@article {7048,
	title = {UMAP does not reproduce high-dimensional similarities due to negative sampling},
	number = {arXiv:2103.14608},
	year = {2021},
	publisher = {arXiv preprint},
	author = {Damrich, S and Hamprecht, F.H.}
}
@proceedings {7085,
	title = {On UMAP{\textquoteright}s True Loss Function},
	volume = {34},
	year = {2021},
	author = {Damrich, S and Hamprecht, FA}
}
@unpublished {7062,
	title = {Understanding Event-Generation Networks via Uncertainties},
	year = {2021},
	publisher = {arXiv preprint},
	url = {https://arxiv.org/abs/2104.04543v1},
	author = {Bellagente, M and Hau{\ss}mann, M and Luchmann, M and Plehn, T}
}
@proceedings {7063,
	title = {Understanding Object Dynamics for Interactive Image-to-Video Synthesis},
	year = {2021},
	abstract = {What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks.},
	url = {https://arxiv.org/abs/2106.11303v1},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald and Bj{\"o}rn Ommer}
}
@article {7045,
	title = {Unsupervised behaviour analysis and magnification (uBAM) using deep learning},
	journal = {Nature Machine Intelligence},
	year = {2021},
	abstract = {Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, because it requires the placement of physical or virtual markers. As well as the effort required for marking the keypoints or annotations necessary for training or fine-tuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. Here, we introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnification of deviations, even across different individuals, is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optogenetic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity.},
	doi = {https://doi.org/10.1038/s42256-021-00326-x},
	url = {https://rdcu.be/ch6pL},
	author = {Biagio Brattoli and Uta B{\"u}chler and Michael Dorkenwald and Philipp Reiser and Linard Filli and Fritjof Helmchen and Anna-Sophia Wahl and Bj{\"o}rn Ommer}
}
@article {Zern:2020ab,
	title = {Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment},
	journal = {Journal of Mathematical Imaging and Vision},
	year = {2020},
	url = {https://doi.org/10.1007/s10851-019-00935-7},
	author = {Zern, A. and M. Zisler and Petra, S. and Schn{\"o}rr, C.}
}
@proceedings {7042,
	title = {Unsupervised Magnification of Posture Deviations Across Subjects},
	year = {2020},
	author = {Michael Dorkenwald and Uta B{\"u}chler and Bj{\"o}rn Ommer}
}
@proceedings {7004,
	title = {Unsupervised Part Discovery by Unsupervised Disentanglement},
	year = {2020},
	month = {09/2020},
	address = {T{\"u}bingen},
	abstract = {We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object{\textquoteright}s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency. Our work demonstrates the feasibility to discover semantic part segmentations without supervision.},
	url = {https://compvis.github.io/unsupervised-part-segmentation/},
	author = {Sandro Braun and Patrick Esser and Bj{\"o}rn Ommer}
}
@article {6339,
	title = {Unsupervised Representation Learning by Discovering Reliable Image Relations},
	journal = {Pattern Recognition},
	volume = {102},
	year = {2020},
	month = {June 2020},
	url = {http://arxiv.org/abs/1911.07808},
	author = {Timo Milbich and Omair Ghori and Bj{\"o}rn Ommer}
}
@article {Zern:2019aa,
	title = {Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment},
	journal = {preprint: arXiv},
	year = {2019},
	url = {https://arxiv.org/abs/1904.10863},
	author = {Zern, A. and M. Zisler and Petra, S. and Schn{\"o}rr, C.}
}
@conference {Zisler:2019aa,
	title = {Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment},
	booktitle = {Proc. SSVM},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {M. Zisler and Zern, A. and Petra, S. and Schn{\"o}rr, C.}
}
@conference {6301,
	title = {Unsupervised Part-Based Disentangling of Object Shape and Appearance},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)},
	year = {2019},
	author = {Dominik Lorenz and Leonard Bereska and Timo Milbich and Bj{\"o}rn Ommer}
}
@conference {6323,
	title = {Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis},
	booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},
	year = {2019},
	abstract = {Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available.},
	url = {https://compvis.github.io/robust-disentangling/},
	author = {Patrick Esser and Johannes Haux and Bj{\"o}rn Ommer}
}
@proceedings {6300,
	title = {Using a Transformation Content Block For Image Style Transfer},
	year = {2019},
	author = {Dmytro Kotovenko and Sanakoyeu, A. and Sabine Lang and Ma, P. and Bj{\"o}rn Ommer}
}
@conference {Zern:2018ac,
	title = {Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment},
	booktitle = {GCPR},
	year = {2018},
	author = {Zern, A. and M. Zisler and Astr{\"o}m, F. and Petra, S. and Schn{\"o}rr, C.}
}
@proceedings {6311,
	title = {Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment},
	year = {2018},
	pages = {698-713},
	doi = {10.1007/978-3-030-12939-2_48},
	author = {Zern, A and M. Zisler and Freddie Astr{\"o}m and Petra, S and Schn{\"o}rr, C}
}
@conference {6187,
	title = {Unsupervised Video Understanding by Reconciliation of Posture Similarities},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
	year = {2017},
	url = {https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17},
	author = {Timo Milbich and Miguel Bautista and Ekaterina Sutter and Bj{\"o}rn Ommer}
}
@conference {Brachmann2016a,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Brachmann2016,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
@article {kelm_11_using,
	title = {Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images},
	journal = {NMR in Biomedicine},
	volume = {25(1)},
	year = {2011},
	note = {1},
	pages = {1-13},
	doi = {10.1002/nbm.1704},
	author = {B. Michael Kelm and F. O. Kaster and Henning, A. and M.-A. Weber and Bachert, P. and B{\"o}singer, P. and Fred A. Hamprecht and Bjoern H. Menze}
}
@mastersthesis {riedl_10_using,
	title = {Using Protein Identification Data to Improve Mass Spectrometry Feature Extraction},
	year = {2010},
	school = {University of Heidelberg},
	author = {Riedl, M.}
}
@conference {Kirk2006,
	title = {Understanding photowork},
	booktitle = {Conference on Human Factors in Computing Systems - Proceedings},
	volume = {2},
	year = {2006},
	pages = {761{\textendash}770},
	abstract = {In this paper we introduce the notion of "photowork" as the activities people perform with their digital photos after capture but prior to end use such as sharing. Surprisingly, these processes of reviewing, downloading, organizing, editing, sorting and filing have received little attention in the literature yet they form the context for a large amount of the {\textquoteright}search{\textquoteright} and {\textquoteright}browse{\textquoteright} activities so commonly referred to in studies of digital photo software. Through a deeper understanding of photowork using field observation and interviews, we seek to highlight its significance as an interaction practice. At the same time, we discover how "search" as it is usually defined may have much less relevance than new ways of browsing for the design of new digital photo tools, in particular, browsing in support of the photowork activities we describe. Copyright 2006 ACM.},
	keywords = {Browsing, Content-based image retrieval, Digital photo albums, Photowork, Searching, Use of images},
	isbn = {1595931783},
	doi = {10.1145/1124772.1124885},
	author = {Kirk, David S and Sellen, Abigail J and Carsten Rother and Wood, Kenneth R}
}
@phdthesis {popp2006,
	title = {Untersuchung von Austauschprozessen an der Wasseroberfl{\"a}che aus Infrarot-Bildsequenzen mittels frequenzmodulierter W{\"a}rmeeinstrahlung},
	year = {2006},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {The study presents a new technique for measuring the gas transfer rate at a free water surface with high temporal and spatial resolution, a method suitable for field experiments. The approach is based on the Controlled Flux Technique (CFT) using heat as a proxy tracer for gas exchange. A periodically varying heat flux at water surface is used. The heat flux is generated by a 100 W CO2-laser, while a highly sensitive infrared camera measures the temperature response of the water surface. Heat - and consequently gas transfer rates - can be deduced from the analysis of the spectral amplitude attenuation at water surface. Furthermore, the approach presented allows direct insight into the transport mechanisms of gas exchange processes by an analysis of the phase shift observed at water surface. A first series of measurements at the Heidelberg wind wave facility AEOLOTRON shows that the spectral amplitude attenuation is in keeping with model predictions. The calculated transfer rates for CO2 are also well in accordance with results from other laboratory and field experiments. Contrary to the amplitude attenuation, the calculated phase shifts differ signicantly from model predictions, especially for high wind speeds. This effect, observed for the first time, can be interpreted as being caused by the intermittence of transfer processes. This would indicate that stochastic processes play a larger role in gas transfer than assumed so far.},
	url = {http://www.ub.uni-heidelberg.de/archiv/6489},
	author = {Christopher Popp}
}
@phdthesis {degreif2006,
	title = {Untersuchungen zum Gasaustausch - Entwicklung und Applikation eines zeitlich aufgel{\"o}sten Massenbilanzverfahrens},
	year = {2006},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {The study presents a novel technique for measuring time resolved gas transfer rates at the water surface. By applying this new mass balance technique neither absolute concentrations of the trace gases nor the measurement of their concentrations in the water phase is required. Gas fluxes are calculated exclusively by the temporal change of the air-sided concentrations in an evasion experiment. The high temporal resolution of this procedure allows fast measurements under different conditions. Systematic measurements of gas exchange rates are now feasible within hours. By using UV-spectroscopy, simultaneous concentration measurements of volatile aromatics in the air and water phases verified the results obtained by the presented technique. Within the framework of the UV-measurements a method was developed to determine the air-sided concentrations of the aromatics precisely, even at low spectral resolution of the spectrometer. The differential optical absorption spectroscopy was successfully applied to a wide concentration range of these tracers. Reliable gas transfer velocities are now also available for very low wind speeds and friction velocities. The transition of the Schmidt number exponent from a flat to a rough water surface was reproduced. The transition begins at unexpectedly low wind speeds and extends over a wide range of wind speeds. For the first time the effect of chemically enhanced gas-exchange was demonstrated under a variety of chemical and physical conditions.},
	url = {http://www.ub.uni-heidelberg.de/archiv/6120},
	author = {Kai Degreif}
}
@conference {Keuchel-et-al-02b,
	title = {Unsupervised Image Partitioning with Semidefinite Programming},
	booktitle = {Pattern Recognition, Proc. 24th DAGM Symposium},
	series = {lncs},
	volume = {2449},
	year = {2002},
	pages = {141{\textendash}149},
	publisher = {Springer},
	organization = {Springer},
	address = {Z{\"u}rich, Switzerland},
	author = {Keuchel, J. and Schn{\"o}rr, C. and Schellewald, C. and Daniel Cremers},
	editor = {van Gool, L.}
}
@phdthesis {schimpf2000,
	title = {Untersuchung des Gasaustausches und der Mikroturbulenz an der Meeresoberfl{\"a}che mittels Thermographie},
	year = {2000},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/545},
	author = {Uwe Schimpf}
}
@phdthesis {balschbach2000,
	title = {Untersuchungen statistischer und geometrischer Eigenschaften von Windwellen und ihrer Wechselwirkung mit der wasserseitigen Grenzschicht},
	year = {2000},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {Using color image processing a refraction based technique for retrieving the slope of a water surface covered with waves has been substantially improved. This multichannel technique is similar to photometric stereo analysis for opaque surfaces. The three independend channels of a color camera allow simultaneous detection of both surface gradient components. The third information is used for normalization to the total illumination intensity for every individual pixel, leading to an increased linearity and enhanced robustness. Errors due to inhomogeneous illumination or disturbance by small particles in the water can be corrected almost completely. For combined measurements the technique can be reduced to two colors. Measuring only one slope component normalization is still possible. At two wind/wave facilities statistical and geometrical information for small scale waves were obtained and compared to results from authors using other techniques. Two combined experiments were conducted. At different scales the wave field and the underlying flow field were observed simultaneously. Hering [1996] computed kinetic energy density and vorticity of the flow field in a 10cm x 10cm frame. In the second combined experiment M{\"u}nsterer [1996] measured concentration profiles of a tracer gas in a 4cm x 4cm section in the viscous boundery layer. Both experiments provided new insights in the interaction between capillary waves, turbulence in the flow field and in the boundary layer respectively.},
	url = {http://www.ub.uni-heidelberg.de/archiv/2473/},
	author = {G{\"u}nther Balschbach}
}
@mastersthesis {ehrler1999,
	title = {Untersuchung physiologischer Parameter und Prozesse in Pflanzen mit Hilfe von Bildverarbeitung},
	year = {1999},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	author = {Harald Ehrler}
}
@phdthesis {lauer1998,
	title = {Untersuchung der Neigungsstatistik von Wasseroberfl{\"a}chenwellen mittels eines schnellen, bildaufnehmenden Verfahrens},
	year = {1998},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Lauer, Hermann}
}
@mastersthesis {fachat1995,
	title = {Untersuchung eines 3D-Aufnahmeverfahrens f{\"u}r Str{\"o}mungsvorg{\"a}nge an der Wasseroberfl{\"a}che},
	year = {1995},
	note = {IUP D-436},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	author = {Andre Fachat}
}
@article {Schnoerr-jmiv-94,
	title = {Unique Reconstruction of Piecewise Smooth Images by Minimizing Strictly Convex Non-Quadratic Functionals},
	volume = {4},
	year = {1994},
	pages = {189{\textendash}198},
	author = {Schn{\"o}rr, C.}
}
@mastersthesis {beyer1993,
	title = {Untersuchung der Relaxationszeiten von winderzeugten Wasseroberfl{\"a}chenwellen durch periodische Variation der Windgeschwindigkeit},
	year = {1993},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Markus Beyer}
}
@mastersthesis {tremmel1989,
	title = {Untersuchungen zur Wiederbel{\"u}ftung von Neckar und Rhein mit der Konstantflu{\ss}methode},
	year = {1989},
	note = {IUP D-252},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Tremmel, H. G.}
}
@article {lifermann1987,
	title = {Une {\`e}tude en soufflerie de la r{\`e}flexion des hyperfr{\`e}quences par des champs de houles et de vagues},
	journal = {Oceanologia Acta},
	volume = {SP},
	year = {1987},
	pages = {15--22},
	doi = {10.5281/zenodo.13128},
	author = {A. Lifermann and Bernd J{\"a}hne and A. Ramamonjiarisoa}
}
@mastersthesis {dutzi1984,
	title = {Untersuchungen zum Einfluss der Temperatur auf den Gasaustausch},
	year = {1984},
	note = {IUP D-190},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.5281/zenodo.14919},
	author = {A. Dutzi}
}
