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2020


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Fish-like aquatic propulsion studied using a pneumatically-actuated soft-robotic model

Wolf, Z., Jusufi, A., Vogt, D. M., Lauder, G. V.

Bioinspiration & Biomimetics, 15(4):046008, Inst. of Physics, London, 2020 (article)

bio

DOI [BibTex]

2020


DOI [BibTex]


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Biocompatible magnetic micro- and nanodevices: Fabrication of FePt nanopropellers and cell transfection

Kadiri, V. M., Bussi, C., Holle, A. W., Son, K., Kwon, H., Schütz, G., Gutierrez, M. G., Fischer, P.

Advanced Materials, 32(25), Wiley-VCH, Weinheim, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Adopting the Boundary Homogenization Approximation from Chemical Kinetics to Motile Chemically Active Particles

Popescu, M. N., Uspal, W. E.

In Chemical Kinetics, pages: 517-540, World Scientific, New Jersey, NJ, 2020 (incollection)

icm

DOI [BibTex]

DOI [BibTex]


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Causal Discovery from Heterogeneous/Nonstationary Data

Huang, B., Zhang, K., J., Z., Ramsey, J., Sanchez-Romero, R., Glymour, C., Schölkopf, B.

Journal of Machine Learning Research, 21(89):1-53, 2020 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


Optimizing Rank-based Metrics with Blackbox Differentiation
Optimizing Rank-based Metrics with Blackbox Differentiation

Rolinek, M., Musil, V., Paulus, A., Vlastelica, M., Michaelis, C., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 7620-7630, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020, Best paper nomination (inproceedings)

Abstract
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors.

al

Paper @ CVPR Long Oral Short Oral Arxiv Code Pdf Project Page [BibTex]

Paper @ CVPR Long Oral Short Oral Arxiv Code Pdf Project Page [BibTex]


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Metal organic frameworks as tunable linear magnets

Son, K., Kim, R. K., Kim, S., Schütz, G., Choi, K. M., Oh, H.

Physica Status Solidi A, 217(12), Wiley-VCH, Weinheim, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition

Hassan Alhaija, Siva Mustikovela, Varun Jampani, Justus Thies, Matthias Niessner, Andreas Geiger, Carsten Rother

In International Conference on 3D Vision (3DV), 2020 (inproceedings)

Abstract
Neural rendering techniques promise efficient photo-realistic image synthesis while providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been pro-posed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. We pro-pose an auto encoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand.Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and de-composition is indeed beneficial and that our approach out-performs state-of-the-art image-to-image translation base-lines both qualitatively and quantitatively.

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pdf [BibTex]

pdf [BibTex]


Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

am ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients
Additive manufacturing of cellulose-based materials with continuous, multidirectional stiffness gradients

Giachini, P., Gupta, S., Wang, W., Wood, D., Yunusa, M., Baharlou, E., Sitti, M., Menges, A.

Science Advances, 6, American Association for the Advancement of Science, 2020 (article)

Abstract
Functionally graded materials (FGMs) enable applications in fields such as biomedicine and architecture, but their fabrication suffers from shortcomings in gradient continuity, interfacial bonding, and directional freedom. In addition, most commercial design software fail to incorporate property gradient data, hindering explorations of the design space of FGMs. Here, we leveraged a combined approach of materials engineering and digital processing to enable extrusion-based multimaterial additive manufacturing of cellulose-based tunable viscoelastic materials with continuous, high-contrast, and multidirectional stiffness gradients. A method to engineer sets of cellulose-based materials with similar compositions, yet distinct mechanical and rheological properties, was established. In parallel, a digital workflow was developed to embed gradient information into design models with integrated fabrication path planning. The payoff of integrating these physical and digital tools is the ability to achieve the same stiffness gradient in multiple ways, opening design possibilities previously limited by the rigid coupling of material and geometry.

pi

[BibTex]

[BibTex]


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Generation and characterization of focused helical x-ray beams

Loetgering, L., Baluktsian, M., Keskinbora, K., Horstmeyer, R., Wilhein, T., Schütz, G., Eikema, K. S. E., Witte, S.

Science Advances, 6(7), American Association for the Advancement of Science, 2020 (article)

mms

Generation and characterization of focused helical x-ray beams link (url) DOI [BibTex]

Generation and characterization of focused helical x-ray beams link (url) DOI [BibTex]


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Materials for hydrogen-based energy storage - past, recent progress and future outlook

Hirscher, M., Yartys, V. A., Baricco, M., Bellosta von Colbe, J., Blanchard, D., Bowman Jr., R. C., Broom, D. P., Buckley, C. E., Chang, F., Chen, P., Cho, Y. W., Crivello, J., Cuevas, F., David, W. I. F., de Jongh, P. E., Denys, R. V., Dornheim, M., Felderhoff, M., Filinchuk, Y., Froudakis, G. E., Grant, D. M., Gray, E. M., Hauback, B. C., He, T., Humphries, T. D., Jensen, T. R., Kim, S., Kojima, Y., Latroche, M., Li, H., Lotostskyy, M. V., Makepeace, J. W., M\oller, K. T., Naheed, L., Ngene, P., Noréus, D., Nyg\aard, M. M., Orimo, S., Paskevicius, M., Pasquini, L., Ravnsbaek, D. B., Sofianos, M. V., Udovic, T. J., Vegge, T., Walker, G. S., Webb, C. J., Weidenthaler, C., Zlotea, C.

{Journal of Alloys and Compounds}, 827, Elsevier B.V., Lausanne, Switzerland, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


{Thermal nucleation and high-resolution imaging of submicrometer magnetic bubbles in thin thulium iron garnet films with perpendicular anisotropy}
Thermal nucleation and high-resolution imaging of submicrometer magnetic bubbles in thin thulium iron garnet films with perpendicular anisotropy

Büttner, F., Mawass, M. A., Bauer, J., Rosenberg, E., Caretta, L., Avci, C. O., Gräfe, J., Finizio, S., Vaz, C. A. F., Novakovic, N., Weigand, M., Litzius, K., Förster, J., Träger, N., Groß, F., Suzuki, D., Huang, M., Bartell, J., Kronast, F., Raabe, J., Schütz, G., Ross, C. A., Beach, G. S. D.

{Physical Review Materials}, 4(1), American Physical Society, College Park, MD, 2020 (article)

Abstract
Ferrimagnetic iron garnets are promising materials for spintronics applications, characterized by ultralow damping and zero current shunting. It has recently been found that few nm-thick garnet films interfaced with a heavy metal can also exhibit sizable interfacial spin-orbit interactions, leading to the emergence, and efficient electrical control, of one-dimensional chiral domain walls. Two-dimensional bubbles, by contrast, have so far only been confirmed in micrometer-thick films. Here, we show by high resolution scanning transmission x-ray microscopy and photoemission electron microscopy that submicrometer bubbles can be nucleated and stabilized in ∼25-nm-thick thulium iron garnet films via short heat pulses generated by electric current in an adjacent Pt strip, or by ultrafast laser illumination. We also find that quasistatic processes do not lead to the formation of a bubble state, suggesting that the thermodynamic path to reaching that state requires transient dynamics. X-ray imaging reveals that the bubbles have Bloch-type walls with random chirality and topology, indicating negligible chiral interactions at the garnet film thickness studied here. The robustness of thermal nucleation and the feasibility demonstrated here to image garnet-based devices by x-rays both in transmission geometry and with sensitivity to the domain wall chirality are critical steps to enabling the study of small spin textures and dynamics in perpendicularly magnetized thin-film garnets.

mms

DOI [BibTex]

DOI [BibTex]


{Real-space imaging of confined magnetic skyrmion tubes}
Real-space imaging of confined magnetic skyrmion tubes

Birch, M. T., Cortés-Ortuño, D., Turnbull, L. A., Wilson, M. N., Groß, F., Träger, N., Laurenson, A., Bukin, N., Moody, S. H., Weigand, M., Schütz, G., Popescu, H., Fan, R., Steadman, P., Verezhak, J. A. T., Balakrishnan, G., Loudon, J. C., Twitchett-Harrison, A. C., Hovorka, O., Fangohr, H., Ogrin, F., Gräfe, J., Hatton, P. D.

Nature Communications, 11, pages: 1726, 2020 (article)

Abstract
Magnetic skyrmions are topologically nontrivial particles with a potential application as information elements in future spintronic device architectures. While they are commonly portrayed as two dimensional objects, in reality magnetic skyrmions are thought to exist as elongated, tube-like objects extending through the thickness of the host material. The study of this skyrmion tube state (SkT) is vital for furthering the understanding of skyrmion formation and dynamics for future applications. However, direct experimental imaging of skyrmion tubes has yet to be reported. Here, we demonstrate the real-space observation of skyrmion tubes in a lamella of FeGe using resonant magnetic x-ray imaging and comparative micromagnetic simulations, confirming their extended structure. The formation of these structures at the edge of the sample highlights the importance of confinement and edge effects in the stabilisation of the SkT state, opening the door to further investigation into this unexplored dimension of the skyrmion spin texture.

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Introducing Progress in Biomedical Engineering; Issue 2 Vol 2

Sitti, M.

Progress in Biomedical Engineering, IOP Publishing, 2020 (article)

pi

[BibTex]

[BibTex]


Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.

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pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]


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Divide-and-Conquer Monte Carlo Tree Search for goal directed planning

Parascandolo*, G., Buesing*, L., Merel, J., Hasenclever, L., Aslanides, J., Hamrick, J. B., Heess, N., Neitz, A., Weber, T.

2020, *equal contribution (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


A machine learning route between band mapping and band structure
A machine learning route between band mapping and band structure

Xian*, R. P., Stimper*, V., Zacharias, M., Dong, S., Dendzik, M., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R.

2020, *equal contribution (misc)

ei

arXiv [BibTex]

arXiv [BibTex]


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Energy storage in steady states under cyclic local energy input

Zhang, Y., Holyst, R., Maciolek, A.

Physical Review E, 101(1), American Physical Society, Melville, NY, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


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Current-induced dynamical tilting of chiral domain walls in curved microwires

Finizio, S., Wintz, S., Mayr, S., Huxtable, A. J., Langer, M., Bailey, J., Burnell, G., Marrows, C. H., Raabe, J.

Applied Physics Letters, 116(18), American Institute of Physics, Melville, NY, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Numerical simulations of self-diffusiophoretic colloids at fluid interfaces

Peter, T., Malgaretti, P., Rivas, N., Scagliarini, A., Harting, J., Dietrich, S.

Soft Matter, 16(14):3536-3547, Royal Society of Chemistry, Cambridge, UK, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


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Highly effective hydrogen isotope separation through dihydrogen bond on Cu(I)-exchanged zeolites well above liquid nitrogen temperature

Xiong, R., Zhang, L., Li, P., Luo, W., Tang, T., Ao, B., Sang, G., Chen, C., Yan, X., Chen, J., Hirscher, M.

Chemical Engineering Journal, 391, Elsevier, Lausanne, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, Foundations and Trends in Computer Graphics and Vision, 2020 (book)

Abstract
Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.

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pdf Project Page link Project Page [BibTex]


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DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

Wang, R., Yang, N., Stückler, J., Cremers, D.

In Proceedings of the IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings)

ev

[BibTex]

[BibTex]


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Learning to Adapt Multi-View Stereo by Self-Supervision

Mallick, A., Stückler, J., Lensch, H.

Proceedings of the British Machine Vision Conference (BMVC), 2020, to appear (conference) To be published

ev

link (url) [BibTex]

link (url) [BibTex]


Learning Implicit Surface Light Fields
Learning Implicit Surface Light Fields

Oechsle, M., Niemeyer, M., Reiser, C., Mescheder, L., Strauss, T., Geiger, A.

In International Conference on 3D Vision (3DV), 2020 (inproceedings)

Abstract
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requires reasoning about the complex interplay of light, geometry and surface properties. In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field. In contrast to existing representations, our implicit model represents surface light fields in a continuous fashion and independent of the geometry. Moreover, we condition the surface light field with respect to the location and color of a small light source. Compared to traditional surface light field models, this allows us to manipulate the light source and relight the object using environment maps. We further demonstrate the capabilities of our model to predict the visual appearance of an unseen object from a single real RGB image and corresponding 3D shape information. As evidenced by our experiments, our model is able to infer rich visual appearance including shadows and specular reflections. Finally, we show that the proposed representation can be embedded into a variational auto-encoder for generating novel appearances that conform to the specified illumination conditions.

avg

[BibTex]

[BibTex]


Event-triggered Learning for Linear Quadratic Control
Event-triggered Learning for Linear Quadratic Control

Schlüter, H., Solowjow, F., Trimpe, S.

IEEE Transactions on Automatic Control, 2020 (article) Accepted

ics

arXiv [BibTex]

arXiv [BibTex]


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Scaling Guarantees for Nearest Counterfactual Explanations

Mohammadi, K., Karimi, A., Barthe, G., Valera, I.

2020 (misc) Submitted

ei

arXiv [BibTex]


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Ferrimagnetic skyrmions in topological insulator/ferrimagnet heterostructures

Wu, H., Groß, F., Dai, B. Q., Lujan, D., Razavi, S. A., Zhang, P., Liu, Y. X., Sobotkiewich, K., Förster, J., Weigand, M., Schütz, G., Li, X. Q., Gräfe, J., Wang, K. L.

Advanced Materials, 32(34), Wiley-VCH, Weinheim, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Spin-Bahn-Effekte in der Vortexdynamik und kürzeste Spinwellen in Y3Fe5O12

Förster, J.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2020 (phdthesis)

mms

[BibTex]

[BibTex]


Multiwavelength-Steerable Visible-Light-Driven Magnetic CoO--TiO2 Microswimmers
Multiwavelength-Steerable Visible-Light-Driven Magnetic CoO–TiO2 Microswimmers

Sridhar, V., Park, B., Guo, S., van Aken, P. A., Sitti, M.

ACS Applied Materials \& Interfaces, 12(21):24149-24155, 2020 (article)

pi

DOI [BibTex]

DOI [BibTex]


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Suppressing instabilities in defect-scarce nanowires by controlling the energy release rate during incipient plasticity

Shin, J. G., Richter, G., Gianola, D. S.

Materials & Design, 189, 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]


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Rational strain engineering in delafossite oxides for highly efficient hydrogen evolution catalysis in acidic media

Podjaski, F., Weber, D., Zhang, S. Y., Diehl, L., Eger, R., Duppel, V., Alarcon-Llado, E., Richter, G., Haase, F., Morral, A. F. I., Scheu, C., Lotsch, B. V.

Nature Catalysis, 3(1):55-63, 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]


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Enhanced Electrocatalytic Activities toward the Ethanol Oxidation of Nanoporous Gold Prepared via Solid-Phase Reaction

Zhang, A., Chen, Y. Y., Yang, Z. P., Ma, S., Huang, Y., Richter, G., Schutzendube, P., Zhong, C., Wang, Z. M.

Acs Applied Energy Materials, 3(1):336-343, 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]


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High capacity rock salt type Li2MnO3-delta thin film battery electrodes

Muller, H. A., Joshi, Y., Hadjixenophontos, E., Peter, C., Csiszar, G., Richter, G., Schmitz, G.

Rsc Advances, 10(7):3636-3645, 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]


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Room temperature ferromagnetism driven by Ca-doped BiFeO3 multiferroic functional material

Marzouk, M., Hashem, H. M., Soltan, S., Ramadan, A. A.

{Journal of Materials Science: Materials in Electronics}, 31(7):5599-5607, Springer, Norwell, MA, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Tantalum and zirconium induced structural transitions at complex [111] tilt grain boundaries in copper

Meiners, T., Duarte, J. M., Richter, G., Dehm, G., Liebscher, C. H.

Acta Materialia, 190, pages: 93-104, 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]


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Magnetic quantification of single-crystalline Fe and Co nanowires via off-axis electron holography

Chai, K., Li, Z. A., Huang, W. T., Richter, G., Liu, R. B., Zou, B. S., Caron, J., Kovacs, A., Dunin-Borkowski, R. E., Li, J. Q.

Journal of Chemical Physics, 152(11), 2020 (article)

ZWE

DOI [BibTex]

DOI [BibTex]

2019


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Foundations of Comparison-Based Hierarchical Clustering

Ghoshdastidar, D., Perrot, M., von Luxburg, U.

Advances in Neural Information Processing Systems 32 (NIPS 2019), NeurIPS, Neural Information Processing Systems 2019, December 2019 (conference)

slt

link (url) Project Page [BibTex]

2019


link (url) Project Page [BibTex]


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Assessing Aesthetics of Generated Abstract Images Using Correlation Structure

Khajehabdollahi, S., Martius, G., Levina, A.

In Proceedings 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pages: 306-313, IEEE, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), December 2019 (inproceedings)

al

DOI [BibTex]

DOI [BibTex]


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, pages: 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., Neural Information Processing Systems 2019, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Soft-magnetic coatings as possible sensors for magnetic imaging of superconductors
Soft-magnetic coatings as possible sensors for magnetic imaging of superconductors

Ionescu, A., Simmendinger, J., Bihler, M., Miksch, C., Fischer, P., Soltan, S., Schütz, G., Albrecht, J.

Supercond. Sci. and Tech., 33, pages: 015002, IOP, December 2019 (article)

Abstract
Magnetic imaging of superconductors typically requires a soft-magnetic material placed on top of the superconductor to probe local magnetic fields. For reasonable results the influence of the magnet onto the superconductor has to be small. Thin YBCO films with soft-magnetic coatings are investigated using SQUID magnetometry. Detailed measurements of the magnetic moment as a function of temperature, magnetic field and time have been performed for different heterostructures. It is found that the modification of the superconducting transport in these heterostructures strongly depends on the magnetic and structural properties of the soft-magnetic material. This effect is especially pronounced for an inhomogeneous coating consisting of ferromagnetic nanoparticles.

pf mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Towards Geometric Understanding of Motion
Towards Geometric Understanding of Motion

Ranjan, A.

University of Tübingen, December 2019 (phdthesis)

Abstract

The motion of the world is inherently dependent on the spatial structure of the world and its geometry. Therefore, classical optical flow methods try to model this geometry to solve for the motion. However, recent deep learning methods take a completely different approach. They try to predict optical flow by learning from labelled data. Although deep networks have shown state-of-the-art performance on classification problems in computer vision, they have not been as effective in solving optical flow. The key reason is that deep learning methods do not explicitly model the structure of the world in a neural network, and instead expect the network to learn about the structure from data. We hypothesize that it is difficult for a network to learn about motion without any constraint on the structure of the world. Therefore, we explore several approaches to explicitly model the geometry of the world and its spatial structure in deep neural networks.

The spatial structure in images can be captured by representing it at multiple scales. To represent multiple scales of images in deep neural nets, we introduce a Spatial Pyramid Network (SpyNet). Such a network can leverage global information for estimating large motions and local information for estimating small motions. We show that SpyNet significantly improves over previous optical flow networks while also being the smallest and fastest neural network for motion estimation. SPyNet achieves a 97% reduction in model parameters over previous methods and is more accurate.

The spatial structure of the world extends to people and their motion. Humans have a very well-defined structure, and this information is useful in estimating optical flow for humans. To leverage this information, we create a synthetic dataset for human optical flow using a statistical human body model and motion capture sequences. We use this dataset to train deep networks and see significant improvement in the ability of the networks to estimate human optical flow.

The structure and geometry of the world affects the motion. Therefore, learning about the structure of the scene together with the motion can benefit both problems. To facilitate this, we introduce Competitive Collaboration, where several neural networks are constrained by geometry and can jointly learn about structure and motion in the scene without any labels. To this end, we show that jointly learning single view depth prediction, camera motion, optical flow and motion segmentation using Competitive Collaboration achieves state-of-the-art results among unsupervised approaches.

Our findings provide support for our hypothesis that explicit constraints on structure and geometry of the world lead to better methods for motion estimation.

ps

PhD Thesis [BibTex]

PhD Thesis [BibTex]


HPLC of monolayer-protected Gold clusters with baseline separation
HPLC of monolayer-protected Gold clusters with baseline separation

Knoppe, S., Vogt, P.

Analytical Chemistry, 91, pages: 1603, December 2019 (article)

Abstract
The properties of ultrasmall metal nanoparticles (ca. 10–200 metal atoms), or monolayer-protected metal clusters (MPCs), drastically depend on their atomic structure. For systematic characterization and application, assessment of their purity is of high importance. Currently, the gold standard for purity control of MPCs is mass spectrometry (MS). Mass spectrometry, however, cannot always detect small impurities; MS of certain clusters, for example, ESI-TOF of Au40(SR)24, is not successful at all. We here present a simple reversed-phase HPLC method for purity control of a series of small alkanethiolate-protected gold clusters. The method allows the detection of small impurities with high sensitivity. Linear correlation between alkyl chain length of Au25(SC_n H_(2n+1))18 clusters (n = 6, 8, 10, 12) and their retention time was noticed.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Semi-supervised learning, causality, and the conditional cluster assumption
Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Advances in Neural Information Processing Systems 32, Curran Associates, Inc., Neural Information Processing Systems 2019 - Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (conference)

ei

Poster PDF link (url) [BibTex]

Poster PDF link (url) [BibTex]