@article{Stulpe2019,
  author    = {Stulpe, Werner},
  title     = {Aspects of the Quantum-Classical Connection Based on Statistical Maps},
  series = {Foundations of Physics},
  volume    = {49},
  journal   = {Foundations of Physics},
  number    = {6},
  publisher = {Springer},
  address   = {Berlin},
  doi       = {10.1007/s10701-019-00269-9},
  pages     = {677 -- 692},
  year      = {2019},
  language  = {en}
}
@phdthesis{Engelmann2019,
  author    = {Engelmann, Ulrich M.},
  title     = {Assessing magnetic fluid hyperthermia : magnetic relaxation simulation, modeling of nanoparticle uptake inside pancreatic tumor cells and in vitro efficacy},
  publisher = {Infinite Science Publishing},
  address   = {L{\"u}beck},
  isbn      = {978-3-945954-58-4},
  year      = {2019},
  language  = {en}
}
@article{AlbrachtArampatzisBaltzopoulos2008,
  author    = {Albracht, Kirsten and Arampatzis, A. and Baltzopoulos, V.},
  title     = {Assessment of muscle volume and physiological cross-sectional area of the human triceps surae muscle in vivo},
  series = {Journal of Biomechanics},
  volume    = {41},
  journal   = {Journal of Biomechanics},
  issn      = {0021-9290},
  doi       = {10.1016/j.jbiomech.2008.04.020},
  pages     = {2211 -- 2218},
  year      = {2008},
  language  = {en}
}
@article{SiqueiraBaeckerPoghossianetal.2010,
  author    = {Siqueira, Jos{\´e} R. Jr. and B{\"a}cker, Matthias and Poghossian, Arshak and Zucolotto, Valtencir and Oliveira, Osvaldo N. Jr. and Sch{\"o}ning, Michael Josef},
  title     = {Associating biosensing properties with the morphological structure of multilayers containing carbon nanotubes on field-effect devices},
  series = {Physica status solidi (a). 207 (2010), H. 4},
  journal   = {Physica status solidi (a). 207 (2010), H. 4},
  isbn      = {1862-6300},
  pages     = {781 -- 786},
  year      = {2010},
  language  = {en}
}
@article{BialonskiLehnertz2013,
  author    = {Bialonski, Stephan and Lehnertz, Klaus},
  title     = {Assortative mixing in functional brain networks during epileptic seizures},
  series = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume    = {23},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number    = {3},
  doi       = {10.1063/1.4821915},
  pages     = {033139},
  year      = {2013},
  language  = {en}
}
@article{Dikta1995,
  author    = {Dikta, Gerhard},
  title     = {Asymptotic Normality Under the Koziol-Green Model},
  series = {Communications in Statistics: Theory and Methods. 24 (1995), H. 6},
  journal   = {Communications in Statistics: Theory and Methods. 24 (1995), H. 6},
  isbn      = {0361-0926},
  pages     = {1537 -- 1549},
  year      = {1995},
  language  = {en}
}
@article{DiktaKuehlheimMendoncaetal.2015,
  author    = {Dikta, Gerhard and K{\"u}hlheim, Ren{\´e} and Mendonca, Jorge and Una-Alcarez, Jacobo de},
  title     = {Asymptotic representation of presmoothed Kaplan-Meier integrals with covariates in a semiparametric censorship model},
  series = {Journal of Statistical Planning and Inference},
  volume    = {Vol. 171},
  journal   = {Journal of Statistical Planning and Inference},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0378-3758},
  doi       = {10.1016/j.jspi.2015.12.001},
  pages     = {10 -- 37},
  year      = {2015},
  language  = {en}
}
@article{Dikta2014,
  author    = {Dikta, Gerhard},
  title     = {Asymptotically efficient estimation under semi-parametric random censorship models},
  series = {Journal of multivariate analysis},
  volume    = {124},
  journal   = {Journal of multivariate analysis},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1095-7243 (E-Journal); 0047-259X (Print)},
  doi       = {10.1016/j.jmva.2013.10.002},
  pages     = {10 -- 24},
  year      = {2014},
  abstract  = {We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan-Meier estimator. In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.},
  language  = {en}
}
@article{GrotendorstScottAubertFreconetal.2004,
  author    = {Grotendorst, Johannes and Scott, Tony C. and Aubert-Fr{\´e}con, Monique and Hadinger, Gis{\`e}le},
  title     = {Asymptotically exact calculation of the exchange energies of one-active-electron diatomic ions with the surface integral method / Scott, Tony C. ; Aubert-Fr{\´e}con, Monique ; Hadinger, Gis{\`e}le ; Andrae, Dirk ; Grotendorst, Johannes ; Morgan Ill, John D.},
  series = {Journal of Physics B: Atomic, Molecular and Optival Physics. 37 (2004), H. 22},
  journal   = {Journal of Physics B: Atomic, Molecular and Optival Physics. 37 (2004), H. 22},
  isbn      = {0953-4075},
  pages     = {4451 -- 4469},
  year      = {2004},
  language  = {en}
}
@article{SchwabedalSippelBrandtetal.2018,
  author    = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan},
  title     = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning},
  doi       = {10.48550/arXiv.1809.08443},
  year      = {2018},
  abstract  = {Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.},
  language  = {en}
}