Publications

Publications of the IDA Lab since project start (March 2020)

[139] F. Berns, G. Zimmermann, C. Borgelt, et al.: Trustworthy Medical Operational AI: Marrying AI and Regulatory Requirements. IEEE Int. Conf. on Big Data, pp. 2700-2704 (2023) https://doi.org/10.1109/BigData59044.2023.10386683

[138] S. Kranzinger, S. Baron, C. Borgelt, et al.: Generalisability of Sleep Stage Classification Based on Interbeat Intervals: Validating Three Machine Learning Approaches on Self-recorded Test Data. Behaviormetrika, Volume 51, pp. 341–358 (2024) https://doi.org/10.1007/s41237-023-00199-x

[137] V. N. Frey, P. B. Langthaler, T. Prinz, et al.: Stress and the City: Mental Health in Urbanized vs. Rural Areas in Salzburg, Austria. Int. J. Environ. Res. Public Health, Volume 21, Issue 11 (2024) https://doi.org/10.3390/ijerph21111459

[136] M. Tschimpke, M. Schreyer, W. Trutschnig: Revisiting the region determined by Spearman’s rho and Spearman’s footrule. Journal of Computational and Applied Mathematics, 116259 (2024), https://doi.org/10.1016/j.cam.2024.116259

[135] J. Ansari, L. Rüschendorf: Supermodular and directionally convex comparison results for general factor models. Journal of Multivariate Analysis (2024) https://doi.org/10.1016/j.jmva.2023.105264

[134] J. Ansari, E. Lütkebohmert, M. Rockel: An Empirical Study on New Model-Free Multi-output Variable Selection Methods. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 9-17, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_2

[133] H. Kaiser: Common Models of Errors in Variables. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 208-216, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_25

[132] G. Schäfer, S. Huber, S. Hirländer, et al.: Python-Based Reinforcement Learning on Simulink Models. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 449-456, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_55

[131] S. Pochaba, R. Kwitt, S. Hirländer, et al.: Multi-agent Reinforcement Learning and Its Application to Wireless Network Communication. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 363-370, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_45

[130] S. Hirländer, S. Pochaba, C. Xu, et al.: Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 175-183, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_21

[129] J. Ansari, S. Fuchs, W. Trutschnig, et al.: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. Advances in Intelligent Systems and Computing (AISC, volume 1458), Springer (2024) https://doi.org/10.1007/978-3-031-65993-5

[128] Y. Wang, S. Fuchs: Hierarchical Variable Clustering Based on Measures of Predictability. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 559-564, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_67

[127] C. Limbach, S. Fuchs: Quantifying Directed Dependence with Kendall’s Tau. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 257-264, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_30

[126] P.B. Langthaler, J. Ansari, S. Fuchs, W. Trutschnig: Constructing measures of dependence via sensitivity of conditional distributions. in: Combining, Modelling and Analyzing Imprecision, in J. Ansari et al. (Eds) Randomness and Dependence, Advances in Intelligent Systems and Computing, Volume 1458, pp. 241-248, Springer (2024) https://doi.org/10.1007/978-3-031-65993-5_28

[125] S. Fuchs, Y. Wang: Hierarchical variable clustering based on the predictive strength between random vectors. Int. Journal of Approximate Reasoning 170 (2024) https://doi.org/10.1016/j.ijar.2024.109185

[124] Y. Deng, D. Lahnsteiner, T. Prinz: Promoting Active and Sustainable Commuting: A Tool for Analysing Location-specific Conditions and Potentials for Walking, Cycling and Public Transport. GI Forum Salzburg (2023) https://doi.org/10.1553/giscience2023_01_s101

[123] G. Kalss, G. Zimmermann, et al.: The Fingerprint of Scalp-EEG in Drug-Resistant Frontal Lobe Epilepsies. J Clin Neurophysiol (2024) https://doi.org/10.1097/WNP.0000000000001106

[122] V. N. Frey, P. Langthaler, G. Zimmermann, et al.: Influence of sports on cortical excitability in patients with spinal cord injury: a TMS study. Front Med Technol (2024) https://doi.org/10.3389/fmedt.2024.1297552

[121] A. E. Carrozzo, A. C. Bathke, G. Zimmermann, et al.: Applying Exercise Capacity and Physical Activity as Single vs Composite Endpoints for Trials of Cardiac Rehabilitation Interventions: Rationale, Use-case, and a Blueprint Method for Sample Size Calculation. Arch Phys Med Rehabil (2024) https://doi.org/10.1016/j.apmr.2024.04.004

[120] P. Bosque Varela, G. Zimmermann, E. Trinka, et al.: Magnetic resonance imaging fingerprints of status epilepticus: A case-control study. Epilepsia (2024) https://doi.org/10.1111/epi.17949

[119] S. Schoenen, G. Zimmermann, et al.: Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations. Orphanet Journal of Rare Diseases (2024) https://doi.org/10.1186/s13023-024-03103-2

[118] A. Romagna, M. Geroldinger, et al.: Wound healing after intracutaneous vs. staple-assisted skin closure in lumbar, non-instrumented spine surgery: a multicenter prospective randomized trial. Acta Neurochirugica (2024) https://doi.org/10.1007/s00701-024-06227-3

[117] J. Nyberg, G. Zimmermann, M. Geroldinger, K. E. Thiel, et al.: Optimizing designs in clinical trials with an application in treatment of Epidermolysis bullosa simplex, a rare genetic skin disease. Computational Statistics & Data Analysis, 199, 108015 (2024) https://doi.org/10.1016/j.csda.2024.108015

[116] T. Pixner, W. Lauth, G. Zimmermann, et al.: Rise in fasting and dynamic glucagon levels in children and adolescents with obesity is moderate in subjects with impaired fasting glucose but accentuated in subjects with impaired glucose tolerance or type 2 diabetes. Frontiers in Endocrinology, 15 (2024) https://doi.org/10.3389/fendo.2024.1368570

[115] A. Domnica Hoeggerl, W. Lauth, G. Zimmermann, et al.: Dissecting the dynamics of SARS-CoV-2 reinfections in blood donors with pauci- or asymptomatic COVID-19 disease course at initial infection. Infectious Diseases, 0, 1-11 (2024) https://doi.org/10.1080/23744235.2024.2367112

[114] K. Zeman-Kuhnert, G. Zimmermann, W. Lauth, et al.: Long-Term Outcomes of Dental Rehabilitation and Quality of Life after Microvascular Alveolar Ridge Reconstruction in Patients with Head and Neck Cancer. Journal of Clinical Medicine, 13, 3110 (2024) https://doi.org/10.3390/jcm13113110

[113] S. Deininger, W. Lauth, et al.: Functional Outcome and Safety of Endoscopic Treatment Options for Benign Prostatic Obstruction (BPO) in Patients ≥ 75 Years of Age. Journal of Clinical Medicine, 13, 1561 (2024) https://doi.org/10.3390/jcm13061561

[112] A. Astner-Rohracher, G. Zimmermann, B. Frauscher, et al.: Prognostic value of the 5-SENSE Score to predict focality of the seizure-onset zone as assessed by stereoelectroencephalography: a prospective international multicentre validation study. BMJ Neurol Open (2024) https://doi.org/10.1136/bmjno-2024-000765

[111] J. Ansari, M. Rockel: Dependence properties of bivariate copula families. Dependence Modeling (2024) https://doi.org/10.1515/demo-2024-0002

[110] J. Ansari, E. Lütkebohmert, A. Neufeld, J. Sester: Improved robust price bounds for multiasset derivatives under market-implied dependence information. Finance and Stochastics (2024) https://doi.org/10.1007/s00780-024-00539-z

[109] A. Santamaría, C. Xu, L. Scomparin, S. Hirländer, S. Pochaba, A. Eichler, J. Kaiser, M. Schenk: The Reinforcerment Learning for Autonomous Accelerators Collaboration. 15th International Particle Accelerator Conference,Nashville, TN (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS62

[108] S. Hirländer, S. Appel, N. Madysa: Data-Driven model predictive control for automated optimitization of injection into the SIS18 synchrotron. 15th International Particle Accelerator Conference,Nashville, TN (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59

[107] S. Hirländer, L. Lamminger, S. Pochaba, J. Kaiser, C. Xu, A. Santamaría, L. Scomparin, V. Kain: Towards few-shot reinforcement learning in particle accelerator control. 15th International Particle Accelerator Conference,Nashville, TN (2024) https://doi.org/10.18429/JACoW-IPAC2024-TUPS59

[106] M. Geroldinger, J. Verbeeck, A.C. Hooker, K.E. Thiel, G. Molenberghs, J. Nyberg, J. Bauer, M. Laimer, V. Wally, A.C. Bathke, G. Zimmermann: Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials. Orphanet J Rare Dis (2023) https://doi.org/10.1186/s13023-023-02990-1

[105] S. Fuchs: Quantifying directed dependence via dimension reduction. Journal of Multivariate Analysis (2024) https://doi.org/10.1016/j.jmva.2023.105266

[104] S. Fuchs, M. Tschimpke: A novel positive dependence property and its impact on a popular class of concordance measures. Journal of Multivariate Analysis (2024) https://doi.org/10.1016/j.jmva.2023.105259

[103] R. Kozlica, G. Schäfer, S. Hirländer, S. Wegenkittl: A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications. iDSC 2023: Data Science—Analytics and Applications (2024) https://doi.org/10.1007/978-3-031-42171-6_15

[102] T. Kasper, N. P. Dietrich, W. Trutschnig: On convergence and mass distributions of multivariate Archimedean copulas and their interplay with the Williamson transform. Journal of Mathematical Analysis and Applications (2024) https://doi.org/10.1016/j.jmaa.2023.127555

[101] J. Ansari, T. Shushi, S. Vanduffel: Up- and down-correlations in normal variance mixture models. Statistics & Probability Letters (2024) https://doi.org/10.1016/j.spl.2023.109949

[100] O. Kartal, N. Lindlbauer, S. Laner-Plamberger, E. Rohde, F. Föttinger, L. Ombres, G. Zimmermann, C. Mrazek, W. Lauth, C. Grabmer: Collection efficiency of mononuclear cells in offline extracorporeal photopheresis: can processing time be shortened? Blood transfusion (2023) https://doi.org/10.2450/BloodTransfus.442

[99] J. Verbeeck, M. Geroldinger, K. Thiel, […], G. Zimmermann: How to analyze continuous and discrete repeated measures in small sample cross-over trials? Biometrics (2023) https://doi.org/10.1111/biom.13920

[98] T. Moser, G. Zimmermann, et al.: Long-term outcome of natalizumab-associated progressive multifocal leukoencephalopathy in Austria: a nationwide retrospective study. J Neurol (2023) https://doi.org/10.1007/s00415-023-11924-7

[97] M. Hanusch, X. He, S. Janssen, J. Selke, W. Trutschnig, R.R. Junker: Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents. Ecosystems (2023) https://doi.org/10.1007/s10021-023-00867-9

[96] J. Fernández Sánchez, J. López-Salazar Codes, J. B. Seoane-Sepúlveda, W. Trutschnig: Generalized Notions of Continued Fractions Ergodicity and Number Theoretic Applications. New York: Chapman And Hall (2023) https://doi.org/10.1201/9781003404064

[95] J. Fernández Sánchez, W. Trutschnig: A link between Kendall’s τ , the length measure andthe surface of bivariate copulas, and a consequence to copulas with self-similar support. Dependence Modeling (2023) https://doi.org/10.1515/demo-2023-0105

[94] R. Kozlica, S. Wegenkittl, S. Hirländer: Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task. in 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE) (2023) https://doi.org/10.1109/isie51358.2023.10228056

[93] A. Oeftiger, S. Garcia, J. Lagrange, S. Hirländer: Active Deep Learning for Nonlinear Optics Design of a Vertical FFA Accelerato. in Proc. IPAC’23, in IPAC’23 – 14th International Particle Accelerator Conference (2023) https://doi.org/10.18429/jacow-ipac2023-wepa026

[92] S. Hirländer, L. Lamminger, G. Zevi-Della-Porta, V. Kain: Ultra fast reinforcement learning in accelerator control demonstrated on CERN AWAKE. in IPAC’23 – 14th International Particle Accelerator Conference (2023) https://doi.org/10.18429/jacow-ipac2023-thpl038

[91] Senker, H. Stefanits, S. Aspalter, W. Trutschnig, J. Franke, A. Gruber: Nonsteroidal anti-inflammatory drugs (NSAID) do not increase blood loss or the incidence of postoperative epidural hematomas when using minimally invasive fusion techniques in the degenerative lumbar spine. Frontiers in Surgery (2022) https://doi.org/10.3389%2Ffsurg.2022.1000238

[90] T. Kasper, N. Dietrich, W. Trutschnig: On convergence and mass distributions of multivariate Archimedean copulas and their interplay with the Williamson transform. Journal of Mathematical Analysis and Applications (2023) https://doi.org/10.1016/j.jmaa.2023.1275555

[89] J. Fernández-Sánchez, J. López-Salazar Codes, J.B. Seoane Sepúlveda, W. Trutschnig: Generalized Notions of Continued Fractions: Ergodicity and Number Theoretic Applications (1st ed.). Chapman and Hall/CRC (2023) https://doi.org/10.1201/9781003404064

[88] A.M. Wiesinger, B. Bigger, R. Giugliani, C. Lampe, M. Scarpa, T. Moser, C. Kampmann, G. Zimmermann, F.B. Lagler: An Innovative Tool for Evidence-Based, Personalized Treatment Trials in Mucopolysaccharidosis. Pharmaceutics (2023) https://doi.org/10.3390/pharmaceutics15051565

[87] F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G. Zevi Della Porta, S. Hirländer: Towards automatic setup of 18 MeV electron beamline using machine learning. Mach. Learn.: Sci. Technol. (2023) https://doi.org/10.1088/2632-2153/acce21

[86] E. Trinka, L.J. Rainer, C.A. Granbichler, G. Zimmermann, M. Leitinger: Mortality, and life expectancy in Epilepsy and Status epilepticus — current trends and future aspects. Front Epidemiol (2023) https://doi.org/10.3389/fepid.2023.1081757

[85] M. Geroldinger, J. Verbeeck, K.E. Thiel, G. Molenberghs, A.C. Bathke, M. Laimer, G. Zimmermann: A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases. Biom J. (2023) https://doi.org/10.1002/bimj.202200236

[84] F. Schöpflin, S. Erber, D. Madlener, T. Prinz: Densification of Single and Two-Family Houses considering Green Space and Mobility. Acta Polytechnica CTU Proceedings (2022) https://doi.org/10.14311/APP.2022.38.0613

[83] G. Schäfer, R. Kozlica, S. Wegenkittl, S.Huber: An Architecture for Deploying Reinforcement Learning in Industrial Environments. In: R. Moreno-Díaz, F. Pichler, A. Quesada-Arencibia (eds) Computer Aided Systems Theory – EUROCAST 2022 (2022) https://doi.org/10.1007/978-3-031-25312-6_67

[82] S. Fuchs and M. Tschimpke: Total positivity of copulas from a Markov kernel perspective. Journal of Mathematical Analysis and Applications (2022) https://doi.org/10.1016/j.jmaa.2022.126629

[81] M. Genitrini, J. Fritz, G. Zimmermann, H. Schwameder: Downhill Sections Are Crucial for Performance in Trail Running Ultramarathons-A Pacing Strategy Analysis. J Funct Morphol Kinesiol. (2022) https://doi.org/10.3390/jfmk7040103

[80] S. Laner-Plamberger S, […], W. Lauth, G. Zimmermann, et al.: SARS-CoV-2 IgG Levels Allow Predicting the Optimal Time Span of Convalescent Plasma Donor Suitability. Diagnostics (2022) https://doi.org/10.3390/diagnostics12112567

[79] T. Kasper, W. Trutschnig: A Markov Kernel Approach to Multivariate Archimedean Copulas. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science (2022) https://doi.org/10.1007/978-3-031-15509-3_30

[78] N.P. Dietrich, J. Fernández Sánchez, W. Trutschnig: Convergence of Copulas Revisited: Different Notions of Convergence and Their Interrelations. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science (2022) https://doi.org/10.1007/978-3-031-15509-3_16

[77] M. Pallauf, F. Steinkohl, G. Zimmermann, et al.: External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy. World Journal of Urology (2022) https://doi.org/10.1007/s00345-022-04119-8

[76] W. Trutschnig, F. Griessenberger: On Quantifying and Estimating Directed Dependence. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science (2022) https://doi.org/10.1007/978-3-031-15509-3_50

[75] L. Machegger […], T. Prüwasser, G. Zimmermann et al.: Quantitative Analysis of Diffusion-Restricted Lesions in a Differential Diagnosis of Status Epilepticus and Acute Ischemic Stroke. Front Neurol. (2022) https://doi.org/10.3389/fneur.2022.926381

[74] F. Griessenberger, W. Trutschnig: qad: An R-package to detect asymmetric and directed dependence in bivariate samples. Methods in Ecology and Evolution (2022) https://doi.org/10.1111/2041-210X.13951

[73] F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G.Z.D. Porta, S. Hirländer: Automatic setup of 18 MeV electron beamline using machine learning. InProceedings (Velotti2022AutomaticSO) (2022) https://doi.org/10.48550/arXiv.2209.03183

[72] F. Griessenberger, W. Trutschnig: Maximal asymmetry of bivariate copulas and consequences to measures of dependence. Dependence Modeling (2022) https://doi.org/10.1515/demo-2022-0115

[71] V. Kain, N. Bruchon, S. Hirländer, N. Madysa, I. Vojskovic, P.K. Skowronski, G. Valentino: Test of Machine Learning at the Cern LINAC4. InProceedings (Kain2022TESTOM) (2022) https://doi.org/10.18429/JACoW-HB2021-TUEC4

[70] S. Fuchs: The simplifying assumption in pair-copula constructions from an analytic perspective. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science (2022) https://doi.org/10.1007/978-3-031-15509-3_20

[69] Á.K. Csete, A. Kovács-Győri, P. Szilassi: Age-group-based evaluation of residents’ urban green space provision: Szeged, Hungary. A case study. Hungarian Geographical Bulletin (2022) https://doi.org/10.15201/hungeobull.71.3.3

72] F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G.Z.D. Porta, S. Hirländer: Automatic setup of 18 MeV electron beamline using machine learning. InProceedings (Velotti2022AutomaticSO) (2022) https://doi.org/10.48550/arXiv.2209.03183

[68] L. Grech, G. Valentino, D. Alves, S. Hirländer: Application of reinforcement learning in the LHC tune feedback. Frontiers in Physics (2022) https://doi.org/10.3389/fphy.2022.929064

[67] M. Happ, J.L. Du, M. Herlich, C. Maier, P. Dorfinger, J. Suárez-Varela: Exploring the Limitations of Current Graph Neural Networks for Network Modeling. In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium. (2022) https://ieeexplore.ieee.org/document/9789708

[66] S. Fuchs, M. Tschimpke:. On positive dependence properties for Archimedean copulas. In L.A. García-Escudero et al. (Eds.), Building Bridges between Soft and Statistical Methodologies for Data Science (2022) https://doi.org/10.1007/978-3-031-15509-3_21

[68] L. Grech, G. Valentino, D. Alves, S. Hirländer: Application of reinforcement learning in the LHC tune feedback. Frontiers in Physics (2022) https://doi.org/10.3389/fphy.2022.929064

[67] M.Happ, J.L. Du, M. Herlich, C. Maier, P. Dorfinger, J. Suárez-Varela: Exploring the Limitations of Current Graph Neural Networks for Network Modeling. In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium (2022) https://ieeexplore.ieee.org/document/9789708

[66] F. Griessenberger, R.R. Junker, W. Trutschnig: On a multivariate copula-based dependence measure and its estimation. Electronic Journal of Statistics (2022) https://doi.org/10.1214/22-EJS2005

[65] T. Mroz, J. Fernández Sánchez, S. Fuchs, W.Trutschnig: On distributions with fixed marginals maximizing the joint or the prior default probability, estimation, and related results. Journal of Statistical Planning and Inference (2022) https://doi.org/10.1016/j.jspi.2022.07.005

[64] C. Ferner: Captioning Bosch: A Twitter Bot. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, ĲCAI-22 (2022) https://doi.org/10.24963/ijcai.2022/694

[63] C. Ferner, S. Wegenkittl: Benefits from Variational Regularization in Language Models. Machine Learning & Knowledge Extraction (2022) https://doi.org/10.3390/make4020025

[62] L.J. Rainer, M. Kronbichler, G. Kuchukhidze, E. Trinka, P.B. Langthaler, L. Kronbichler, S. Said-Yuerekli, M. Kirschner, G. Zimmermann, J. Höfler, E. Schmid, M. Braun: Emotional Word Processing in Patients With Juvenile Myoclonic Epilepsy. Front Neurol. (2022) https://doi.org/10.3389/fneur.2022.875950

[61] J. Carmona Tapia, J. Fernández Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, Spaceability, and Latticeability of subsets of C([0,1]) and Sobolev Spaces. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas (2022), https://doi.org/10.1007/s13398-022-01256-y

[60] M.J. Mair, J.M. Berger, M. Mitterer, P. Gattinger, J.M. Berger, W. Trutschnig, A.C. Bathke, et al.: Enhanced SARS-CoV-2 breakthrough infections in patients with hematologic and solid cancers due to Omicron. Cancer Cell (2022) https://doi.org/10.1016/j.ccell.2022.04.003

[59] F. Griessenberger, R.R. Junker, W. Trutschnig: On a multivariate copula-based dependence measure and its estimation. Electronic Journal of Statistics (2022) https://doi.org/10.1214/22-EJS2005

[58] V. Nunhofer, L. Weidner, A.D. Hoeggerl, G. Zimmermann, et al.: Persistence of Naturally Acquired and Functional SARS-CoV-2 Antibodies in Blood Donors One Year after Infection. Viruses (2022) https://doi.org10.3390/v14030637

[57] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. Proc. 38th Int. Conf. on Machine Learning (ICML 2021) PMLR Proceedings (2021), https://youtu.be/38dvqdYEs1o, https://doi.org/10.48550/arXiv.2105.04019

[56] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: GenDR: A Generalized Differentiable Renderer. Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR 2022, New Orleans, LA, USA) (2021), https://youtu.be/p-ZCcUWzriE, https://doi.org/10.48550/arXiv.2204.13845

[55] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Monotonic Differentiable Sorting Networks. Proc. 10th Int. Conf. on Learning Representations (ICLR 2022, virtual) (2021), https://youtu.be/Rl-sFaE1z4M, https://doi.org/10.48550/arXiv.2203.09630

[544] K. Aleksovska, T. Kobulashvili, J. Costa, G. Zimmermann, et al.: European Academy of Neurology guidance for developing and reporting clinical practice guidelines on rare neurological diseases. European Journal of Neurology (2022) https://doi.org/10.1111/ene.15267

[53] J. Fernández-Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, algebrability, and sequences of random variables. Mathematische Nachrichten (2022) https://doi.org/10.1002/mana.202000102

[52] S. Roesch, A. O’Sullivan, G. Zimmermann, A. Mair, C. Lipuš, J.A. Mayr, S.B. Wortmann and G. Rasp: Mitochondrial Disease and Hearing Loss in Children: A Systematic Review. The Laryngoscope (2022) https://doi.org/10.1002/lary.30067

[51] M.J. Mair, J.M. Berger, M. Mitterer, M. Gansterer, A.C. Bathke, W. Trutschnig, A. S. Berghoff, T. Perkmann, H. Haslacher, W.W. Lamm, M. Raderer, S. Tobudic, T. Fuereder, T. Buratti, D. Fong, M. Preusser: Third dose of SARS-CoV-2 vaccination in hemato-oncological patients and health care workers: immune responses and adverse events – a retrospective cohort study. European Journal of Cancer (2022) https://doi.org/10.1016/j.ejca.2022.01.019

[50] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Learning with Algorithmic Supervision via Continuous Relaxations2. Proc. Neural Information Processing Systems 34 (NeurIPS 2021) (2021), https://youtu.be/01ENzpkjOCE, https://doi.org/10.48550/arXiv.2110.05651

[49] F. Petersen, C. Borgelt, H. Kuehne, O. Deussen: Learning with Algorithmic Supervision via Continuous Relaxation. Proc. Neural Information Processing Systems 34 (NeurIPS 2021) (2021) https://doi.org/10.48550/arXiv.2110.05651

[48] V. Racher, C. Borgelt: Gradient Ascent for Best Response Regression. Proc. 19th Int. Symposium on Intelligent Data Analysis (IDA 2021, Porto, Portugal) (2021) https://doi.org/10.1007/978-3-030-74251-5_12

[47] A. Astner-Rohracher, G. Zimmermann, T. Avigdor, et al.: Development and Validation of the 5-SENSE Score to Predict Focality of the Seizure-Onset Zone as Assessed by Stereoelectroencephalography. JAMA Neurol. (2021) https://doi.org/10.1001/jamaneurol.2021.4405

[46] M. Wagner, G. Brunauer, A.C. Bathke, S.C. Cary, R. Fuchs, L.G. Sancho, R. Türk, U. Ruprecht: Macroclimatic conditions as main drivers for symbiotic association patterns in lecideoid lichens along the Transantarctic Mountains, Ross Sea region, Antarctica. Scientific Reports (2021) https://doi.org/10.1038/s41598-021-02940-6

[45] G. Zimmermann, E. Brunner, W. Brannath, M. Happ, A.C. Bathke: Pseudo-Ranks: The Better Way of Ranking? The American Statistician (2021) https://doi.org/10.1080/00031305.2021.1972836

[44] A. Egger-Rainer, S.M. Hettegger, R. Feldner, S. Arnold, C. Bosselmann, H. Hamer, A. Hengsberger, J. Lang, S. Lorenzl, H. Lerche, S. Noachtar, E. Pataraia, A. Schulze-Bonhage, A.M. Staack, E. Trinka, I. Unterberger, G. Zimmermann: Do all patients in the epilepsy monitoring unit experience the same level of comfort? A quantitative exploratory secondary analysis. J Adv Nurs. (2021) https://doi.org/10.1111/jan.15105

[43] E. Gfrerer, D. Laina, G. Danae, M. Gibernau, R. Fuchs, M. Happ, T. Tolasch, W. Trutschnig, A.C. Hörger, H.P. Comes, S. Dötterl: Floral scents of a deceptive plant are hyperdiverse and under population-specific phenotypic selection. Frontiers in Plant Science (2021) https://doi.org/10.3389/fpls.2021.719092

[42] W. Senker, H. Stefanits, M. Gmeiner, W. Trutschnig, C. Radl, A. Gruber: The Influence of Smoking in Minimally Invasive Spinal Fusion Surgery. Open Medicine (2021) https://doi.org/10.1515/med-2021-022

[41] F. Kröger, G. Weber, S. Hirländer, R. Alemany–Fernández, M. W. Krasny, T. Stohlker, I. Tolstikhina, V. Shevelko: Charge-state distributions of highly charged lead ions at relativistic collision energies. Annalen der Physik (2021) https://doi.org/10.1002/andp.202100245

[40] N. Bruchon, G. Fenu, G. Gaio, S. Hirländer, M. Lonza, F.A. Pellegrino, E. Salvato: An Online Iterative Linear Quadratic Approach for a Satisfactory Working Point Attainment at FERMI. Information (2021) https://doi.org/10.3390/info12070262

[39] T. Kovács, A. Kovacs-Györi, B. Resch: #AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling. International Journal of Geo-Information (2021) https://www.mdpi.com/2220-9964/10/9/585#

[38] J. Suárez-Varela, M. Ferriol-Galmés, A. López, P. Almasan, G. Bernárdez , D. Pujol-Perich, K. Rusek, L. Bonniot, C. Neumann, F. Schnitzler, F. Taïani, M. Happ, C. Maier, J. Lei Du, M. Herlich, P. Dorfinger, N.V. Hainke, S. Venz, J. Wegener, H. Wissing, B. Wu, S. Xiao, P. Barlet-Ros, A. Cabellos-Aparicio: The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks. ACM SIGCOMM Computer Communication Review (2021) https://doi.org/10.1145/3477482.3477485

[37] T. Kasper, S. Fuchs, W. Trutschnig: On convergence of associative copulas and related results. Dependence Modeling (2021), https://doi.org/10.1515/demo-2021-0114

[36] R.R. Junker, F. Griessenberger, W. Trutschnig: Estimating scale-invariant directed dependence of bivariate distributions. Computational Statistics and Data Analysis (2021) https://doi.org/10.1016/j.csda.2020.107058

[35] F. Konietschke, C. Cao, A. Gunawardana, G. Zimmermann: Analysis of covariance under variance heteroscedasticity in general factorial designs. Stat Med. (2021), https://doi.org/10.1002/sim.9092

[34] L. Weidner, V. Nunhofer, C. Jungbauer, A.D. Hoeggerl, L. Grüner, C. Grabmer, G. Zimmermann, E. Rohde, S. Laner-Plamberger: Seroprevalence of anti-SARS-CoV-2 total antibody is higher in younger Austrian blood donors. Infection (2021) https://doi.org/10.1007/s15010-021-01639-0

[33] T. Kasper, S. Fuchs, W. Trutschnig: On weak conditional convergence of bivariate Archimedean and Extreme Value copulas, and consequences to nonparametric estimation. Bernoulli (2021) https://doi.org/10.3150/20-BEJ1306

[32] M. Happ, M Herrlich, C. Maier, J. L. Du, P. Dorfinger: Graph‑neural‑network‑based delay estimation for communication networkswith heterogeneous scheduling policies. ITU Journal on Future and Evolving Technologies (2021) ISSN: 2616-8375

[31] A. Kovacs-Györi, B. Resch: Towards an automated spatial workflow for the global monitoring of public urban green accessibility in the light of the sustainable development goals. gis.Science 2 (2021)

[30] J. Pilz, L. Hehenwarter, G. Zimmermann, G. Rendl, G. Schweighofer-Zwink, M. Beheshti, C. Pirich: Feasibility of equivalent performance of 3D TOF [18F]-FDG PET/CT with reduced acquisition time using clinical and semiquantitative parameters. EJNMMI Res. (2021) https://doi.org/10.1186/s13550-021-00784-9

[29] J. Fernández Sánchez, W. Trutschnig, M. Tschimpke: Markov product invariance in classes of bivariate copulas characterized by univariate functions. Journal of Mathematical Analysis and Applications 501(2), 125184 (2021) https://doi.org/10.1016/j.jmaa.2021.125184

[28] T. Mroz, S. Fuchs, W. Trutschnig: How simplifying and flexible is the simplifying assumption in pair-copula constructions – analytic answers in dimension three and a glimpse beyond. Electronic Journal of Statistics 15, 1951-1992 (2021) https://doi.org/10.1214/21-EJS1832

[27] A.C. Bathke, M. Happ, M. Hummer: Indirekte Impfeffekte für Kinder und Jugendliche bei Durchimpfung der Erwachsenen. Vergleichende Fallstudie Schwaz / Tirol, Stand 24.05.2021. Executive Policy Brief (2021)

[26] A. Schenk, M. Neuhäuser, G.D. Ruxton, A.C. Bathke: Predictors of pre-European deforestation on Pacific islands: A re-analysis using modern multivariate non-parametric statistical methods. Forest Ecology and Management (2021) https://doi.org/10.1016/j.foreco.2021.119238

[25] F. Graf, C.D. Hofer, M. Niethammer and R. Kwitt: Dissecting Supervised Constrastive Learning (2021) https://arxiv.org/abs/2102.08817

[24] T. Kiesslich, M. Beyreis, G. Zimmermann and A. Traweger: Citation inequality and the Journal Impact Factor: median, mean, (does it) matter? Scientometrics (2021) https://doi.org/10.1007/s11192-020-03812-y

[23] S. Fuchs, F.M.L. Di Lascio and F. Durante: Dissimilarity functions for rank-based hierarchical clustering of continuous variables. to appear in Computational Statistics and Data Analysis (2021) https://arxiv.org/abs/2007.04799

[22] E. Brunner, F. Konietschke, A.C. Bathke and M. Pauly: Ranks and Pseudo-ranks—Surprising Results of Certain Rank Tests in Unbalanced Designs. International Statistical Review (2020) https://doi.org/10.1111/insr.12418

[21] C. Borgelt: Even Faster Exact k-Means Clustering. Proc. 18th Int. Symp. on Intelligent Data Analysis (IDA 2020, Konstanz, Germany) (2020), https://youtu.be/oJb_RqA6qI4, https://doi.org/10.1007/978-3-030-44584-3_8

[20] F. Durante, J. Fernández Sánchez, W. Trutschnig, M. Úbeda-Flores: On the size of subclasses of quasi-copulas and their Dedekind-MacNeille completion. Mathematics (2020) https://doi.org/10.3390/math8122238

[19] S. Hirländer, N. Bruchon: Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL (2020) https://arxiv.org/abs/2012.09737

[18] A. Kovacs-Györi, A. Ristea, C. Havas, M. Mehaffy, H.H. Hochmair, B. Resch, L. Juhasz, A. Lehner, L. Ramasubramanian, T. Blaschke: Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. ISPRS Int. J. Geo-Inf. (2020) https://doi.org/10.3390/ijgi9120752

[17] V. Kain, S. Hirländer, B. Goddard, F.M.Velotti, G. Zevi Della Porta, N. Bruchon, G. Valentino: Sample-efficient reinforcement learning for CERN accelerator control. Physical Review Accelerators and Beams (2020) https://doi.org/10.1103/PhysRevAccelBeams.23.124801

[16] S. Fuchs, W. Trutschnig: On quantile-based co-risk measures and their estimation. Dependence Modeling (2020) https://doi.org/10.1515/demo-2020-0021

[15] F. Durante, J. Fernández Sánchez, C. Ignazzi, W. Trutschnig: On extremal problems for pairs of uniformly distributed sequences and integrals with respect to copula measures. to appear in Uniform Distribution Theory (2020) https://content.sciendo.com/view/journals/udt/15/2/article-p99.xml?

[14] G. Zimmermann: To rank or to permute when comparing an ordinal outcome between two groups while adjusting for a covariate? In: La Rocca et al. (eds.), Nonparametric Statistics. 4th ISNPS, Salerno, Italy, Springer Proceedings in Mathematics and Statistics (2018) https://doi.org/10.1007/978-3-030-57306-5_48

[13] A. Egger-Rainer, E. Trinka, G. Zimmermann, S. Arnold, C. Boßelmann, H. Hamer, A. Hengsberger, J. Lang, H. Lerche, S. Noachtar, E. Pataraia, A. Schulze-Bonhage, A.M. Staack, I. Unterberger, S. Lorenzl: Assessing comfort in the epilepsy monitoring unit: Psychometric testing of an instrument. Epilepsy Behav (2020) https://doi.org/10.1016/j.yebeh.2020.107460

[12] M. Leitinger, K.N. Poppert, M. Mauritz, F. Rossini, G. Zimmermann, A. Rohracher, G. Kalss, G. Kuchukhidze, J. Höfler, P. Bosque Varela, R. Kreidenhuber, K. Volna, C. Neuray, T. Kobulashvili, C.A. Granbichler, U. Siebert, E. Trinka: Status epilepticus admissions during the COVID-19 pandemic in Salzburg. A population-based study. Epilepsia (2020) https://doi.org/10.1111/epi.16737

[11] F.X.Vialard, R. Kwitt, S. Wei, M. Niethammer: A Shooting Formulation of Deep Learning. NeurIPS 2020 (ERA CORE A*) https://arxiv.org/abs/2006.10330

[10] J.Y. Ahn, S. Fuchs, R. Oh: A copula transformation in multivariate mixed discrete-continuous models. Fuzzy Sets and Systems (2020) https://doi.org/10.1016/j.fss.2020.11.008

[9] M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn, R. Silipo: Guide to Intelligent Data Science (2nd edition). Springer-Verlag, Berlin, Germany 2020, ISBN 978-3-030-45574-3. https://doi:10.1007/978-3-030-45574-3

[8] S. Fuchs and K.D. Schmidt: On order statistics and Kendall’s tau for copulas. Statistics and Probability Letters (2021) https://doi.org/10.1016/j.spl.2020.108972

[7] J. Fernández Sánchez, D.L. Rodríguez-Vidanes, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability, differentiable functions and special derivatives. Banach Journal of Mathematical Analysis (2020) https://doi.org/10.1007/s43037-020-00103-9

[6] M. Wagner, A.C. Bathke, S.C. Cary, T.G.A. Green, R.R. Junker, W. Trutschnig, U. Ruprecht: Myco- and photobiont associations in crustose lichens in the McMurdo Dry Valleys (Antarctica) reveal high differentiation along an elevational gradient. Polar Biology (2020) https://doi.org/10.1007/s00300-020-02754-8

[5] A.S. Berghoff, M. Gansterer, A.C. Bathke, W. Trutschnig, P. Hungerländer, J.M.Berger, J. Kreminger, A.M. Starzer, R. Strassl, R. Schmid, H. Willschke, W. Lamm, M. Raderer, A.D. Gottlieb, N. J. Mauser, M. Preusser: SARS-CoV-2 Testing in Patients With Cancer Treated at a Tertiary Care Hospital During the COVID-19 Pandemic. Journal of Clinical Onkology (2020) https://ascopubs.org/doi/10.1200/JCO.20.01442

[4] G. Zimmermann, E. Trinka: Accounting for individual variability in baseline seizure frequencies when planning randomized clinical trials remains challenging. Epilepsia (2020) https://doi.org/10.1111/epi.16676

[3] L. Bernal-González, J. Fernández Sánchez, J.B. Seoane-Sepúlveda, W. Trutschnig: Highly tempering infinite matrices II: From divergence to convergence via Toeplitz-Silverman matrices. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas (2020) https://doi.org/10.1007/s13398-020-00934-z

[2] R.R. Junker, F. Griessenberger, W. Trutschnig: Estimating scale-invariant directed dependence of bivariate distributions. Computational Statistics and Data Analysis (2021) https://doi.org/10.1016/j.csda.2020.107058

[1] J. Fernández-Sánchez, D.L. Rodríguez-Vidanes, J.B. Seoane-Sepúlveda, W. Trutschnig: Lineability and integrability in the sense of Riemann, Lebesgue, Denjoy, and Khintchine. Journal of Mathematical Analysis and Applications (2020) https://doi.org/10.1016/j.jmaa.2020.124433