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2019


Shape-encoded dynamic assembly of mobile micromachines
Shape-encoded dynamic assembly of mobile micromachines

Alapan, Y., Yigit, B., Beker, O., Demirörs, A. F., Sitti, M.

Nature, 18, 2019 (article)

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[BibTex]

2019


[BibTex]


Peptide-Induced Biomineralization of Tin Oxide (SnO2) Nanoparticles for Antibacterial Applications
Peptide-Induced Biomineralization of Tin Oxide (SnO2) Nanoparticles for Antibacterial Applications

Singh, A. V., Jahnke, T., Xiao, Y., Wang, S., Yu, Y., David, H., Richter, G., Laux, P., Luch, A., Srivastava, A., Saxena, P. S., Bill, J., Sitti, M.

Journal of nanoscience and nanotechnology, 19, American Scientific Publishers, 2019 (article)

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[BibTex]

[BibTex]


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Niobium near-surface composition during nitrogen infusion relevant for superconducting radio-frequency cavities

Semione, G. D. L., Dangwal Pandey, A., Tober, S., Pfrommer, J., Poulain, A., Drnec, J., Schütz, G., Keller, T. F., Noei, H., Vonk, V., Foster, B., Stierle, A.

{Physical Review Accelerators and Beams}, 22(10), American Physical Society, Ridge, NY, USA, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Microfluidics Integrated Lithography‐Free Nanophotonic Biosensor for the Detection of Small Molecules
Microfluidics Integrated Lithography‐Free Nanophotonic Biosensor for the Detection of Small Molecules

Sreekanth, K. V., Sreejith, S., Alapan, Y., Sitti, M., Lim, C. T., Singh, R.

Advanced Optical Materials, 2019 (article)

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[BibTex]

[BibTex]


{Vizualizing nanoscale spin waves using MAXYMUS}
Vizualizing nanoscale spin waves using MAXYMUS

Gräfe, J., Weigand, M., Van Waeyenberge, B., Gangwar, A., Groß, F., Lisiecki, F., Rychly, J., Stoll, H., Träger, N., Förster, J., Stobiecki, F., Dubowik, J., Klos, H., Krwaczyk, M., Back, C. H., Goering, E. J., Schütz, G.

{Proceedings of SPIE}, 11090, SPIE, Bellingham, Washington, 2019 (article)

Abstract
Magnonics research, i.e. the manipulation of spin waves for information processing, is a topic of intense research interest in the past years. FMR, BLS and MOKE measurements lead to tremendous success and advancement of the field. However, these methods are limited in their spatial resolution. X-ray microscopy opens up a way to push to spatial resolutions below 100 nm. Here, we discuss the methodology of STXM for pump-probe data acquisition with single photon counting and arbitrary excitation patterns. Furthermore, we showcase these capabilities using two magnonic crystals as examples: an antidot lattice and a Fibonacci quasicrystal.

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DOI [BibTex]

DOI [BibTex]


{Interpreting first-order reversal curves beyond the Preisach model: An experimental permalloy microarray investigation}
Interpreting first-order reversal curves beyond the Preisach model: An experimental permalloy microarray investigation

Groß, F., Ilse, S. E., Schütz, G., Gräfe, J., Goering, E.

{Physical Review B}, 99(6), American Physical Society, Woodbury, NY, 2019 (article)

Abstract
First-order reversal curves (FORCs) are a powerful tool to separate microscopic coercivities and interactions in a system without the need for lateral resolution. However, measured FORC densities are not always straightforward to interpret, especially if the system is interaction dominated and the Preisach-like interpretation of the FORC density breaks down. This is why FORC is often seen as a magnetic fingerprint instead of a measurement method yielding quantitative information. To understand additional features arising from the interactions in the system, we purposely designed permalloy microstructures which violate the Mayergoyz criteria. These artificial systems allow us to isolate the origin of an additional interaction peak in the FORC density. Modeling the system as a superposition of dipoles allows us to extract interaction strength parameters from this static simulation. Additionally, we suggest a linear relation between integrated interaction peak volume and interaction strength within the system. The presented correlation could be used to investigate the interaction behavior of samples as a function of structural parameters within a series of FORC measurements. This is an important step towards a more quantitative understanding of FORCs which violate the Mayergoyz criteria and away from a fingerprint interpretation.

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DOI [BibTex]


Bistability of magnetic states in Fe-Pd nanocap arrays
Bistability of magnetic states in Fe-Pd nanocap arrays

Aravind, P. B., Heigl, M., Fix, M., Groß, F., Gräfe, J., Mary, A., Rajgowrav, C. R., Krupiński, M., Marszałek, M., Thomas, S., Anantharaman, M. R., Albrecht, M.

Nanotechnology, 30, pages: 405705, 2019 (article)

Abstract
Magnetic bistability between vortex and single domain states in nanostructures are of great interest from both fundamental and technological perspectives. In soft magnetic nanostructures, the transition from a uniform collinear magnetic state to a vortex state (or vice versa) induced by a magnetic field involves an energy barrier. If the thermal energy is large enough for overcoming this energy barrier, magnetic bistability with a hysteresis-free switching occurs between the two magnetic states. In this work, we tune this energy barrier by tailoring the composition of FePd alloys, which were deposited onto self-assembled particle arrays forming magnetic vortex structures on top of the particles. The bifurcation temperature, where a hysteresis-free transition occurs, was extracted from the temperature dependence of the annihilation and nucleation field which increases almost linearly with Fe content of the magnetic alloy. This study provides insights into the magnetization reversal process associated with magnetic bistability, which allows adjusting the bifurcation temperature range by the material properties of the nanosystem.

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link (url) [BibTex]

link (url) [BibTex]


Anatomy of Skyrmionic Textures in Magnetic Multilayers
Anatomy of Skyrmionic Textures in Magnetic Multilayers

Li, W., Bykova, I., Zhang, S., Yu, G., Tomasello, R., Carpentieri, M., Liu, Y., Guang, Y., Gräfe, J., Weigand, M., Burn, D. M., Laan, G. V. D., Hesjedal, T., Yan, Z., Feng, J., Wan, C., Wei, J., Wang, X., Zhang, X., Xu, H., Guo, C., Wei, H., Finocchio, G., Han, X., Schütz, G.

Advanced Materials, 31, pages: 1807683, 2019 (article)

Abstract
Room temperature magnetic skyrmions in magnetic multilayers are considered as information carriers for future spintronic applications. Currently, a detailed understanding of the skyrmion stabilization mechanisms is still lacking in these systems. To gain more insight, it is first and foremost essential to determine the full real‐space spin configuration. Here, two advanced X‐ray techniques are applied, based on magnetic circular dichroism, to investigate the spin textures of skyrmions in [Ta/CoFeB/MgO]n multilayers. First, by using ptychography, a high‐resolution diffraction imaging technique, the 2D out‐of‐plane spin profile of skyrmions with a spatial resolution of 10 nm is determined. Second, by performing circular dichroism in resonant elastic X‐ray scattering, it is demonstrated that the chirality of the magnetic structure undergoes a depth‐dependent evolution. This suggests that the skyrmion structure is a complex 3D structure rather than an identical planar texture throughout the layer stack. The analyses of the spin textures confirm the theoretical predictions that the dipole–dipole interactions together with the external magnetic field play an important role in stabilizing sub‐100 nm diameter skyrmions and the hybrid structure of the skyrmion domain wall. This combined X‐ray‐based approach opens the door for in‐depth studies of magnetic skyrmion systems, which allows for precise engineering of optimized skyrmion heterostructures.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Comment on "Which interactions dominate in active colloids?" [J. Chem. Phys. 150, 061102 (2019)]

Popescu, M. N., Dominguez, A., Uspal, W. E., Tasinkevych, M., Dietrich, S.

The Journal of Chemical Physics, 151(6), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Current-mediated synchronization of a pair of beating non-identical flagella

Dotsenko, V., Maciolek, A., Oshanin, G., Vasilyev, O., Dietrich, S.

New Journal of Physics, 21, IOP Publishing, Bristol, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Driving an electrolyte through a corrugated nanopore

Malgaretti, P., Janssen, M., Pagonabarraga, I., Rubi, J. M.

The Journal of Chemical Physics, 151(8), American Institute of Physics, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Interfaces in fluids of ionic liquid crystals

Bartsch, H.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Spectral Content of a Single Non-Brownian Trajectory

Krapf, D., Lukat, N., Marinari, E., Metzler, R., Oshanin, G., Selhuber-Unkel, C., Squarcini, A., Stadler, L., Weiss, M., Xu, X.

Physical Review X, 9(1), American Physical Society, New York, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Special issue on transport in narrow channels
Journal of Physics: Condensed Matter, 31, IOP Publishing, Bristol, 2019 (misc)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Curvature affects electrolyte relaxation: Studies of spherical and cylindrical electrodes

Janssen, M.

Physical Review E, 100(4), American Physical Society, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Mobile microrobots for active therapeutic delivery
Mobile microrobots for active therapeutic delivery

Erkoc, P., Yasa, I. C., Ceylan, H., Yasa, O., Alapan, Y., Sitti, M.

Advanced Therapeutics, Wiley Online Library, 2019 (article)

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DOI [BibTex]


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Geometric Image Synthesis

Abu Alhaija, H., Mustikovela, S. K., Geiger, A., Rother, C.

Computer Vision – ACCV 2018, 11366, pages: 85-100, Lecture Notes in Computer Science, (Editors: Jawahar, C. and Li, H. and Mori, G. and Schindler, K. ), Asian Conference on Computer Vision, 2019 (conference)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Cutting the Cord: Progress in Untethered Soft Robotics and Actuators
Cutting the Cord: Progress in Untethered Soft Robotics and Actuators

Li, M., Ostrovsky-Snider, N. A., Sitti, M., Omenetto, F. G.

MRS Advances, 4, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

Park, S., Min, Y., Ha, J., Cho, D., Choi, H.

IEEE Access, 7, pages: 163589-163603, IEEE, New York, NY, 2019 (article)

DOI [BibTex]

DOI [BibTex]


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Feature extraction from the Hermitian manifold for Brain-Computer Interfaces

Xu, J., Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

9th International IEEE/EMBS Conference on Neural Engineering (NER), pages: 965-968, IEEE, 2019 (conference) In press

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DOI [BibTex]

DOI [BibTex]


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Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., Helmstaedter, M.

Science, 366(6469):eaay3134, American Association for the Advancement of Science, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J.

Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Birch tar production does not prove Neanderthal behavioral complexity

Schmidt, P., Blessing, M., Rageot, M., Iovita, R., Pfleging, J., Nickel, K. G., Righetti, L., Tennie, C.

Proceedings of the National Academy of Sciences (PNAS), 116(36):17707-17711, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Brainglance: Visualizing Group Level MRI Data at One Glance

Stelzer, J., Lacosse, E., Bause, J., Scheffler, K., Lohmann, G.

Frontiers in Neuroscience, 13(972), 2019 (article)

ei

DOI [BibTex]

DOI [BibTex]


Micro-nanorobots: Important considerations when developing novel drug delivery platforms
Micro-nanorobots: Important considerations when developing novel drug delivery platforms

Singh, A. V., Ansari, M. H. D., Laux, P., Luch, A.

Expert Opinion on Drug Delivery, 16, Taylor & Francis, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Strain-Visualization with Ultrasensitive Nanoscale Crack-Based Sensor Assembled with Hierarchical Thermochromic Membrane
Strain-Visualization with Ultrasensitive Nanoscale Crack-Based Sensor Assembled with Hierarchical Thermochromic Membrane

Park, B., Kim, J. U., Kim, J., Tahk, D., Jeong, C., Ok, J., Shin, J. H., Kang, D., Kim, T.

Advanced Functional Materials, 29, Wiley Online Library, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


A force-measuring and behaviour-recording system consisting of 24 individual 3D force plates for the study of single limb forces in climbing animals on a quasi-cylindrical tower
A force-measuring and behaviour-recording system consisting of 24 individual 3D force plates for the study of single limb forces in climbing animals on a quasi-cylindrical tower

Ji, A., Yuan, S., Endlein, T., Hill, I. D., Wang, W., Wang, H., Jiang, N., Zhao, Z., Barnes, W. J. P., Dai, Z.

Bioinspiration \& biomimetics, IOP Publishing, 2019 (article)

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[BibTex]

[BibTex]


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Remediating Cognitive Decline with Cognitive Tutors

Das, P., Callaway, F., Griffiths, T. L., Lieder, F.

RLDM 2019, 2019 (conference)

Abstract
As people age, their cognitive abilities tend to deteriorate, including their ability to make complex plans. To remediate this cognitive decline, many commercial brain training programs target basic cognitive capacities, such as working memory. We have recently developed an alternative approach: intelligent tutors that teach people cognitive strategies for making the best possible use of their limited cognitive resources. Here, we apply this approach to improve older adults' planning skills. In a process-tracing experiment we found that the decline in planning performance may be partly because older adults use less effective planning strategies. We also found that, with practice, both older and younger adults learned more effective planning strategies from experience. But despite these gains there was still room for improvement-especially for older people. In a second experiment, we let older and younger adults train their planning skills with an intelligent cognitive tutor that teaches optimal planning strategies via metacognitive feedback. We found that practicing planning with this intelligent tutor allowed older adults to catch up to their younger counterparts. These findings suggest that intelligent tutors that teach clever cognitive strategies can help aging decision-makers stay sharp.

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DOI [BibTex]

DOI [BibTex]


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The route to supercurrent transparent ferromagnetic barriers in superconducting matrix

Ivanov, Y. P., Soltan, S., Albrecht, J., Goering, E., Schütz, G., Zhang, Z., Chuvilin, A.

{ACS Nano}, 13(5):5655-5661, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Systematic experimental study on quantum sieving of hydrogen isotopes in metal-amide-imidazolate frameworks with narrow 1-D channels

Mondal, S. S., Kreuzer, A., Behrens, K., Schütz, G., Holdt, H., Hirscher, M.

{ChemPhysChem}, 20(10):1311-1315, Wiley-VCH, Weinheim, Germany, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Learning to Navigate Endoscopic Capsule Robots
Learning to Navigate Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H. B., Mahmood, F., Durr, N. J., Araujo, H., Sarı, A. E., Ajay, A., Sitti, M.

IEEE Robotics and Automation Letters, 4, 2019 (article)

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[BibTex]

[BibTex]


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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Exploiting dynamic opening of apertures in a partially fluorinated MOF for enhancing H2 desorption temperature and isotope separation

Zhang, L., Jee, S., Park, J., Jung, M., Wallcher, D., Franz, A., Lee, W., Yoon, M., Choi, K., Hirscher, M., Oh, H.

{Journal of the American Chemical Society}, 141(50):19850-19858, American Chemical Society, Washington, DC, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Order and Information in the Phases of a Torque-driven Artificial Collective System
Order and Information in the Phases of a Torque-driven Artificial Collective System

Wang, W., Gardi, G., Kishore, V., Koens, L., Son, D., Gilbert, H., Harwani, P., Lauga, E., Sitti, M.

arXiv preprint arXiv:1910.11226, 2019 (article)

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[BibTex]

[BibTex]


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Speeding up the extended Kalman filter approach for denoising XMCD movies of fast magnetization dynamics

Fähnle, M., Schütz, G.

{Ultramicroscopy}, 206, North-Holland, Amsterdam, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Electromechanical actuation of dielectric liquid crystal elastomers for soft robotics

Davidson, Z., Shahsavan, H., Guo, Y., Hines, L., Xia, Y., Yang, S., Sitti, M.

Bulletin of the American Physical Society, APS, 2019 (article)

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[BibTex]

[BibTex]


Occupancy Networks: Learning 3D Reconstruction in Function Space
Occupancy Networks: Learning 3D Reconstruction in Function Space

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

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, 2019 (inproceedings)

Abstract
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.

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Code Video pdf suppmat Project Page blog [BibTex]

Code Video pdf suppmat Project Page blog [BibTex]


Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Ghosh, P., Losalka, A., Black, M. J.

In Proc. AAAI, 2019 (inproceedings)

Abstract
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, ``adversarial samples" and ``fooling samples", have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.

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link (url) Project Page [BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

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[BibTex]

[BibTex]