Biblio

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2023
Berlin, C., Adomeit S., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2023).  Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays. Global Spine Journal. 21925682231154543.
2022
Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.
Adomeit, S., Berlin C., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2022).  Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays. Brain and Spine. 2, 101156.
2015
Schuldt, T., Schubert C., Krutzik M., Bote L., Gaaloul N., Hartwig J., et al. (2015).  Design of a dual species atom interferometer for space. Experimental Astronomy. 39, 167-206.PDF icon Schuldt et al._2015_Design of a dual species atom interferometer for space.pdf (2.98 MB)
2014
Aguilera, D. N., Ahlers H., Battelier B., Bawamia A., Bertoldi A., Bondarescu R., et al. (2014).  STE-QUEST — Test of the universality of free fall using cold atom interferometry. Classical and Quantum Gravity. 31, 115010.PDF icon Aguilera, Ahlers_2014_STE-QUEST—test of the universality of free fall using cold atom interferometry.pdf (778.56 KB)
2005
Heyse, S., Brodte A., Bruttger O., Duerr O., Freeman T., Jung T., et al. (2005).  Quantifying bioactivity on a large scale: quality assurance and analysis of multiparametric ultra-HTS data. JALA: Journal of the Association for Laboratory Automation. 10, 207–212.
2004
Franz, M. O., & Schölkopf B. (2004).  Implicit Wiener series for capturing higher-order interactions in images. (Olshausen, B. A., & Lewicki M., Ed.).{Proc. Sensory Coding and the Natural Environment 2004}.
2003
Franz, M. O., & Chahl J. S. (2003).  Linear combinations of optic flow vectors for estimating self-motion-a real-world test of a neural model. (Becker, S., Obermayer K., & Thrun S., Ed.).{Advances in Neural Information Processing Systems 15}. 1343–1350.
2001
Oliver, D., Frisch H., & Dieterich W. (2001).  Melt viscosities of lattice polymers using a Kramers potential treatment. J. Chem. Phys.. 115, 9042–9045.