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2019


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X-ray Optics Fabrication Using Unorthodox Approaches

Sanli, U., Baluktsian, M., Ceylan, H., Sitti, M., Weigand, M., Schütz, G., Keskinbora, K.

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

mms pi

[BibTex]

2019


[BibTex]


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Nanoscale detection of spin wave deflection angles in permalloy

Gross, F., Träger, N., Förster, J., Weigand, M., Schütz, G., Gräfe, J.

{Applied Physics Letters}, 114(1), American Institute of Physics, Melville, NY, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Coherent excitation of heterosymmetric spin waves with ultrashort wavelengths

Dieterle, G., Förster, J., Stoll, H., Semisalova, A. S., Finizio, S., Gangwar, A., Weigand, M., Noske, M., Fähnle, M., Bykova, I., Gräfe, J., Bozhko, D. A., Musiienko-Shmarova, H. Y., Tiberkevich, V., Slavin, A. N., Back, C. H., Raabe, J., Schütz, G., Wintz, S.

{Physical Review Letters}, 122(11), American Physical Society, Woodbury, N.Y., 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Reprogrammability and Scalability of Magnonic Fibonacci Quasicrystals

Lisiecki, F., Rychły, J., Kuświk, P., Głowiński, H., Kłos, J. W., Groß, F., Bykova, I., Weigand, M., Zelent, M., Goering, E. J., Schütz, G., Gubbiotti, G., Krawczyk, M., Stobiecki, F., Dubowik, J., Gräfe, J.

Physical Review Applied, 11, pages: 054003, 2019 (article)

Abstract
Magnonic crystals are systems that can be used to design and tune the dynamic properties of magnetization. Here, we focus on one-dimensional Fibonacci magnonic quasicrystals. We confirm the existence of collective spin waves propagating through the structure as well as dispersionless modes; the reprogammability of the resonance frequencies, dependent on the magnetization order; and dynamic spin-wave interactions. With the fundamental understanding of these properties, we lay a foundation for the scalable and advanced design of spin-wave band structures for spintronic, microwave, and magnonic applications.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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)

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DOI [BibTex]


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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]


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Probabilistic Linear Solvers: A Unifying View

Bartels, S., Cockayne, J., Ipsen, I. C. F., Hennig, P.

Statistics and Computing, 2019 (article) Accepted

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link (url) [BibTex]

link (url) [BibTex]


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Artifacts from manganese reduction in rock samples prepared by focused ion beam (FIB) slicing for X-ray microspectroscopy

Macholdt, D. S., Förster, J., Müller, M., Weber, B., Kappl, M., Kilcoyne, A. L. D., Weigand, M., Leitner, J., Jochum, K. P., Pöhlker, C., Andreae, M. O.

{Geoscientific instrumentation, methods and data systems}, 8(1):97-111, Copernicus Publ., Göttingen, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Mixed-state magnetotransport properties of MgB2 thin film prepared by pulsed laser deposition on an Al2O3 substrate

Alzayed, N. S., Shahabuddin, M., Ramey, S. M., Soltan, S.

{Journal of Materials Science: Materials in Electronics}, 30(2):1547-1552, Springer, Norwell, MA, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Comparison of theories of fast and ultrafast magnetization dynamics

Fähnle, M.

{Journal of Magnetism and Magnetic Materials}, 469, pages: 28-29, NH, Elsevier, Amsterdam, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Concepts for improving hydrogen storage in nanoporous materials

Broom, D. P., Webb, C. J., Fanourgakis, G. S., Froudakis, G. E., Trikalitis, P. N., Hirscher, M.

{International Journal of Hydrogen Energy}, 44(15):7768-7779, Elsevier, Amsterdam, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Controlling dislocation nucleation-mediatd plasticity in nanostructures via surface modification

Shin, J., Chen, L. Y., Sanli, U. T., Richter, G., Labat, S., Richard, M., Cornelius, T., Thomas, O., Gianola, D. S.

{Acta Materialia}, 166, pages: 572-586, Elsevier Science, Kidlington, 2019 (article)

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DOI [BibTex]

DOI [BibTex]

2018


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Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective

Tronarp, F., Kersting, H., Särkkä, S., Hennig, P.

ArXiv preprint 2018, arXiv:1810.03440 [stat.ME], October 2018 (article)

Abstract
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to consists of the observations of the difference between the derivative of the GP and the vector field evaluated at the GP---which are all identically zero at the solution of the ODE. When the GP has a state-space representation, the problem can be reduced to a Bayesian state estimation problem and all widely-used approximations to the Bayesian filtering and smoothing problems become applicable. Furthermore, all previous GP-based ODE solvers, which were formulated in terms of generating synthetic measurements of the vector field, come out as specific approximations. We derive novel solvers, both Gaussian and non-Gaussian, from the Bayesian state estimation problem posed in this paper and compare them with other probabilistic solvers in illustrative experiments.

pn

link (url) Project Page [BibTex]

2018



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Convergence Rates of Gaussian ODE Filters

Kersting, H., Sullivan, T. J., Hennig, P.

arXiv preprint 2018, arXiv:1807.09737 [math.NA], July 2018 (article)

Abstract
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution $x$ and its first $q$ derivatives a priori as a Gauss--Markov process $\boldsymbol{X}$, which is then iteratively conditioned on information about $\dot{x}$. We prove worst-case local convergence rates of order $h^{q+1}$ for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order $h^q$ in the case of $q=1$ and an integrated Brownian motion prior, and analyse how inaccurate information on $\dot{x}$ coming from approximate evaluations of $f$ affects these rates. Moreover, we present explicit formulas for the steady states and show that the posterior confidence intervals are well calibrated in all considered cases that exhibit global convergence---in the sense that they globally contract at the same rate as the truncation error.

pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

Kanagawa, M., Hennig, P., Sejdinovic, D., Sriperumbudur, B. K.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other. It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them. Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches.

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arXiv [BibTex]

arXiv [BibTex]


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Transmission x-ray microscopy at low temperatures: Irregular supercurrent flow at small length scales

Simmendinger, J., Ruoss, S., Stahl, C., Weigand, M., Gräfe, J., Schütz, G., Albrecht, J.

{Physical Review B}, 97(13), American Physical Society, Woodbury, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

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arXiv [BibTex]

arXiv [BibTex]


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Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models

Nishiyama, Y., Kanagawa, M., Gretton, A., Fukumizu, K.

Arxiv e-prints, arXiv:1409.5178v2 [stat.ML], 2018 (article)

Abstract
Kernel Bayesian inference is a powerful nonparametric approach to performing Bayesian inference in reproducing kernel Hilbert spaces or feature spaces. In this approach, kernel means are estimated instead of probability distributions, and these estimates can be used for subsequent probabilistic operations (as for inference in graphical models) or in computing the expectations of smooth functions, for instance. Various algorithms for kernel Bayesian inference have been obtained by combining basic rules such as the kernel sum rule (KSR), kernel chain rule, kernel product rule and kernel Bayes' rule. However, the current framework only deals with fully nonparametric inference (i.e., all conditional relations are learned nonparametrically), and it does not allow for flexible combinations of nonparametric and parametric inference, which are practically important. Our contribution is in providing a novel technique to realize such combinations. We introduce a new KSR referred to as the model-based KSR (Mb-KSR), which employs the sum rule in feature spaces under a parametric setting. Incorporating the Mb-KSR into existing kernel Bayesian framework provides a richer framework for hybrid (nonparametric and parametric) kernel Bayesian inference. As a practical application, we propose a novel filtering algorithm for state space models based on the Mb-KSR, which combines the nonparametric learning of an observation process using kernel mean embedding and the additive Gaussian noise model for a state transition process. While we focus on additive Gaussian noise models in this study, the idea can be extended to other noise models, such as the Cauchy and alpha-stable noise models.

pn

arXiv [BibTex]

arXiv [BibTex]


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A probabilistic model for the numerical solution of initial value problems

Schober, M., Särkkä, S., Philipp Hennig,

Statistics and Computing, Springer US, 2018 (article)

Abstract
We study connections between ordinary differential equation (ODE) solvers and probabilistic regression methods in statistics. We provide a new view of probabilistic ODE solvers as active inference agents operating on stochastic differential equation models that estimate the unknown initial value problem (IVP) solution from approximate observations of the solution derivative, as provided by the ODE dynamics. Adding to this picture, we show that several multistep methods of Nordsieck form can be recast as Kalman filtering on q-times integrated Wiener processes. Doing so provides a family of IVP solvers that return a Gaussian posterior measure, rather than a point estimate. We show that some such methods have low computational overhead, nontrivial convergence order, and that the posterior has a calibrated concentration rate. Additionally, we suggest a step size adaptation algorithm which completes the proposed method to a practically useful implementation, which we experimentally evaluate using a representative set of standard codes in the DETEST benchmark set.

pn

PDF Code DOI Project Page [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)

mms pi

[BibTex]

[BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

link (url) Project Page [BibTex]

link (url) 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)

mms pi

DOI [BibTex]

DOI [BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

[BibTex]

[BibTex]


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Thick permalloy films for the imaging of spin texture dynamics in perpendicularly magnetized systems

Finizio, S., Wintz, S., Bracher, D., Kirk, E., Semisalova, A. S., Förster, J., Zeissler, K., We\ssels, T., Weigand, M., Lenz, K., Kleibert, A., Raabe, J.

{Physical Review B}, 98(10), American Physical Society, Woodbury, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Dynamic Janus metasurfaces in the visible spectral region

Yu, P., Li, J., Zhang, S., Jin, Z., Schütz, G., Qiu, C., Hirscher, M., Liu, N.

{Nano Letters}, 18(7):4584-4589, American Chemical Society, Washington, DC, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Review of ultrafast demagnetization after femtosecond laser pulses: A complex interaction of light with quantum matter

Fähnle, M., Haag, M., Illg, C., Müller, B. Y., Weng, W., Tsatsoulis, T., Huang, H., Briones Paz, J. Z., Teeny, N., Zhang, L., Kuhn, T.

{American Journal of Modern Physics}, 7(2):68-74, Science Publishing Group, New York, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Direct observation of Zhang-Li torque expansion of magnetic droplet solitons

Chung, S., Tuan Le, Q., Ahlberg, M., Awad, A. A., Weigand, M., Bykova, I., Khymyn, R., Dvornik, M., Mazraati, H., Houshang, A., Jiang, S., Nguyen, T. N. A., Goering, E., Schütz, G., Gräfe, J., \AAkerman, J.

{Physical Review Letters}, 120(21), American Physical Society, Woodbury, N.Y., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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XMCD investigations on new hard magnetic systems

Chen, Y.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Current-induced skyrmion generation through morphological thermal transitions in chiral ferromagnetic heterostructures

Lemesh, I., Litzius, K., Böttcher, M., Bassirian, P., Kerber, N., Heinze, D., Zázvorka, J., Büttner, F., Caretta, L., Mann, M., Weigand, M., Finizio, S., Raabe, J., Im, M., Stoll, H., Schütz, G., Dupé, B., Kläui, M., Beach, G. S. D.

{Advanced Materials}, 30(49), Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Assessment methodology of promising porous materials for near ambient temperature hydrogen storage applications

Minuto, F. D., Balderas-Xicohténcatl, R., Policicchio, A., Hirscher, M., Agostino, R. G.

{International Journal of Hydrogen Energy}, 43(31):14550-14556, Elsevier, Amsterdam, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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3d nanofabrication of high-resolution multilayer Fresnel zone plates

Sanli, U. T., Jiao, C., Baluktsian, M., Grévent, C., Hahn, K., Wang, Y., Srot, V., Richter, G., Bykova, I., Weigand, M., Schütz, G., Keskinbora, K.

{Advanced Science}, 5(9), Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Photocatalytic CO2 reduction by Cr-substituted Ba2(In2-xCrx)O5\mbox⋅(H2O)δ(0.04 ≤x ≤0.60)

Yoon, S., Gaul, M., Sharma, S., Son, K., Hagemann, H., Ziegenbalg, D., Schwingenschlogl, U., Widenmeyer, M., Weidenkaff, A.

{Solid State Sciences}, 78, pages: 22-29, Elsevier Masson SAS, Paris, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Correction of axial position uncertainty and systematic detector errors in ptychographic diffraction imaging

Loetgering, L., Rose, M., Keskinbora, K., Baluktsian, M., Dogan, G., Sanli, U., Bykova, I., Weigand, M., Schütz, G., Wilhein, T.

{Optical Engineering}, 57(8), The Society, Redondo Beach, Calif., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Thermodynamics, kinetics and selectivity of H2 and D2 on zeolite 5A below 77K

Xiong, R., Balderas-Xicohténcatl, R., Zhang, L., Li, P., Yao, Y., Sang, G., Chen, C., Tang, T., Luo, D., Hirscher, M.

{Microporous and Mesoporous Materials}, 264, pages: 22-27, Elsevier, Amsterdam, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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The role of surface oxides on hydrogen sorption kinetics in titanium thin films

Hadjixenophontos, E., Michalek, L., Roussel, M., Hirscher, M., Schmitz, G.

{Applied Surface Science}, 441, pages: 324-330, Elsevier B.V., Amsterdam, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Volumetric hydrogen storage capacity in metal-organic frameworks

Balderas-Xicohténcatl, R., Schlichtenmayer, M., Hirscher, M.

{Energy Technology}, 6(3):578-582, Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Ferromagnetism in nitrogen and fluorine substituted BaTiO3

Yoon, S., Son, K., Ebbinghaus, S. G., Widenmeyer, M., Weidenkaff, A.

{Journal of Alloys and Compounds}, 749, pages: 628-633, Elsevier B.V., Lausanne, Switzerland, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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New concepts for 3d optics in x-ray microscopy

Sanli, U., Ceylan, H., Jiao, C., Baluktsian, M., Grevent, C., Hahn, K., Wang, Y., Srot, V., Richter, G., Bykova, I., Weigand, M., Sitti, M., Schütz, G., Keskinbora, K.

{Microscopy and Microanalysis}, 24(Suppl 2):288-289, Cambridge University Press, New York, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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High volumetric hydrogen storage capacity using interpenetrated metal-organic frameworks

Balderas-Xicohténcatl, R., Schmieder, P., Denysenko, D., Volkmer, D., Hirscher, M.

{Energy Technology}, 6(3):510-512, Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Spin-wave interference in magnetic vortex stacks

Behncke, C., Adolff, C. F., Lenzing, N., Hänze, M., Schulte, B., Weigand, M., Schütz, G., Meier, G.

{Communications Physics}, 1, Nature Publishing Group, London, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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High-throughput synthesis of modified Fresnel zone plate arrays via ion beam lithography

Keskinbora, K., Sanli, U. T., Baluktsian, M., Grévent, C., Weigand, M., Schütz, G.

{Beilstein Journal of Nanotechnology}, 9, pages: 2049-2056, Beilstein-Institut, Frankfurt am Main, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Deterministic creation and deletion of a single magnetic skyrmion observed by direct time-resolved X-ray microscopy

Woo, S., Song, K. M., Zhang, X., Ezawa, M., Zhou, Y., Liu, X., Weigand, M., Finizio, S., Raabe, J., Park, M.-C., Lee, K.-Y., Choi, J. W., Min, B.-C., Koo, H. C., Chang, J.

{Nature Electronics}, 1(5):288-296, Springer Nature, London, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic skyrmion as a nonlinear resistive element: A potential building block for reservoir computing

Prychynenko, D., Sitte, M., Litzius, K., Krüger, B., Bourianoff, G., Kläui, M., Sinova, J., Everschor-Sitte, K.

{Physical Review Applied}, 9(1), American Physical Society, College Park, Md. [u.a.], 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Tunable geometrical frustration in magnoic vortex crystals

Behncke, C., Adolff, C. F., Wintz, S., Hänze, M., Schulte, B., Weigand, M., Finizio, S., Raabe, J., Meier, G.

{Scientific Reports}, 8, Nature Publishing Group, London, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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High-Resolution X-ray Ptychography for Magnetic Imaging

Bykova, I.

Universität Stuttgart, Stuttgart, 2018 (phdthesis)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2017


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

Journal of Machine Learning Research, 18(119):1-59, November 2017 (article)

pn

link (url) Project Page [BibTex]

2017


link (url) Project Page [BibTex]


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Early Stopping Without a Validation Set

Mahsereci, M., Balles, L., Lassner, C., Hennig, P.

arXiv preprint arXiv:1703.09580, 2017 (article)

Abstract
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel early stopping criterion which is based on fast-to-compute, local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression as well as neural networks.

ps pn

link (url) Project Page Project Page [BibTex]