@article{ZenderMetzlerLucke2014,
  author    = {Zender, Raphael and Metzler, Richard and Lucke, Ulrike},
  title     = {FreshUP-A pervasive educational game for freshmen},
  series = {Pervasive and mobile computing},
  volume    = {14},
  journal   = {Pervasive and mobile computing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1574-1192},
  doi       = {10.1016/j.pmcj.2013.09.003},
  pages     = {47 -- 56},
  year      = {2014},
  abstract  = {Students beginning their studies at university face manifold problems such as orientation in a new environment and organizing their courses. This article presents the implementation and successful empirical evaluation of the pervasive browser-based educational game "FreshUP", which aims at helping to overcome the initial difficulties of freshmen. In contrast to a conventional scavenger hunt, mobile pervasive games like FreshUP, bridging in-game and real world activities, have the potential to provide help in a motivating manner using new technology which is currently becoming more and more common. (C) 2013 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{ZieheKawanabeHarmeling2004,
  author    = {Ziehe, Andreas and Kawanabe, Motoaki and Harmeling, Stefan},
  title     = {Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation},
  issn      = {1532-4435},
  year      = {2004},
  abstract  = {We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm-a powerful technique from nonparametric statistics-to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearitics. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results},
  language  = {en}
}
@article{ZieheMuellerNolteetal.2000,
  author    = {Ziehe, Andreas and M{\"u}ller, Klaus-Robert and Nolte, G. and Mackert, B.-M. and Curio, Gabriel},
  title     = {Artifact reduction in magnetoneurography based on time-delayed second-order correlations},
  year      = {2000},
  language  = {en}
}
@article{ZienRaetschMikaetal.2000,
  author    = {Zien, Alexander and R{\"a}tsch, Gunnar and Mika, Sebastian and Sch{\"o}lkopf, Bernhard and Lengauer, Thomas and M{\"u}ller, Klaus-Robert},
  title     = {Engineering support vector machine kernels that recognize translation initiation sites},
  issn      = {1367-4803},
  year      = {2000},
  language  = {en}
}