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2018


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Case series: Slowing alpha rhythm in late-stage ALS patients

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Clinical Neurophysiology, 129(2):406-408, 2018 (article)

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DOI [BibTex]

2018


DOI [BibTex]


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Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.

Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

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link (url) [BibTex]

link (url) [BibTex]


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Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

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DOI [BibTex]

DOI [BibTex]


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Measures of distortion for machine learning

Vankadara, L., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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A Differentially Private Kernel Two-Sample Test

Raj*, A., Law*, L., Sejdinovic*, D., Park, M.

2018, *equal contribution (conference) Submitted

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[BibTex]

[BibTex]


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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

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link (url) [BibTex]

link (url) [BibTex]


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A Causal Perspective on Deep Representation Learning

Suter, R.

ETH Zurich, 2018 (mastersthesis)

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[BibTex]


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Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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[BibTex]

[BibTex]


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Endo-VMFuseNet: A Deep Visual-Magnetic Sensor Fusion Approach for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H. B., Sari, A. E., Soylu, U., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-7, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Autofocusing-based phase correction

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Geckos Race Across the Water’s Surface Using Multiple Mechanisms

Nirody, J. L. L. J. A. H. D. F. R.

Current Biology, 28(24):4046-4051.e2, Elsevier, 2018 (article)

Abstract
Acrobatic geckos can sprint at high speeds over challenging terrain {$[$}1{$]$}, scamper up the smoothest surfaces {$[$}2{$]$}, rapidly swing underneath leaves {$[$}3{$]$}, and right themselves in midair by swinging only their tails {$[$}4, 5{$]$}. From our field observations, we can add racing on the water?s surface to the gecko?s list of agile feats. Locomotion at the air-water interface evolved in over a thousand species, including insects, fish, reptiles, and mammals {$[$}6{$]$}. To support their weight, some larger-legged vertebrates use forces generated by vigorous slapping of the fluid?s surface followed by a stroke of their appendage {$[$}7?12{$]$}, whereas smaller animals, like arthropods, rely on surface tension to walk on water {$[$}6, 13{$]$}. Intermediate-sized geckos (Hemidactylus platyurus) fall squarely between these two regimes. Here, we report the unique ability of geckos to exceed the speed limits of conventional surface swimming. Several mechanisms likely contribute in this intermediate regime. In contrast to bipedal basilisk lizards {$[$}7?10{$]$}, geckos used a stereotypic trotting gait with all four limbs, creating air cavities during slapping to raise their head and anterior trunk above water. Adding surfactant to the water decreased velocity by half, confirming surface tension?s role. The superhydrophobic skin could reduce drag during semi-planing. Geckos laterally undulated their bodies, including their submerged posterior trunk and tail, generating thrust for forward propulsion, much like water dragons {$[$}14{$]$} and alligators {$[$}15{$]$}. Geckos again remind us of the advantages of multi-functional morphologies providing the opportunity for multiple mechanisms for motion.

link (url) DOI [BibTex]


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Incorporation of Terbium into a Microalga Leads to Magnetotactic Swimmers

Santomauro, G., Singh, A., Park, B. W., Mohammadrahimi, M., Erkoc, P., Goering, E., Schütz, G., Sitti, M., Bill, J.

Advanced Biosystems, 2(12):1800039, 2018 (article)

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[BibTex]

[BibTex]


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Temporal Human Action Segmentation via Dynamic Clustering

Zhang, Y., Sun, H., Tang, S., Neumann, H.

arXiv preprint arXiv:1803.05790, 2018 (article)

Abstract
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

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link (url) [BibTex]

link (url) [BibTex]


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Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

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link (url) [BibTex]

link (url) [BibTex]


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MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 15(6):066011, 2018 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Robust and Cheap 3D Haptic Sensation using Deformation Patterns and Machine Learning

Huanbo Sun, Georg Martius

In IEEE-RAS International Conference on Humanoid Robots (Humanoids 2018), 2018, to appear (inproceedings)

[BibTex]

[BibTex]


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f-Divergence constrained policy improvement

Belousov, B., Peters, J.

Journal of Machine Learning Research, 2018 (article) Submitted

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[BibTex]

[BibTex]


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Practical Methods for Graph Two-Sample Testing

Ghoshdastidar, D., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Phylogenetic convolutional neural networks in metagenomics

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C.

BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

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DOI [BibTex]

DOI [BibTex]


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Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A.

International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Morphological intelligence counters foot slipping in the desert locust and dynamic robots

Woodward, M. A., Sitti, M.

Proceedings of the National Academy of Sciences, 115, pages: E8358-E8367, 2018 (article)

Abstract
During dynamic terrestrial locomotion, animals use complex multifunctional feet to extract friction from the environment. However, whether roboticists assume sufficient surface friction for locomotion or actively compensate for slipping, they use relatively simple point-contact feet. We seek to understand and extract the morphological adaptations of animal feet that contribute to enhancing friction on diverse surfaces, such as the desert locust (Schistocerca gregaria) [Bennet-Clark HC (1975) J Exp Biol 63:53–83], which has both wet adhesive pads and spines. A buckling region in their knee to accommodate slipping [Bayley TG, Sutton GP, Burrows M (2012) J Exp Biol 215:1151–1161], slow nerve conduction velocity (0.5–3 m/s) [Pearson KG, Stein RB, Malhotra SK (1970) J Exp Biol 53:299–316], and an ecological pressure to enhance jumping performance for survival [Hawlena D, Kress H, Dufresne ER, Schmitz OJ (2011) Funct Ecol 25:279–288] further suggest that the locust operates near the limits of its surface friction, but without sufficient time to actively control its feet. Therefore, all surface adaptation must be through passive mechanics (morphological intelligence), which are unknown. Here, we report the slipping behavior, dynamic attachment, passive mechanics, and interplay between the spines and adhesive pads, studied through both biological and robotic experiments, which contribute to the locust’s ability to jump robustly from diverse surfaces. We found slipping to be surface-dependent and common (e.g., wood 1.32 ± 1.19 slips per jump), yet the morphological intelligence of the feet produces a significant chance to reengage the surface (e.g., wood 1.10 ± 1.13 reengagements per jump). Additionally, a discovered noncontact-type jump, further studied robotically, broadens the applicability of the morphological adaptations to both static and dynamic attachment.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Linking imaging to omics utilizing image-guided tissue extraction

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J.

Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

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[BibTex]

[BibTex]


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Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 (article)

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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Endosensorfusion: Particle filtering-based multi-sensory data fusion with switching state-space model for endoscopic capsule robots

Turan, M., Almalioglu, Y., Gilbert, H., Araujo, H., Cemgil, T., Sitti, M.

In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages: 1-8, 2018 (inproceedings)

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[BibTex]

[BibTex]


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Three‐dimensional patterning in biomedicine: Importance and applications in neuropharmacology

Singh, A. V., Gharat, T., Batuwangala, M., Park, B. W., Endlein, T., Sitti, M.

Journal of Biomedical Materials Research Part B: Applied Biomaterials, 106(3):1369-1382, 2018 (article)

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[BibTex]

[BibTex]


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Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning 3D Shape Completion under Weak Supervision

Stutz, D., Geiger, A.

International Journal of Computer Vision (IJCV), 2018, 2018 (article)

Abstract
We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet and ModelNet as well as on real robotics data from KITTI and Kinect, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and outperforms the data-driven approach of Engelmann et al., while requiring less supervision and being significantly faster.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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3D nanoprinted plastic kinoform x-ray optics

Sanli, U. T., Ceylan, H., Bykova, I., Weigand, M., Sitti, M., Schütz, G., Keskinbora, K.

{Advanced Materials}, 30(36), Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Transformation Invariant Representations with Weak Supervision

Coors, B., Condurache, A., Mertins, A., Geiger, A.

In International Conference on Computer Vision Theory and Applications, International Conference on Computer Vision Theory and Applications, 2018 (inproceedings)

Abstract
Deep convolutional neural networks are the current state-of-the-art solution to many computer vision tasks. However, their ability to handle large global and local image transformations is limited. Consequently, extensive data augmentation is often utilized to incorporate prior knowledge about desired invariances to geometric transformations such as rotations or scale changes. In this work, we combine data augmentation with an unsupervised loss which enforces similarity between the predictions of augmented copies of an input sample. Our loss acts as an effective regularizer which facilitates the learning of transformation invariant representations. We investigate the effectiveness of the proposed similarity loss on rotated MNIST and the German Traffic Sign Recognition Benchmark (GTSRB) in the context of different classification models including ladder networks. Our experiments demonstrate improvements with respect to the standard data augmentation approach for supervised and semi-supervised learning tasks, in particular in the presence of little annotated data. In addition, we analyze the performance of the proposed approach with respect to its hyperparameters, including the strength of the regularization as well as the layer where representation similarity is enforced.

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pdf [BibTex]

pdf [BibTex]


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Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images

Zuffi, S., Kanazawa, A., Black, M. J.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2018, 2018 (inproceedings)

Abstract
Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.

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pdf code/data 3D models Project Page [BibTex]

pdf code/data 3D models Project Page [BibTex]


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Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Photorealistic Video Super Resolution

Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), 2018 (poster)

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[BibTex]

[BibTex]


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Denotational Validation of Higher-order Bayesian Inference

Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., Ostermann, K., Moss, S. K., Heunen, C., Ghahramani, Z.

Proceedings of the ACM on Principles of Programming Languages (POPL), 2(Article No. 60):1-29, ACM, 2018 (conference)

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DOI [BibTex]

DOI [BibTex]


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Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M.

Neuron, 100(5):1224-1240, 2018 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Optimizing Execution of Dynamic Goal-Directed Robot Movements with Learning Control

Koc, O., Maeda, G., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

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arXiv [BibTex]

arXiv [BibTex]


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Retinal image quality of the human eye across the visual field

Meding, K., Hirsch, M., Wichmann, F. A.

14th Biannual Conference of the German Society for Cognitive Science (KOGWIS 2018), 2018 (poster)

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[BibTex]

[BibTex]


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Visualizing and understanding Sum-Product Networks

Vergari, A., Di Mauro, N., Esposito, F.

Machine Learning, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

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[BibTex]

[BibTex]