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2017


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Pattern Generation for Walking on Slippery Terrains

Khadiv, M., Moosavian, S. A. A., Herzog, A., Righetti, L.

In 2017 5th International Conference on Robotics and Mechatronics (ICROM), Iran, August 2017 (inproceedings)

Abstract
In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this for- mulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.

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link (url) [BibTex]

2017


link (url) [BibTex]


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A neutral atom moving in an external magnetic field does not feel a Lorentz force

Fähnle, M.

{American Journal of Modern Physics}, 6(6):153-155, Science Publishing Group, New York, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Temperature-dependent first-order reversal curve measurements on unusually hard magnetic low-temperature phase of MnBi

Muralidhar, S., Gräfe, J., Chen, Y., Etter, M., Gregori, G., Ener, S., Sawatzki, S., Hono, K., Gutfleisch, O., Kronmüller, H., Schütz, G., Goering, E. J.

{Physical Review B}, 95(2), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Smooth and rapid microwave synthesis of MIL-53(Fe) including superparamagnetic \textlessgamma\textgreater-Fe2O3 nanoparticles

Wengert, S., Albrecht, J., Ruoß, S., Stahl, C., Schütz, G., Schäfer, R.

{Journal of Magnetism and Magnetic Materials}, 444, pages: 168-172, NH, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Characterization and differentiation of rock varnish types from different environments by microanalytical techniques

Macholdt, D. S., Jochum, K. P., Pöhlker, C., Arangio, A., Förster, J., Stoll, B., Weis, U., Weber, B., Müller, M., Kappl, M., Shiraiwa, M., Kilcoyne, A. L. D., Weigand, M., Scholz, D., Haug, G. H., Al-Amri, A., Andreae, M. O.

{Chemical Geology}, 459, pages: 91-118, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Skyrmion Hall effect revealed by direct time-resolved X-ray microscopy

Litzius, K., Lemesh, I., Krüger, B., Bassirian, P., Caretta, L., Richter, K., Büttner, F., Sato, K., Tretiakov, O. A., Förster, J., Reeve, R. M., Weigand, M., Bykova, I., Stoll, H., Schütz, G., Beach, G. S. D., Kläui, M.

{Nature Physics}, 13(2):170-175, Nature Pub. Group, London, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Is Growing Good for Learning?

Heim, Steve, Spröwitz, Alexander

In Proceedings of the 8th International Symposium on Adaptive Motion of Animals and Machines AMAM2017, Hokkaido, Japan, 2017 (inproceedings)

[BibTex]

[BibTex]


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When does bounded-optimal metareasoning favor few cognitive systems?

Milli, S., Lieder, F., Griffiths, T. L.

In AAAI Conference on Artificial Intelligence, 31, 2017 (inproceedings)

re

[BibTex]

[BibTex]


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The Structure of Goal Systems Predicts Human Performance

Bourgin, D., Lieder, F., Reichman, D., Talmon, N., Griffiths, T.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

re

[BibTex]

[BibTex]


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Learning to (mis) allocate control: maltransfer can lead to self-control failure

Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., Cohen, J.

In The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making. Ann Arbor, Michigan, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Inspecting cognitive load factors in digital learning settings with ACT-R

Wirzberger, M.

In Dagstuhl 2017. Proceedings of the 11th Joint Workshop of the German Research Training Groups in Computer Science, pages: 62, 2017 (inproceedings)

re

[BibTex]

[BibTex]


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Lernförderliche Gestaltung computerbasierter Instruktionen zur Roboterkonstruktion [Enhancing design of computer-based instructions in a robot construction task]

Esmaeili Bijarsari, S., Wirzberger, M., Rey, G. D.

In INFORMATIK 2017, Lecture Notes in Informatics (LNI), pages: 2279-2286, Gesellschaft für Informatik, Bonn, 2017 (inproceedings)

re

DOI [BibTex]

DOI [BibTex]


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Comment on magnonic black holes

Fähnle, M., Schütz, G.

{Journal of Magnetism and Magnetic Materials}, 444, pages: 146-146, NH, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Cr-Substitution in Ba2In2O5 \mbox⋅ (H2O)x (x \textequals 0.16, 0.74)

Yoon, S., Son, K., Hagemann, H., Widenmeyer, M., Weidenkaff, A.

{Solid State Sciences}, 73, pages: 1-6, Elsevier Masson SAS, Paris, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Exploiting diffusion barrier and chemical affinity of metal-organic frameworks for efficient hydrogen isotope separation

Kim, J. Y., Balderas-Xicohténcatl, R., Zhang, L., Kang, S. G., Hirscher, M., Oh, H., Moon, H. R.

{Journal of the American Chemical Society}, 139(42):15135-15141, American Chemical Society, Washington, DC, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Facile fabrication of mesoporous silica micro-jets with multi-functionalities

Vilela, D., Hortelao, A. C., Balderas-Xicohténcatl, R., Hirscher, M., Hahn, K., Ma, X., Sánchez, S.

{Nanoscale}, 9(37):13990-13997, Royal Society of Chemistry, Cambridge, UK, 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Comment on half-integer quantum numbers for the total angular momentum of photons in light beams with finite lateral extensions

Fähnle, M.

{American Journal of Modern Physics}, 6(5):88-90, Science Publishing Group, New York, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Toward a rational and mechanistic account of mental effort

Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T., Cohen, J., Botvinick, M.

Annual Review of Neuroscience, 40, pages: 99-124, Annual Reviews, 2017 (article)

re

Project Page [BibTex]

Project Page [BibTex]


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An automatic method for discovering rational heuristics for risky choice

Lieder, F., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 2017 (inproceedings)

re

Project Page [BibTex]

Project Page [BibTex]


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Mouselab-MDP: A new paradigm for tracing how people plan

Callaway, F., Lieder, F., Krueger, P. M., Griffiths, T. L.

In The 3rd multidisciplinary conference on reinforcement learning and decision making, 2017 (inproceedings)

re

[BibTex]

[BibTex]


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A dynamic process model for predicting workload in an air traffic controller task

Truschzinski, M., Wirzberger, M.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, pages: 1224-1229, Cognitive Science Society, Austin, TX, 2017 (inproceedings)

re

link (url) [BibTex]

link (url) [BibTex]


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Auswirkung systeminduzierter Delays auf die menschliche Gedächtnisleistung in einem virtuellen agentenbasierten Trainingssetting [Influence of system-induced delays on human memory performance in a virtual agent-based training scenario]

Wirzberger, M., Schmidt, R., Rey, G. D., Hardt, W.

In INFORMATIK 2017, Lecture Notes in Informatics (LNI), pages: 2287-2294, Gesellschaft für Informatik, Bonn, 2017 (inproceedings)

re

DOI [BibTex]

DOI [BibTex]


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Selective hydrogen isotope separation via breathing transition in MIL-53(Al)

Kim, J. Y., Zhang, L., Balderas-Xicohténcatl, R., Park, J., Hirscher, M., Moon, H. R., Oh, H.

{Journal of the American Chemical Society}, 139(49):17743-17746, American Chemical Society, Washington, DC, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Advanced magneto-optical Kerr effect measurements of superconductors at low temperatures

Stahl, C., Gräfe, J., Ruoß, S., Zahn, P., Bayer, J., Simmendinger, J., Schütz, G., Albrecht, J.

{AIP Advances}, 7(10), 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Efficient synthesis for large-scale production and characterization for hydrogen storage of ligand exchanged MOF-74/174/184-M (M\textequalsMg2+, Ni2+)

Oh, H., Maurer, S., Balderas-Xicohténcatl, R., Arnold, L., Magdysyuk, O. V., Schütz, G., Müller, U., Hirscher, M.

{International Journal of Hydrogen Energy}, 42(2):1027-1035, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unifying ultrafast demagnetization and intrinsic Gilbert damping in Co/Ni bilayers with electronic relaxation near the Fermi surface

Zhang, W., He, W., Zhang, X.-Q., Cheng, Z.-H., Teng, J., Fähnle, M.

{Physical Review B}, 96(22), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Influence of the skin barrier on the penetration of topically-applied dexamethasone probed by soft X-ray spectromicroscopy

Yamamoto, K., Klossek, A., Flesch, R., Rancan, F., Weigand, M., Bykova, I., Bechtel, M., Ahlberg, S., Vogt, A., Blume-Peytavi, U., Schrade, P., Bachmann, S., Hedtrich, S., Schäfer-Korting, M., Rühl, E.

{European Journal of Pharmaceutics and Biopharmaceutics}, 118, pages: 30-37, Elsevier, Amsterdam, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Enhancing metacognitive reinforcement learning using reward structures and feedback

Krueger, P. M., Lieder, F., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

re

Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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The anchoring bias reflects rational use of cognitive resources

Lieder, F., Griffiths, T. L., Huys, Q. J. M., Goodman, N. D.

Psychonomic Bulletin \& Review, 25, pages: 762-794, Springer, 2017 (article)

re

[BibTex]

[BibTex]


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Helping people choose subgoals with sparse pseudo rewards

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

In Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2017 (inproceedings)

re

[BibTex]

[BibTex]


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Modeling cognitive load effects in an interrupted learning task: An ACT-R approach

Wirzberger, M., Rey, G. D., Krems, J.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, pages: 3540-3545, Cognitive Science Society, Austin, TX, 2017 (inproceedings)

re

link (url) [BibTex]

link (url) [BibTex]


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Capture of heavy hydrogen isotopes in a metal-organic framework with active Cu(I) sites

Weinrauch, I., Savchenko, I., Denysenko, D., Souliou, S. M., Kim, H., Le Tacon, M., Daemen, L. L., Cheng, Y., Mavrandonakis, A., Ramirez-Cuesta, A. J., Volkmer, D., Schütz, G., Hirscher, M., Heine, T.

{Nature Communications}, 8, Nature Publishing Group, London, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Multiscale simulations of topological transformations in magnetic-skyrmion spin structures

De Lucia, A., Litzius, K., Krüger, B., Tretiakov, O. A., Kläui, M.

{Physical Review B}, 96(2), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unexpectedly marginal effect of electronic correlations on ultrafast demagnetization after femtosecond laser-pulse excitation

Weng, W., Huang, Haonan, Briones Paz, J. Z., Teeny, N., Müller, B. Y., Haag, M., Kuhn, T., Fähnle, M.

{Physical Review B}, 95(22), American Physical Society, Woodbury, NY, 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Black manganese-rich crusts on a Gothic cathedral

Macholdt, D. S., Herrmann, S., Jochum, K. P., Kilcoyne, A. L. D., Laubscher, T., Pfisterer, H. K., Pöhlker, C., Schwager, B., Weber, B., Weigand, M., Domke, K. F., Andreae, M. O.

{Atmospheric Environment}, 171, pages: 205-220, Elsevier, Amsterdam [u.a.], 2017 (article)

mms

DOI [BibTex]

DOI [BibTex]

2005


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Spectral clustering and transductive inference for graph data

Zhou, D.

NIPS Workshop on Kernel Methods and Structured Domains, December 2005 (talk)

ei

PDF Web [BibTex]

2005


PDF Web [BibTex]


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

ei

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


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Kernel ICA for Large Scale Problems

Jegelka, S., Gretton, A., Achlioptas, D.

In pages: -, NIPS Workshop on Large Scale Kernel Machines, December 2005 (inproceedings)

ei

Web [BibTex]

Web [BibTex]


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Infinite dimensional exponential families by reproducing kernel Hilbert spaces

Fukumizu, K.

In IGAIA 2005, pages: 324-333, 2nd International Symposium on Information Geometry and its Applications, December 2005 (inproceedings)

Abstract
The purpose of this paper is to propose a method of constructing exponential families of Hilbert manifold, on which estimation theory can be built. Although there have been works on infinite dimensional exponential families of Banach manifolds (Pistone and Sempi, 1995; Gibilisco and Pistone, 1998; Pistone and Rogantin, 1999), they are not appropriate to discuss statistical estimation with finite number of samples; the likelihood function with finite samples is not continuous on the manifold. In this paper we use a reproducing kernel Hilbert space as a functional space for constructing an exponential manifold. A reproducing kernel Hilbert space is dened as a Hilbert space of functions such that evaluation of a function at an arbitrary point is a continuous functional on the Hilbert space. Since we can discuss the value of a function with this space, it is very natural to use a manifold associated with a reproducing kernel Hilbert space as a basis of estimation theory. We focus on the maximum likelihood estimation (MLE) with the exponential manifold of a reproducing kernel Hilbert space. As in many non-parametric estimation methods, straightforward extension of MLE to an infinite dimensional exponential manifold suffers the problem of ill-posedness caused by the fact that the estimator should be chosen from the infinite dimensional space with only finite number of constraints given by the data. To solve this problem, a pseudo-maximum likelihood method is proposed by restricting the infinite dimensional manifold to a series of finite dimensional submanifolds, which enlarge as the number of samples increases. Some asymptotic results in the limit of infinite samples are shown, including the consistency of the pseudo-MLE.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Some thoughts about Gaussian Processes

Chapelle, O.

NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

ei

PDF [BibTex]

PDF [BibTex]


Thumb xl ivc05
Representing cyclic human motion using functional analysis

Ormoneit, D., Black, M. J., Hastie, T., Kjellström, H.

Image and Vision Computing, 23(14):1264-1276, December 2005 (article)

Abstract
We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

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pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [BibTex]


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Shortest-path kernels on graphs

Borgwardt, KM., Kriegel, H-P.

In pages: 74-81, IEEE Computer Society, Los Alamitos, CA, USA, Fifth International Conference on Data Mining (ICDM), November 2005 (inproceedings)

Abstract
Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Training Support Vector Machines with Multiple Equality Constraints

Kienzle, W., Schölkopf, B.

In Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720, pages: 182-193, (Editors: JG Carbonell and J Siekmann), Springer, Berlin, Germany, ECML, November 2005 (inproceedings)

Abstract
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

Abstract
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on {methodname} do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron

Kowalczyk, A., Chapelle, O.

In Algorithmic Learning Theory: 16th International Conference, pages: 78-92, Algorithmic Learning Theory, October 2005 (inproceedings)

Abstract
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from overfitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate ma- chines able to classify training and test data consistently.

ei

PDF [BibTex]

PDF [BibTex]


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Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

Abstract
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]