Publications

Here you can find all publications in which at least one member of the working group was involved. You can directly access the .bib file as well as a link to access the article.

  1. Thomas J, Hepp T, Mayr A, Bischl B (2018) Probing for sparse and fast variable selection with model-based boosting.
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  2. Thomas J, Mayr A, Bischl B, Schmid M, Smith A, Hofner B (2018) Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Statistics and Computing 28, 673–687.
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  3. Kühn D, Probst P, Thomas J, Bischl B (2018) Automatic Exploration of Machine Learning Experiments on OpenML. arXiv preprint arXiv:1806.10961.
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  4. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  5. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. The Journal of Open Source Software, 967.
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  6. Casalicchio G, Molnar C, Bischl B (2018) Visualizing the Feature Importance for Black Box Models. arXiv preprint.
    arXiv:1804.06620
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  7. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786.
    10.21105/joss.00786
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  8. Schoedel R, Au Q, Völkel S et al. (2018) Digital footprints of sensation seeking: a traditional concept in the big data era.
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  9. Völkel ST, Graefe J, Schödel R et al. (2018) I Drive My Car and My States Drive Me Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’18, pp. 198–203. ACM Press, New York, New York, USA.
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  10. Horn D, Dagge M, Sun X, Bischl B (2017) First Investigations on Noisy Model-Based Multi-objective Optimization. In: In: Trautmann H , In: Rudolph G , In: Klamroth K et al. (eds) Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, pp. 298–313. Springer International Publishing, Cham.
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  11. Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373.
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  12. Kotthaus H, Richter J, Lang A et al. (2017) RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization International Conference on Learning and Intelligent Optimization, pp. 180–195. Springer.
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  13. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
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  14. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 1–15.
    arXiv:1701.01293
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  15. Casalicchio G, Lesaffre E, Küchenhoff H, Bruyneel L (2017) Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship 49, 537–547.
    Researchgate
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  16. Bischl B, Casalicchio G, Feurer M et al. (2017) OpenML benchmarking suites and the OpenML100. arXiv preprint arXiv:1708.03731.
    arXiv:1708.03731
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  17. Lang M, Bischl B, Surmann D (2017) batchtools: Tools for R to work on batch systems. The Journal of Open Source Software 2.
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  18. Thomas J, Hepp T, Mayr A, Bischl B (2017) Probing for sparse and fast variable selection with model-based boosting.
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  19. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. arXiv preprint arXiv:1701.01293.
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  20. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
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  21. Stachl C, Hilbert S, Au Q et al. (2017) Personality Traits Predict Smartphone Usage (C Wrzus, Ed.). European Journal of Personality 31, 701–722.
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  22. Rietzler M, Geiselhart F, Thomas J, Rukzio E (2016) FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 73–84. ACM.
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  23. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  24. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2016) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401.
    Researchgate
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  25. Bischl B, Lang M, Kotthoff L et al. (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942.
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  26. Bischl B, Kühn T, Szepannek G (2016) On Class Imbalance Correction for Classification Algorithms in Credit Scoring Operations Research Proceedings 2014, pp. 37–43. Springer International Publishing.
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  27. Degroote H, Bischl B, Kotthoff L, Causmaecker PD (2016) Reinforcement Learning for Automatic Online Algorithm Selection - an Empirical Study Proceedings of the 16th ITAT Conference Information Technologies - Applications and Theory, Tatranské Matliare, Slovakia, September 15-19, 2016., pp. 93–101.
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  28. Horn D, Bischl B (2016) Multi-objective Parameter Configuration of Machine Learning Algorithms using Model-Based Optimization Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, pp. 1–8. IEEE.
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  29. Richter J, Kotthaus H, Bischl B, Marwedel P, Rahnenführer J, Lang M (2016) Faster Model-Based Optimization through Resource-Aware Scheduling Strategies Proceedings of the 10th Learning and Intelligent OptimizatioN Conference (LION 10), Ischia Island (Napoli), Italy.
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  30. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2016) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401.
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  31. Horn D, Demircioğlu A, Bischl B, Glasmachers T, Weihs C (2016) A Comparative Study on Large Scale Kernelized Support Vector Machines. Advances in Data Analysis and Classification, 1–17.
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  32. Weihs C, Horn D, Bischl B (2016) Big data Classification: Aspects on Many Features and Many Observations. In: In: Wilhelm AFX , In: Kestler HA (eds) Analysis of Large and Complex Data, pp. 113–122. Springer International Publishing, Cham.
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  33. Demirciğlu A, Horn D, Glasmachers T, Bischl B, Weihs C (2016) Fast model selection by limiting SVM training times. arxiv.org
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  34. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547.
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  35. Bauer N, Friedrichs K, Bischl B, Weihs C (2015) Fast Model Based Optimization of Tone Onset Detection by Instance Sampling Data Analysis, Machine Learning and Knowledge Discovery,
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  36. Bischl B, Kühn T, Szepannek G (2015) On Class Imbalancy Correction for Classification Algorithms in Credit Scoring Operations Research Proceedings 2014, Springer.
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  37. Bossek J, Bischl B, Wagner T, Rudolph G (2015) Learning feature-parameter mappings for parameter tuning via the profile expected improvement Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1319–1326. ACM.
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  38. Brockhoff D, Bischl B, Wagner T (2015) The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study GECCO ’15 Companion, Madrid, Spain.
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  39. Horn D, Wagner T, Biermann D, Weihs C, Bischl B (2015) Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. In: In: Gaspar-Cunha A , In: Henggeler Antunes C , In: Coello CC (eds) Evolutionary Multi-Criterion Optimization (EMO), pp. 64–78. Springer.
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  40. Bischl B, Lang M, Mersmann O, Rahnenführer J, Weihs C (2015) BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments. Journal of Statistical Software 64, 1–25.
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  41. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2015) The Residual-based Predictiveness Curve - a Visual Tool to Assess the Performance of Prediction Models. Biometrics.
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  42. Feilke M, Bischl B, Schmid VJ, Gertheiss J (2015) Boosting in non-linear regression models with an application to DCE-MRI data. Methods of Information in Medicine.
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  43. Kotthaus H, Korb I, Lang M, Bischl B, Rahnenführer J, Marwedel P (2015) Runtime and memory consumption analyses for machine learning R programs. Journal of Statistical Computation and Simulation 85, 14–29.
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  44. Lang M, Kotthaus H, Marwedel P, Weihs C, Rahnenführer J, Bischl B (2015) Automatic model selection for high-dimensional survival analysis. Journal of Statistical Computation and Simulation 85, 62–76.
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  45. Bischl B, Schiffner J, Weihs C (2014) Benchmarking Classification Algorithms on High-Performance Computing Clusters. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 23–31. Springer.
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  46. Bischl B, Wessing S, Bauer N, Friedrichs K, Weihs C (2014) MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization. In: In: Pardalos PM , In: Resende MGC , In: Vogiatzis C , In: Walteros JL (eds) Learning and Intelligent Optimization, pp. 173–186. Springer.
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  47. Kerschke P, Preuss M, Hernández C et al. (2014) Cell Mapping Techniques for Exploratory Landscape Analysis Proceedings of the EVOLVE 2014: A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, pp. 115–131. Springer.
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  48. Meyer O, Bischl B, Weihs C (2014) Support Vector Machines on Large Data Sets: Simple Parallel Approaches. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 87–95. Springer.
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  49. Vatolkin I, Bischl B, Rudolph G, Weihs C (2014) Statistical Comparison of Classifiers for Multi-objective Feature Selection in Instrument Recognition. In: In: Spiliopoulou M , In: Schmidt-Thieme L , In: Janning R (eds) Data Analysis, Machine Learning and Knowledge Discovery, pp. 171–178. Springer.
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  50. Bischl B, Kerschke P, Kotthoff L et al. (2014) ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence.
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  51. Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C (2014) Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary Computation Journal.
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  52. Vanschoren J, Rijn JN van, Bischl B, Torgo L (2014) OpenML: Networked Science in Machine Learning. SIGKDD Explor. Newsl. 15, 49–60.
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  53. Bergmann S, Ziegler N, Bartels T et al. (2013) Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry science 92, 1171–1176.
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  54. Ziegler N, Bergmann S, Huebei J et al. (2013) Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT 126, 181–188.
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  55. Hess S, Wagner T, Bischl B (2013) PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection. In: In: Nicosia G , In: Pardalos P (eds) Learning and Intelligent Optimization, pp. 110–124. Springer.
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  56. Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H (2013) A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem Foundations of Genetic Algorithms (FOGA),
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  57. Rijn J van, Bischl B, Torgo L et al. (2013) OpenML: A Collaborative Science Platform ECML/PKDD (3), pp. 645–649.
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  58. Rijn J van, Umaashankar V, Fischer S et al. (2013) A RapidMiner extension for Open Machine Learning RapidMiner Community Meeting and Conference (RCOMM), pp. 59–70.
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  59. Bischl B, Schiffner J, Weihs C (2013) Benchmarking local classification methods. Computational Statistics 28, 2599–2619.
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  60. Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F (2013) A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence March, 1–32.
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  61. Bischl B, Mersmann O, Trautmann H, Preuss M (2012) Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning Genetic and Evolutionary Computation Conference (GECCO),
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  62. Mersmann O, Bischl B, Bossek J, Trautmann H, M. W, Neumann F (2012) Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness Learning and Intelligent Optimization Conference (LION),
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  63. Schiffner J, Bischl B, Weihs C (2012) Bias-variance analysis of local classification methods. In: In: Gaul W , In: Geyer-Schulz A , In: Schmidt-Thieme L , In: Kunze J (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 49–57. Springer, Berlin Heidelberg.
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  64. Bischl B, Mersmann O, Trautmann H, Weihs C (2012) Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation. Evolutionary Computation 20, 249–275.
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  65. Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Weihs C, Konen W (2012) Tuning and evolution of support vector kernels. Evolutionary Intelligence 5, 153–170.
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  66. Bischl B, Lang M, Mersmann O, Rahnenfuehrer J, Weihs C (2012) Computing on high performance clusters with R: Packages BatchJobs and BatchExperiments. SFB 876, TU Dortmund University
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  67. Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G (2011) Exploratory Landscape Analysis. In: In: Krasnogor N (ed) Proceedings of the 13th annual conference on genetic and evolutionary computation (GECCO ’11), pp. 829–836. ACM, New York, NY, USA.
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  68. Weihs C, Friedrichs K, Bischl B (2011) Statistics for hearing aids: Auralization Second Bilateral German-Polish Symposium on Data Analysis and its Applications (GPSDAA),
  69. Blume H, Bischl B, Botteck M et al. (2011) Huge Music Archives on Mobile Devices. IEEE Signal Processing Magazine 28, 24–39.
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  70. Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Konen W (2011) On the Tuning and Evolution of Support Vector Kernels. Research Center CIOP (Computational Intelligence, Optimization and Data Mining), Cologne University of Applied Science, Faculty of Computer Science and Engineering Science
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  71. Bischl B, Mersmann O, Trautmann H (2010) Resampling Methods in Model Validation. In: In: Bartz-Beielstein T , In: Chiarandini M , In: Paquete L , In: Preuss M (eds) WEMACS – Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems, Technical Report TR 10-2-007, Department of Computer Science, TU Dortmund University.
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  72. Bischl B, Eichhoff M, Weihs C (2010) Selecting Groups of Audio Features by Statistical Tests and the Group Lasso 9. ITG Fachtagung Sprachkommunikation, VDE Verlag, Berlin, Offenbach.
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  73. Bischl B, Vatolkin I, Preuss M (2010) Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, pp. 314–323. Springer.
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  74. Szepannek G, Gruhne M, Bischl B et al. (2010) Perceptually Based Phoneme Recognition in Popular Music. In: In: Locarek-Junge H , In: Weihs C (eds) Classification as a Tool for Research, pp. 751–758. Springer, Berlin Heidelberg.
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  75. Weihs C, O. M, Bischl B et al. (2010) A Case Study on the Use of Statistical Classification Methods in Particle Physics MSDM2010, Tunis,
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  76. Bischl B, Ligges U, Weihs C (2009) Frequency estimation by DFT interpolation: A comparison of methods. SFB 475, Faculty of Statistics, TU Dortmund, Germany
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  77. Szepannek G, Bischl B, Weihs C (2008) On the Combination of Locally Optimal Pairwise Classifiers. Journal of Engineering Applications of Artificial Intelligence 22, 79–85.
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  78. Szepannek G, Bischl B, Weihs C (2007) On the Combination of Locally Optimal Pairwise Classifiers Machine Learning and Data Mining in Pattern Recognition, pp. 104–116. Springer, Berlin Heidelberg.
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