahderom, S. and Suter, D. and Maiorana, A. and Dwivedi, G.},
  booktitle = {2020 Computing in Cardiology},
  keywords = {leibnizailab myown},
  pages = {1-4},
  title = {Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals},
  year = 2020
  }

  %0 Conference Paper
  %1 9344384
  %A Fayyazifar, N.
  %A Ahderom, S.
  %A Suter, D.
  %A Maiorana, A.
  %A Dwivedi, G.
  %B 2020 Computing in Cardiology
  %D 2020
  %P 1-4
  %R 10.22489/CinC.2020.161
  %T Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals
• 1.

  Eimer, T., Biedenkapp, A., Hutter, F., and Lindauer, M. (2020) Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).

  BibTeXEndNoteBibSonomy

  @inproceedings{EimBie2020a,
  author = {Eimer, Theresa and Biedenkapp, Andre and Hutter, Frank and Lindauer, Marius},
  booktitle = {Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)},
  keywords = {Context Contextual Evaluations Learning Reinforcement Self-Paced leibnizailab myown},
  month = {jul},
  title = {Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning},
  year = 2020
  }

  %0 Conference Paper
  %1 EimBie2020a
  %A Eimer, Theresa
  %A Biedenkapp, Andre
  %A Hutter, Frank
  %A Lindauer, Marius
  %B Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)
  %D 2020
  %T Towards Self-Paced Context Evaluations for Contextual Reinforcement Learning
• 1.

  Krause, L., Koc, J., Rosenhahn, B., and Rosenhahn, A. (2020) Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure, Environmental Science and Technology 54, 10022–10030.

  URLBibTeXEndNoteBibSonomy

  @article{KraKoc2020a,
  author = {Krause, Lutz and Koc, Julian and Rosenhahn, Bodo and Rosenhahn, Axel},
  journal = {Environmental Science and Technology},
  keywords = {Convolutional Exposure Field Network Neural Short-Term leibnizailab myown},
  number = 16,
  pages = {10022-10030},
  title = {Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure},
  volume = 54,
  year = 2020
  }

  %0 Journal Article
  %1 KraKoc2020a
  %A Krause, Lutz
  %A Koc, Julian
  %A Rosenhahn, Bodo
  %A Rosenhahn, Axel
  %D 2020
  %J Environmental Science and Technology
  %N 16
  %P 10022-10030
  %R 10.1021/acs.est.0c01982
  %T Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure
  %U https://pubs.acs.org/doi/10.1021/acs.est.0c01982
  %V 54
• 1.

  Tan, 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.

  BibTeXEndNoteBibSonomy

  @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 myown},
  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
• 1.

  Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Carola, D., Lindauer, M., and Hutter, F. (2020) Squirrel: A Switching Hyperparameter Optimizer, arxiv.

  URLBibTeXEndNoteBibSonomy

  @article{AwaGre2020a,
  author = {Awad, Noor and Shala, Gresa and Deng, Difan and Mallik, Neeratyoy and Feurer, Matthias and Eggensperger, Katharina and Biedenkapp, André and Vermetten, Diederick and Wang, Hao and Carola, Doerr and Lindauer, Marius and Hutter, Frank},
  journal = {arxiv},
  keywords = {Hyperparameter Optimizer leibnizailab myown},
  month = {dec},
  title = {Squirrel: A Switching Hyperparameter Optimizer},
  year = 2020
  }

  %0 Journal Article
  %1 AwaGre2020a
  %A Awad, Noor
  %A Shala, Gresa
  %A Deng, Difan
  %A Mallik, Neeratyoy
  %A Feurer, Matthias
  %A Eggensperger, Katharina
  %A Biedenkapp, André
  %A Vermetten, Diederick
  %A Wang, Hao
  %A Carola, Doerr
  %A Lindauer, Marius
  %A Hutter, Frank
  %D 2020
  %J arxiv
  %T Squirrel: A Switching Hyperparameter Optimizer
  %U https://arxiv.org/abs/2012.08180
• 1.

  Denkena, B., Dittrich, M., Lindauer, M., Mainka, and Stürenburg, L. (2020) Using AutoML to Optimize Shape Error Prediction in Milling Processes. In Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{DenDit2020a,
  author = {Denkena, Berend and Dittrich, Marc and Lindauer, Marius and Mainka and Stürenburg, Lukas},
  booktitle = {Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC)},
  keywords = {AutoML Error Prediction Shape leibnizailab myown},
  month = {dec},
  title = {Using AutoML to Optimize Shape Error Prediction in Milling Processes},
  year = 2020
  }

  %0 Conference Paper
  %1 DenDit2020a
  %A Denkena, Berend
  %A Dittrich, Marc
  %A Lindauer, Marius
  %A Mainka,
  %A Stürenburg, Lukas
  %B Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC)
  %D 2020
  %T Using AutoML to Optimize Shape Error Prediction in Milling Processes
  %U https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3724234
• 1.

  Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Jacques, J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Wang, J., Wang, P., Wu, C., Xiong, Y., Zela, A., and Zhang, Y. (2020) Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019. In HAL.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{LiuPav2020,
  author = {Liu, Zhengying and Pavao, Adrien and Xu, Zhen and Escalera, Sergio and Ferreira, Fabio and Guyon, Isabelle and Hong, Sirui and Hutter, Frank and Ji, Rongrong and Jacques, Julio and Li, Ge and Lindauer, Marius and Luo, Zhipeng and Madadi, Meysam and Nierhoff, Thomas and Niu, Kangning and Pan, Chunguang and Stoll, Danny and Treguer, Sebastien and Wang, Jin and Wang, Peng and Wu, Chenglin and Xiong, Youcheng and Zela, Arbër and Zhang, Yang},
  booktitle = {HAL},
  keywords = {AutoDL ChaLearn Winning challenge leibnizailab myown solutions},
  month = {sep},
  title = {Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019},
  year = 2020
  }

  %0 Conference Paper
  %1 LiuPav2020
  %A Liu, Zhengying
  %A Pavao, Adrien
  %A Xu, Zhen
  %A Escalera, Sergio
  %A Ferreira, Fabio
  %A Guyon, Isabelle
  %A Hong, Sirui
  %A Hutter, Frank
  %A Ji, Rongrong
  %A Jacques, Julio
  %A Li, Ge
  %A Lindauer, Marius
  %A Luo, Zhipeng
  %A Madadi, Meysam
  %A Nierhoff, Thomas
  %A Niu, Kangning
  %A Pan, Chunguang
  %A Stoll, Danny
  %A Treguer, Sebastien
  %A Wang, Jin
  %A Wang, Peng
  %A Wu, Chenglin
  %A Xiong, Youcheng
  %A Zela, Arbër
  %A Zhang, Yang
  %B HAL
  %D 2020
  %T Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
  %U https://hal.archives-ouvertes.fr/hal-02957135
• 1.

  Eggensperger, K., Haase, K., Müller, P., Lindauer, M., and Hutter, F. (2020) Neural Model-based Optimization with Right-Censored Observations. In CoRR.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{EggHaa2020,
  author = {Eggensperger, Katharina and Haase, Kai and Müller, Philipp and Lindauer, Marius and Hutter, Frank},
  booktitle = {CoRR},
  keywords = {Observations Optimization Right-Censored leibnizailab myown},
  month = {sep},
  title = {Neural Model-based Optimization with Right-Censored Observations},
  year = 2020
  }

  %0 Conference Paper
  %1 EggHaa2020
  %A Eggensperger, Katharina
  %A Haase, Kai
  %A Müller, Philipp
  %A Lindauer, Marius
  %A Hutter, Frank
  %B CoRR
  %D 2020
  %T Neural Model-based Optimization with Right-Censored Observations
  %U https://arxiv.org/abs/2009.13828
• 1.

  Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., and Hutter, F. (2020) Learning Step-Size Adaptation in CMA-ES. In Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN’20).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ShaBie2020,
  author = {Shala, Gresa and Biedenkapp, Andre and Awad, Noor and Adriaensen, Steven and Lindauer, Marius and Hutter, Frank},
  booktitle = {Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20)},
  keywords = {CMA-ES leibnizailab myown},
  month = {sep},
  title = {Learning Step-Size Adaptation in CMA-ES},
  year = 2020
  }

  %0 Conference Paper
  %1 ShaBie2020
  %A Shala, Gresa
  %A Biedenkapp, Andre
  %A Awad, Noor
  %A Adriaensen, Steven
  %A Lindauer, Marius
  %A Hutter, Frank
  %B Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20)
  %D 2020
  %T Learning Step-Size Adaptation in CMA-ES
  %U https://arxiv.org/abs/0805.0231
• 1.

  Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., and Hutter, F. (2020) Auto-Sklearn 2.0: The Next Generation. In arXiv:2007.04074 [cs.LG].

  URLBibTeXEndNoteBibSonomy

  @inproceedings{FeuEgg2020,
  author = {Feurer, Matthias and Eggensperger, Katharina and Falkner, Stefan and Lindauer, Marius and Hutter, Frank},
  booktitle = {arXiv:2007.04074 [cs.LG]},
  keywords = {2.0 Auto-Sklearn leibnizailab myown},
  month = {jul},
  title = {Auto-Sklearn 2.0: The Next Generation},
  year = 2020
  }

  %0 Conference Paper
  %1 FeuEgg2020
  %A Feurer, Matthias
  %A Eggensperger, Katharina
  %A Falkner, Stefan
  %A Lindauer, Marius
  %A Hutter, Frank
  %B arXiv:2007.04074 [cs.LG]
  %D 2020
  %T Auto-Sklearn 2.0: The Next Generation
  %U https://arxiv.org/abs/2007.04074
• 1.

  Zimmer, L., Lindauer, M., and Hutter, F. (2020) Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. In arxiv:2006.13799[cs.LG].

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ZimLin2020,
  author = {Zimmer, Lucas and Lindauer, Marius and Hutter, Frank},
  booktitle = {arxiv:2006.13799[cs.LG]},
  keywords = {Auto-PyTorch Tabular leibnizailab myown},
  month = {jun},
  title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
  year = 2020
  }

  %0 Conference Paper
  %1 ZimLin2020
  %A Zimmer, Lucas
  %A Lindauer, Marius
  %A Hutter, Frank
  %B arxiv:2006.13799[cs.LG]
  %D 2020
  %T Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
  %U https://arxiv.org/abs/2006.13799
• 1.

  Souza, A., Nardi, L., Oliveira, L., Olukotun, K., Lindauer, M., and Hutter, F. (2020) Prior-guided Bayesian Optimization. In arxiv:2006.14608[cs.LG].

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SouNar2020,
  author = {Souza, Artur and Nardi, Luigi and Oliveira, Leonardo and Olukotun, Kunle and Lindauer, Marius and Hutter, Frank},
  booktitle = {arxiv:2006.14608[cs.LG]},
  keywords = {Bayesian Optimization leibnizailab myown},
  month = {jun},
  title = {Prior-guided Bayesian Optimization},
  year = 2020
  }

  %0 Conference Paper
  %1 SouNar2020
  %A Souza, Artur
  %A Nardi, Luigi
  %A Oliveira, Leonardo
  %A Olukotun, Kunle
  %A Lindauer, Marius
  %A Hutter, Frank
  %B arxiv:2006.14608[cs.LG]
  %D 2020
  %T Prior-guided Bayesian Optimization
  %U https://arxiv.org/abs/2006.14608
• 1.

  Biedenkapp, A., Rajan, R., Hutter, F., and Lindauer, M. (2020) Towards TempoRL: Learning When to Act. In Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML’20).

  BibTeXEndNoteBibSonomy

  @inproceedings{BieRaj2020,
  author = {Biedenkapp, Andre and Rajan, Raghu and Hutter, Frank and Lindauer, Marius},
  booktitle = {Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)},
  keywords = {TempoRL Towards leibnizailab myown},
  month = {jul},
  title = {Towards TempoRL: Learning When to Act},
  year = 2020
  }

  %0 Conference Paper
  %1 BieRaj2020
  %A Biedenkapp, Andre
  %A Rajan, Raghu
  %A Hutter, Frank
  %A Lindauer, Marius
  %B Workshop on Inductive Biases, Invariances and Generalization in Reinforcement Learning (BIG@ICML'20)
  %D 2020
  %T Towards TempoRL: Learning When to Act
• 1.

  Adhisantoso, Y. G., Rohlfing, C., Voges, J., and Ostermann, J. (2020) Extension to method for the coding of genomic variants m55355, ISO/IEC JTC 1/SC 29/WG 8.

  BibTeXEndNoteBibSonomy

  @article{AdhR2020,
  author = {Adhisantoso, Yeremia Gunawan and Rohlfing, Christian and Voges, Jan and Ostermann, Jörn},
  journal = {ISO/IEC JTC 1/SC 29/WG 8},
  keywords = {Extension genomic leibnizailab myown variants},
  month = {oct},
  title = {Extension to method for the coding of genomic variants m55355},
  year = 2020
  }

  %0 Journal Article
  %1 AdhR2020
  %A Adhisantoso, Yeremia Gunawan
  %A Rohlfing, Christian
  %A Voges, Jan
  %A Ostermann, Jörn
  %D 2020
  %J ISO/IEC JTC 1/SC 29/WG 8
  %T Extension to method for the coding of genomic variants m55355
• 1.

  Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., and Lindauer, M. (2020) Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{SpeBie2020,
  author = {Speck, David and Biedenkapp, André and Hutter, Frank and Mattmüller, Robert and Lindauer, Marius},
  booktitle = {Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS},
  keywords = {Heuristic Selection leibnizailab myown},
  month = {jun},
  title = {Learning Heuristic Selection with Dynamic Algorithm Configuration},
  year = 2020
  }

  %0 Conference Paper
  %1 SpeBie2020
  %A Speck, David
  %A Biedenkapp, André
  %A Hutter, Frank
  %A Mattmüller, Robert
  %A Lindauer, Marius
  %B Proceedings of international workshop on Bridging the Gap Between AI Planning and Reinforcement Learning at ICAPS
  %D 2020
  %T Learning Heuristic Selection with Dynamic Algorithm Configuration
  %U https://arxiv.org/abs/2006.08246
• 1.

  Cheng, H., Liao, W., Ying, Y. M., Sester, M., and Rosenhahn, B. (2020) MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic. In 23rd International Conference on Intelligent Transportation Systems (ITSC).

  BibTeXEndNoteBibSonomy

  @inproceedings{CheLia2020a,
  author = {Cheng, Hao and Liao, Wentong and Ying, Yang Michael and Sester, Monica and Rosenhahn, Bodo},
  booktitle = {23rd International Conference on Intelligent Transportation Systems (ITSC)},
  keywords = {Encoder Mixed Multi-Context Network Traffic leibnizailab myown},
  month = {sep},
  note = {In cooperation with Institut für Kartographie und Geoinformatik, Uni Hannover, Germany},
  title = {MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic},
  year = 2020
  }

  %0 Conference Paper
  %1 CheLia2020a
  %A Cheng, Hao
  %A Liao, Wentong
  %A Ying, Yang Michael
  %A Sester, Monica
  %A Rosenhahn, Bodo
  %B 23rd International Conference on Intelligent Transportation Systems (ITSC)
  %D 2020
  %T MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic
• 1.

  Zell, P., Rosenhahn, B., and Wandt, B. (2020) Weakly-supervised Learning of Human Dynamics. In European Conference on Computer Vision (ECCV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ZelRos2020a,
  author = {Zell, Petrissa and Rosenhahn, Bodo and Wandt, Bastian},
  booktitle = {European Conference on Computer Vision (ECCV)},
  keywords = {Dynamics Human leibnizailab myown},
  month = {aug},
  title = {Weakly-supervised Learning of Human Dynamics},
  year = 2020
  }

  %0 Conference Paper
  %1 ZelRos2020a
  %A Zell, Petrissa
  %A Rosenhahn, Bodo
  %A Wandt, Bastian
  %B European Conference on Computer Vision (ECCV)
  %D 2020
  %T Weakly-supervised Learning of Human Dynamics
  %U https://arxiv.org/abs/2007.08969
• 1.

  Ostermann, J., and Hinrichs, R. (2020) Links und rechts verbinden, Unimagazin.

  URLBibTeXEndNoteBibSonomy

  @article{OstHin2020a,
  author = {Ostermann, Jörn and Hinrichs, Reemt},
  journal = {Unimagazin},
  keywords = {Links leibnizailab myown rechts und verbinden},
  month = {jun},
  number = 1,
  title = {Links und rechts verbinden},
  year = 2020
  }

  %0 Journal Article
  %1 OstHin2020a
  %A Ostermann, Jörn
  %A Hinrichs, Reemt
  %D 2020
  %J Unimagazin
  %N 1
  %T Links und rechts verbinden
  %U https://anyflip.com/cjox/dool/
• 1.

  Südbeck, S., Krause, T., and Ostermann, J. (2020) Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario. In IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing.

  BibTeXEndNoteBibSonomy

  @inproceedings{SueKra2020,
  author = {Südbeck, Sönke and Krause, Thomas and Ostermann, Jörn},
  booktitle = {IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing},
  keywords = {Localization Non-Line-of-Sight Time-Difference-of-Arrival leibnizailab myown},
  month = {sep},
  title = {Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario},
  year = 2020
  }

  %0 Conference Paper
  %1 SueKra2020
  %A Südbeck, Sönke
  %A Krause, Thomas
  %A Ostermann, Jörn
  %B IEEE MMSP 2020 - IEEE International Workshop on Multimedia Signal Processing
  %D 2020
  %T Non-Line-of-Sight Time-Difference-of-Arrival Localization with Explicit Inclusion of Geometry Information in a Simple Diffraction Scenario
• 1.

  Ackermann, H., Meuel, H., Rosenhahn, B., and Ostermann, J. (2020) Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes 1–12.

  URLBibTeXEndNoteBibSonomy

  @article{AckMeu2020,
  author = {Ackermann, Hanno and Meuel, Holger and Rosenhahn, Bodo and Ostermann, Jörn},
  keywords = {Digitalbild Verfahren leibnizailab myown},
  month = {aug},
  pages = {1-12},
  title = {Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes},
  year = 2020
  }

  %0 Journal Article
  %1 AckMeu2020
  %A Ackermann, Hanno
  %A Meuel, Holger
  %A Rosenhahn, Bodo
  %A Ostermann, Jörn
  %D 2020
  %P 1-12
  %T Verfahren und Vorrichtung zum Aufnehmen eines Digitalbildes
  %U https://register.dpma.de/DPMAregister/pat/register?AKZ=1020171297707&CURSOR=0
• 1.

  Hu, T., Iosifidis, V., Wentong, L., Hang, Z., Yang, M. Y., Ntoutsi, E., and Rosenhahn, B. (2020) FairNN - Conjoint Learning of Fair Representations for Fair Decisions. In 23rd International Conference on Discovery Science.

  BibTeXEndNoteBibSonomy

  @inproceedings{HuIos2020,
  author = {Hu, Tongxin and Iosifidis, Vasileios and Wentong, Liao and Hang, Zhang and Yang, Michael Ying and Ntoutsi, Eirini and Rosenhahn, Bodo},
  booktitle = {23rd International Conference on Discovery Science},
  keywords = {Decisions Fair Representations for leibnizailab myown},
  month = {oct},
  note = {Code available: https://github.com/wtliao/FairNN},
  title = {FairNN - Conjoint Learning of Fair Representations for Fair Decisions},
  year = 2020
  }

  %0 Conference Paper
  %1 HuIos2020
  %A Hu, Tongxin
  %A Iosifidis, Vasileios
  %A Wentong, Liao
  %A Hang, Zhang
  %A Yang, Michael Ying
  %A Ntoutsi, Eirini
  %A Rosenhahn, Bodo
  %B 23rd International Conference on Discovery Science
  %D 2020
  %T FairNN - Conjoint Learning of Fair Representations for Fair Decisions
• 1.

  Sen, H., Wentong, L., Tavakoli, H. R., Yang, M. Y., Rosenhahn, B., and Pugeault, N. (2020) Image Captioning through Image Transformer. In Asian Conference on Computer Vision (ACCV).

  BibTeXEndNoteBibSonomy

  @inproceedings{HeSLia2020,
  author = {Sen, He and Wentong, Liao and Tavakoli, Hamed Rezazadegan and Yang, Michael Ying and Rosenhahn, Bodo and Pugeault, Nicolas},
  booktitle = {Asian Conference on Computer Vision (ACCV)},
  keywords = {Captioning Image leibnizailab myown},
  month = {nov},
  title = {Image Captioning through Image Transformer},
  year = 2020
  }

  %0 Conference Paper
  %1 HeSLia2020
  %A Sen, He
  %A Wentong, Liao
  %A Tavakoli, Hamed Rezazadegan
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %A Pugeault, Nicolas
  %B Asian Conference on Computer Vision (ACCV)
  %D 2020
  %T Image Captioning through Image Transformer
• 1.

  Gritzner, D., and Ostermann, J. (2020) USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 483–492.

  URLBibTeXEndNoteBibSonomy

  @article{GriOst2020,
  author = {Gritzner, Daniel and Ostermann, Jörn},
  journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
  keywords = {Aerial Images Paired Semantically leibnizailab myown},
  month = {aug},
  pages = {483--492},
  title = {USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES},
  year = 2020
  }

  %0 Journal Article
  %1 GriOst2020
  %A Gritzner, Daniel
  %A Ostermann, Jörn
  %D 2020
  %J ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
  %P 483--492
  %R 10.5194/isprs-annals-V-2-2020-483-2020
  %T USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES
  %U https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/483/2020/
• 1.

  Liao, W., Cheng, X., Yang, J., Roth, S., Goesele, M., Yang, M. Y., and Rosenhahn, B. (2020) LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery. In XXIV ISPRS Congress, p. 8.

  BibTeXEndNoteBibSonomy

  @inproceedings{LiaChe2020,
  author = {Liao, Wentong and Cheng, Xiang and Yang, Jingfeng and Roth, Stefan and Goesele, Michael and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle = {XXIV ISPRS Congress},
  keywords = {LR-CNN leibnizailab myown},
  month = {sep},
  note = {oral},
  number = 381,
  pages = 8,
  title = {LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery},
  year = 2020
  }

  %0 Conference Paper
  %1 LiaChe2020
  %A Liao, Wentong
  %A Cheng, Xiang
  %A Yang, Jingfeng
  %A Roth, Stefan
  %A Goesele, Michael
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %B XXIV ISPRS Congress
  %D 2020
  %N 381
  %P 8
  %T LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
• 1.

  Samayoa, Y., and Ostermann, J. (2020) Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems. In 2020 Wireless Telecommunications Symposium (WTS), p. 5.

  BibTeXEndNoteBibSonomy

  @inproceedings{SamOst2020a,
  author = {Samayoa, Yasser and Ostermann, Jörn},
  booktitle = {2020 Wireless Telecommunications Symposium (WTS)},
  keywords = {OFDM PAPR Reduction Systems leibnizailab myown},
  month = {apr},
  pages = 5,
  title = {Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems},
  year = 2020
  }

  %0 Conference Paper
  %1 SamOst2020a
  %A Samayoa, Yasser
  %A Ostermann, Jörn
  %B 2020 Wireless Telecommunications Symposium (WTS)
  %D 2020
  %P 5
  %T Modified Active Constellation Extension Algorithm for PAPR Reduction in OFDM Systems
• 1.

  Awiszus, M., Schubert, F., and Rosenhahn, B. (2020) TOAD-GAN: Coherent Style Level Generation from a Single Example. In AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{AwiSch2020,
  author = {Awiszus, Maren and Schubert, Frederik and Rosenhahn, Bodo},
  booktitle = {AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award},
  keywords = {TOAD-GAN leibnizailab myown},
  month = {oct},
  note = {7 pages, 7 figures.},
  title = {TOAD-GAN: Coherent Style Level Generation from a Single Example},
  year = 2020
  }

  %0 Conference Paper
  %1 AwiSch2020
  %A Awiszus, Maren
  %A Schubert, Frederik
  %A Rosenhahn, Bodo
  %B AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Best Student Paper Award
  %D 2020
  %T TOAD-GAN: Coherent Style Level Generation from a Single Example
  %U /brokenurl#AAAI, github, arxiv, Award
• 1.

  Cong, Y., Ackermann, H., Liao, W., Yang, M. Y., and Rosenhahn, B. (2020) NODIS: Neural Ordinary Differential Scene Understanding. In European Conference on Computer Vision (ECCV).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{ConAck2020,
  author = {Cong, Yuren and Ackermann, Hanno and Liao, Wentong and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle = {European Conference on Computer Vision (ECCV)},
  keywords = {NODIS leibnizailab myown},
  month = {aug},
  title = {NODIS: Neural Ordinary Differential Scene Understanding},
  year = 2020
  }

  %0 Conference Paper
  %1 ConAck2020
  %A Cong, Yuren
  %A Ackermann, Hanno
  %A Liao, Wentong
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %B European Conference on Computer Vision (ECCV)
  %D 2020
  %T NODIS: Neural Ordinary Differential Scene Understanding
  %U https://arxiv.org/abs/2001.04735v2
• 1.

  Gra"shof, S., Ackermann, H., Brandt, S., and Ostermann, J. (2020) Multilinear Modelling of Faces and Expressions, Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

  URLBibTeXEndNoteBibSonomy

  @article{GraAck2020,
  author = {Gra"shof, Stella and Ackermann, Hanno and Brandt, Sami and Ostermann, Jörn},
  journal = {Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  keywords = {Faces Modelling Multilinear leibnizailab myown},
  month = {apr},
  note = {early access},
  title = {Multilinear Modelling of Faces and Expressions},
  year = 2020
  }

  %0 Journal Article
  %1 GraAck2020
  %A Gra"shof, Stella
  %A Ackermann, Hanno
  %A Brandt, Sami
  %A Ostermann, Jörn
  %D 2020
  %J Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  %T Multilinear Modelling of Faces and Expressions
  %U https://ieeexplore.ieee.org/document/9067086
• 1.

  Minh, C. N. D., Gilani, S. Z., Islam, S., and Suter, D. (2020) Learning Affordance Segmentation: An Investigative Study. In DICTA2020.

  BibTeXEndNoteBibSonomy

  @inproceedings{chauDICTA2020,
  author = {Minh, Chau Nguyen Duc and Gilani, Syed Zulqarnain and Islam, Syed and Suter, David},
  booktitle = {DICTA2020},
  keywords = {leibnizailab myown},
  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
• 1.

  Hornakova*, A., Henschel*, R., Rosenhahn, B., Swoboda, P., and equal contribution), (*. (2020) Lifted Disjoint Paths with Application in Multiple Object Tracking, Proceedings of the 37th International Conference on Machine Learning (ICML).

  URLBibTeXEndNoteBibSonomy

  @article{HorHen2020,
  author = {Hornakova*, Andrea and Henschel*, Roberto and Rosenhahn, Bodo and Swoboda, Paul and equal contribution), (*},
  journal = {Proceedings of the 37th International Conference on Machine Learning (ICML)},
  keywords = {Disjoint Multiple Object Paths Tracking leibnizailab myown},
  month = {jul},
  title = {Lifted Disjoint Paths with Application in Multiple Object Tracking},
  year = 2020
  }

  %0 Journal Article
  %1 HorHen2020
  %A Hornakova*, Andrea
  %A Henschel*, Roberto
  %A Rosenhahn, Bodo
  %A Swoboda, Paul
  %A equal contribution), (*
  %D 2020
  %J Proceedings of the 37th International Conference on Machine Learning (ICML)
  %T Lifted Disjoint Paths with Application in Multiple Object Tracking
  %U /brokenurl# Visual Results, Code, Video Presentation
• 1.

  Meuel, H., and Ostermann, J. (2020) Analysis of Affine Motion-Compensated Prediction in Video Coding, IEEE Transactions on Image Processing 29, 7359–7374.

  URLBibTeXEndNoteBibSonomy

  @article{MeuOst2020,
  author = {Meuel, Holger and Ostermann, Jörn},
  journal = {IEEE Transactions on Image Processing},
  keywords = {Coding Motion-Compensated Prediction Video leibnizailab myown},
  month = {jun},
  note = {Orchid iD: 0000-0002-1275-303X},
  pages = {7359-7374},
  title = {Analysis of Affine Motion-Compensated Prediction in Video Coding},
  volume = 29,
  year = 2020
  }

  %0 Journal Article
  %1 MeuOst2020
  %A Meuel, Holger
  %A Ostermann, Jörn
  %D 2020
  %J IEEE Transactions on Image Processing
  %P 7359-7374
  %R 10.1109/TIP.2020.3001734
  %T Analysis of Affine Motion-Compensated Prediction in Video Coding
  %U https://ieeexplore.ieee.org/document/9119829
  %V 29
• 1.

  Pestel-Schiller, U., and Ostermann, J. (2020) 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.

  BibTeXEndNoteBibSonomy

  @inproceedings{PesOst2020,
  author = {Pestel-Schiller, Ulrike and Ostermann, Jörn},
  booktitle = {13th European Conference on Synthetic Aperture Radar},
  keywords = {HEVC Images JPEG SAR leibnizailab myown},
  month = {jun},
  title = {Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability},
  year = 2020
  }

  %0 Conference Paper
  %1 PesOst2020
  %A Pestel-Schiller, Ulrike
  %A Ostermann, Jörn
  %B 13th European Conference on Synthetic Aperture Radar
  %D 2020
  %T Interpreter-Based Evaluation of Compressed SAR Images Using JPEG and HEVC Intra Coding: Compression Can Improve Usability
• 1.

  Samayoa, Y., and Ostermann, J. (2020) Parameter Selection for a Video Communication System based on HEVC and Channel Coding. In IEEE Latin-American Conference on Communications (LATINCOM 2020), p. 5.

  BibTeXEndNoteBibSonomy

  @inproceedings{SamOst2020,
  author = {Samayoa, Yasser and Ostermann, Jörn},
  booktitle = {IEEE Latin-American Conference on Communications (LATINCOM 2020)},
  keywords = {Communication HEVC System Video leibnizailab myown},
  month = {nov},
  pages = 5,
  title = {Parameter Selection for a Video Communication System based on HEVC and Channel Coding},
  year = 2020
  }

  %0 Conference Paper
  %1 SamOst2020
  %A Samayoa, Yasser
  %A Ostermann, Jörn
  %B IEEE Latin-American Conference on Communications (LATINCOM 2020)
  %D 2020
  %P 5
  %T Parameter Selection for a Video Communication System based on HEVC and Channel Coding
• 1.

  Kuhnke, F., Rumberg, L., and Ostermann, J. (2020) Two-Stream Aural-Visual Affect Analysis in the Wild. In 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 366–371.

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KuhRum2020,
  author = {Kuhnke, Felix and Rumberg, Lars and Ostermann, Jörn},
  booktitle = {15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)},
  keywords = {Affect Analysis Aural-Visual leibnizailab myown},
  month = {may},
  pages = {366-371},
  title = {Two-Stream Aural-Visual Affect Analysis in the Wild},
  year = 2020
  }

  %0 Conference Paper
  %1 KuhRum2020
  %A Kuhnke, Felix
  %A Rumberg, Lars
  %A Ostermann, Jörn
  %B 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
  %D 2020
  %P 366-371
  %R 10.1109/FG47880.2020.00056
  %T Two-Stream Aural-Visual Affect Analysis in the Wild
  %U /brokenurl#code
• 1.

  Kluger, F., Brachmann, E., Ackermann, H., Rother, C., Yang, M. Y., and Rosenhahn, B. (2020) CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. In Computer Vision and Pattern Recognition (CVPR).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KluBra2020,
  author = {Kluger, Florian and Brachmann, Eric and Ackermann, Hanno and Rother, Carsten and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  keywords = {CONSAC leibnizailab myown},
  month = {jun},
  title = {CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus},
  year = 2020
  }

  %0 Conference Paper
  %1 KluBra2020
  %A Kluger, Florian
  %A Brachmann, Eric
  %A Ackermann, Hanno
  %A Rother, Carsten
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %B Computer Vision and Pattern Recognition (CVPR)
  %D 2020
  %T CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
  %U https://arxiv.org/pdf/2001.02643.pdf
• 1.

  Kluger, F., Ackermann, H., Yang, M. Y., and Rosenhahn, B. (2020) Temporally Consistent Horizon Lines. In International Conference on Robotics and Automation (ICRA).

  URLBibTeXEndNoteBibSonomy

  @inproceedings{KluAck2020,
  author = {Kluger, Florian and Ackermann, Hanno and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle = {International Conference on Robotics and Automation (ICRA)},
  keywords = {Horizon Lines leibnizailab myown},
  month = {jun},
  note = {Accepted for publication},
  title = {Temporally Consistent Horizon Lines},
  year = 2020
  }

  %0 Conference Paper
  %1 KluAck2020
  %A Kluger, Florian
  %A Ackermann, Hanno
  %A Yang, Michael Ying
  %A Rosenhahn, Bodo
  %B International Conference on Robotics and Automation (ICRA)
  %D 2020
  %T Temporally Consistent Horizon Lines
  %U https://arxiv.org/pdf/1907.10014.pdf
• 1.

  Krause, T., and Ostermann, J. (2020) Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound, Structural Control and Health Monitoring.

  URLBibTeXEndNoteBibSonomy

  @article{KraOst2020a,
  author = {Krause, Thomas and Ostermann, Jörn},
  journal = {Structural Control and Health Monitoring},
  keywords = {Airborne Detection Sound Turbine Wind leibnizailab myown},
  month = {feb},
  title = {Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound},
  year = 2020
  }

  %0 Journal Article
  %1 KraOst2020a
  %A Krause, Thomas
  %A Ostermann, Jörn
  %D 2020
  %J Structural Control and Health Monitoring
  %R doi:10.1002/stc.2520
  %T Damage Detection for Wind Turbine Rotor Blades Using Airborne Sound
  %U https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2520
• 1.

  Hartmann, F., Sommer, A., Pestel-Schiller, U., and Osterman, J. (2020) A scheme for stabilizing the image generation for VideoSAR. In 13th European Conference on Synthetic Aperture Radar.

  BibTeXEndNoteBibSonomy

  @inproceedings{HarSom2020a,
  author = {Hartmann, Fabian and Sommer, Aron and Pestel-Schiller, Ulrike and Osterman, Jörn},
  booktitle = {13th European Conference on Synthetic Aperture Radar},
  keywords = {VideoSAR leibnizailab myown},
  month = {jun},
  title = {A scheme for stabilizing the image generation for VideoSAR},
  year = 2020
  }

  %0 Conference Paper
  %1 HarSom2020a
  %A Hartmann, Fabian
  %A Sommer, Aron
  %A Pestel-Schiller, Ulrike
  %A Osterman, Jörn
  %B 13th European Conference on Synthetic Aperture Radar
  %D 2020
  %T A scheme for stabilizing the image generation for VideoSAR
• 1.

  Henschel, R., von Marcard, T., and Rosenhahn, B. (2020) Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs, IEEE Transactions on Image Processing.

  URLBibTeXEndNoteBibSonomy

  @article{Henvon2020a,
  author = {Henschel, Roberto and von Marcard, Timo and Rosenhahn, Bodo},
  journal = {IEEE Transactions on Image Processing},
  keywords = {Long-Term Multiple People Tracking leibnizailab myown},
  title = {Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs},
  year = 2020
  }

  %0 Journal Article
  %1 Henvon2020a
  %A Henschel, Roberto
  %A von Marcard, Timo
  %A Rosenhahn, Bodo
  %D 2020
  %J IEEE Transactions on Image Processing
  %T Accurate Long-Term Multiple People Tracking using Video and Body-Worn IMUs
  %U https://ieeexplore.ieee.org/abstract/document/9166762
• 1.

  Voges, J., Paridaens, T., Müntefering, F., Mainzer, L. S., Bliss, B., Yang, M., Ochoa, I., Fostier, J., Ostermann, J., and Hernaez, M. (2020) GABAC: an arithmetic coding solution for genomic data, Bioinformatics 36, 2275–2277.

  URLBibTeXEndNoteBibSonomy

  @article{VogPar2020a,
  author = {Voges, Jan and Paridaens, Tom and Müntefering, Fabian and Mainzer, Liudmila S. and Bliss, Brian and Yang, Mingyu and Ochoa, Idoia and Fostier, Jan and Ostermann, Jörn and Hernaez, Mikel},
  journal = {Bioinformatics},
  keywords = {arithmetic coding data genomic leibnizailab myown},
  number = 7,
  pages = {2275-2277},
  title = {GABAC: an arithmetic coding solution for genomic data},
  volume = 36,
  year = 2020
  }

  %0 Journal Article
  %1 VogPar2020a
  %A Voges, Jan
  %A Paridaens, Tom
  %A Müntefering, Fabian
  %A Mainzer, Liudmila S.
  %A Bliss, Brian
  %A Yang, Mingyu
  %A Ochoa, Idoia
  %A Fostier, Jan
  %A Ostermann, Jörn
  %A Hernaez, Mikel
  %D 2020
  %J Bioinformatics
  %N 7
  %P 2275-2277
  %R 10.1093/bioinformatics/btz922
  %T GABAC: an arithmetic coding solution for genomic data
  %U https://doi.org/10.1093/bioinformatics/btz922
  %V 36