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2011


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Herstellung und Qualifizierung gesputterter Magnesiumdiboridschichten

Breyer, F.

Hochschule Aalen, Aalen, 2011 (mastersthesis)

mms

[BibTex]

2011


[BibTex]


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Large hidden orbital moments in magnetite

Goering, E.

{Physica Status Solidi B}, 248(10):2345-2351, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Cr magnetization reversal at the CrO2/RuO2 interface: Origin of the reduced GMR effect

Zafar, K., Audehm, P., Schütz, G., Goering, E., Pathak, M., Chetry, K. B., LeClair, P. R., Gupta, A.

{Physical Review B}, 84, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetocaloric effect, magnetic domain structure and spin-reorientation transitios in HoCo5 single crystals

Skokov, K. P., Pastushenkov, Y. G., Koshkid\textquotesingleko, Y. S., Schütz, G., Goll, D., Ivanova, T. I., Nikitin, S. A., Semenova, E. M., Petrenko, A. V.

{Journal of Magnetism and Magnetic Materials}, 323(5):447-450, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Elucidating gating effects for hydrogen sorption in MFU-4-type triazolate-based metal-organic frameworks featuring different pore sizes

Denysenko, D., Grzywa, M., Tonigold, M., Streppel, B., Krkljus, I., Hirscher, M., Mugnaioli, E., Kolb, U., Hanss, J., Volkmer, D.

{Chemistry - A European Journal}, 17(6):1837-1848, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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BET specific surface area and pore structure of MOFs determined by hydrogen adsorption at 20 K

Streppel, B., Hirscher, M.

{Physical Chemistry Chemical Physics}, 13(8):3220-3222, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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High contrast magnetic and nonmagnetic sample current microscopy for bulk and transparent samples using soft X-rays

Nolle, D., Weigand, M., Schütz, G., Goering, E.

{Microscopy and Microanalysis}, 17, pages: 834-842, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic vortex core reversal by rotating magnetic fields generated on micrometer length scales

Curcic, M., Stoll, H., Weigand, M., Sackmann, V., Jüllig, P., Kammerer, M., Noske, M., Sproll, M., Van Waeyenberge, B., Vansteenkiste, A., Woltersdorf, G., Tyliszczak, T., Schütz, G.

{Physica Status Solidi B}, 248(10):2317-2322, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Enhancing adhesion of biologically inspired polymer microfibers with a viscous oil coating

Cheung, E., Sitti, M.

The Journal of Adhesion, 87(6):547-557, Taylor & Francis Group, 2011 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Formation of two amorphous phases in the Ni60Nb18Y22 alloy after high pressure torsion

Straumal, B. B., Mazilkin, A. A., Protasova, S. G., Goll, D., Baretzky, B., Bakai, A. S., Dobatkin, S. V.

{Kovove Materialy-Metallic Materials}, 49(1):17-22, 2011 (article)

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link (url) [BibTex]

link (url) [BibTex]


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Structure and properties of nanograined Fe-C alloys after severe plastic deformation

Straumal, B. B., Dobatkin, S. V., Rodin, A. O., Protasova, S. G., Mazilkin, A. A., Goll, D., Baretzky, B.

{Advanced Engineering Materials}, 13(6):463-469, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Increased flux pinning in YBa2Cu3O7-δthin-film devices through embedding of Au nano crystals

Katzer, C., Schmidt, M., Michalowski, P., Kuhwald, D., Schmidl, F., Grosse, V., Treiber, S., Stahl, C., Albrecht, J., Hübner, U., Undisz, A., Rettenmayr, M., Schütz, G., Seidel, P.

{Europhysics Letters}, 95(6), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Signal transfer in a chain of stray-field coupled ferromagnetic squares

Vogel, A., Martens, M., Weigand, M., Meier, G.

{Applied Physics Letters}, 99, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Electron theory of magnetoelectric effects in metallic ferromagnetic nanostructures

Subkow, S., Fähnle, M.

{Physical Review B}, 84, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic antivortex-core reversal by rotating magnetic fields

Kamionka, T., Martens, M., Chou, K., Drews, A., Tyliszczak, T., Stoll, H., Van Waeyenberge, B., Meier, G.

{Physical Review B}, 83, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic properties of exchange-spring composite films

Kronmüller, H., Goll, D.

{Physica Status Solidi B}, 248(10):2361-2367, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Wetting transition of grain boundaries in the Sn-rich part of the Sn-Bi phase diagram

Yeh, C.-H., Chang, L.-S., Straumal, B. B.

{Journal of Materials Science}, 46(5):1557-1562, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Piezoelectric polymer fiber arrays for tactile sensing applications

Sümer, B., Aksak, B., Şsahin, K., Chuengsatiansup, K., Sitti, M.

Sensor Letters, 9(2):457-463, American Scientific Publishers, 2011 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Control methodologies for a heterogeneous group of untethered magnetic micro-robots

Floyd, S., Diller, E., Pawashe, C., Sitti, M.

The International Journal of Robotics Research, 30(13):1553-1565, SAGE Publications, 2011 (article)

pi

[BibTex]

[BibTex]


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Study of krypton/xenon storage and separation in microporous frameworks

Soleimani Dorcheh, A.

Universität Darmstadt, Darmstadt, 2011 (mastersthesis)

mms

[BibTex]

[BibTex]


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Influence of dot size and annealing on the magnetic properties of large-area L10-FePt nanopatterns

Bublat, T., Goll, D.

{Journal of Applied Physics}, 110(7), 2011 (article)

mms

DOI [BibTex]


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The temperature-dependent magnetization profile across an epitaxial bilayer of ferromagnetic La2/3Ca1/3MnO3 and superconducting YBa2Cu3O7-δ

Brück, S., Treiber, S., Macke, S., Audehm, P., Christiani, G., Soltan, S., Habermeier, H., Goering, E., Albrecht, J.

{New Journal of Physics}, 13(3), 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Spin interactions in bcc and fcc Fe beyond the Heisenberg model

Singer, R., Dietermann, F., Fähnle, M.

{Physical Review Letters}, 107, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Route to a family of robust, non-interpenetrated metal-organic frameworks with pto-like topology

Klein, N., Senkovska, I., Baburin, I. A., Grünker, R., Stoeck, U., Schlichtenmayer, M., Streppel, B., Mueller, U., Leoni, S., Hirscher, M., Kaskel, S.

{Chemistry - A European Journal}, 17(46):13007-13016, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Initial stages of growth of iron on silicon for spin injection through Schottky barrier

Dash, S. P., Carstanjen, H. D.

{Physica Status Solidi B}, 248(10):2300-2304, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Fe3O4/ZnO: A high-quality magnetic oxide-semiconductor heterostructure by reactive deposition

Paul, M., Kufer, D., Müller, A., Brück, S., Goering, E., Kamp, M., Verbeeck, J., Tian, H., Van Tendeloo, G., Ingle, N. J. C., Sing, M., Claessen, R.

{Applied Physics Letters}, 98, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Influence of texture on the ferromagnetic properties of nanograined ZnO films

Straumal, B., Mazilkin, A., Protasova, S., Myatiev, A., Straumal, P., Goering, E., Baretzky, B.

{Physica Status Solidi B}, 248(7):1581-1586, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Control of spin configuration in half-metallic La0.7Sr0.3MnO3 nano-structures

Rhensius, J., Vaz, C. A. F., Bisig, A., Schweitzer, S., Heidler, J., Körner, H. S., Locatelli, A., Niño, M. A., Weigand, M., Méchin, L., Gaucher, F., Goering, E., Heyderman, L. J., Kläui, M.

{Applied Physics Letters}, 99(6), 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Comparison of various sol-gel derived metal oxide layers for inverted organic solar cells

Oh, H., Krantz, J., Litzov, I., Stubhan, T., Pinna, L., Brabec, C. J.

{Solar Energy Materials \& Solar Cells}, 95(8):2194-2199, 2011 (article)

mms

DOI [BibTex]

DOI [BibTex]

2009


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Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Lampert, C., Blaschko, M., Hofmann, T.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2129-2142, December 2009 (article)

Abstract
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object‘s location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the chi^2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.

ei

PDF Web DOI [BibTex]

2009


PDF Web DOI [BibTex]


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Generation of three-dimensional random rotations in fitting and matching problems

Habeck, M.

Computational Statistics, 24(4):719-731, December 2009 (article)

Abstract
An algorithm is developed to generate random rotations in three-dimensional space that follow a probability distribution arising in fitting and matching problems. The rotation matrices are orthogonally transformed into an optimal basis and then parameterized using Euler angles. The conditional distributions of the three Euler angles have a very simple form: the two azimuthal angles can be decoupled by sampling their sum and difference from a von Mises distribution; the cosine of the polar angle is exponentially distributed and thus straighforward to generate. Simulation results are shown and demonstrate the effectiveness of the method. The algorithm is compared to other methods for generating random rotations such as a random walk Metropolis scheme and a Gibbs sampling algorithm recently introduced by Green and Mardia. Finally, the algorithm is applied to a probabilistic version of the Procrustes problem of fitting two point sets and applied in the context of protein structure superposition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning

Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.

Neural Networks, 22(10):1399-1410, December 2009 (article)

Abstract
Off-policy reinforcement learning is aimed at efficiently using data samples gathered from a policy that is different from the currently optimized policy. A common approach is to use importance sampling techniques for compensating for the bias of value function estimators caused by the difference between the data-sampling policy and the target policy. However, existing off-policy methods often do not take the variance of the value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a variant of cross-validation. We demonstrate the usefulness of the proposed approach through simulations.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Guest editorial: special issue on structured prediction

Parker, C., Altun, Y., Tadepalli, P.

Machine Learning, 77(2-3):161-164, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Structured prediction by joint kernel support estimation

Lampert, CH., Blaschko, MB.

Machine Learning, 77(2-3):249-269, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A note on ethical aspects of BCI

Haselager, P., Vlek, R., Hill, J., Nijboer, F.

Neural Networks, 22(9):1352-1357, November 2009 (article)

Abstract
This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Model Learning with Local Gaussian Process Regression

Nguyen-Tuong, D., Seeger, M., Peters, J.

Advanced Robotics, 23(15):2015-2034, November 2009 (article)

Abstract
Precise models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time model online learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive learning performance for hig h-dimensional data while being sufficiently fast for real-time learning. The effectiveness of LGP is exhibited by a comparison with the state-of-the-art regression techniques, such as GPR, LWPR and ν-support vector regression. The applicability of the proposed LGP method is demonstrated by real-time online learning of the inverse dynamics model for robot model-based control on a Barrett WAM robot arm.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Inferring textual entailment with a probabilistically sound calculus

Harmeling, S.

Natural Language Engineering, 15(4):459-477, October 2009 (article)

Abstract
We introduce a system for textual entailment that is based on a probabilistic model of entailment. The model is defined using a calculus of transformations on dependency trees, which is characterized by the fact that derivations in that calculus preserve the truth only with a certain probability. The calculus is successfully evaluated on the datasets of the PASCAL Challenge on Recognizing Textual Entailment.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures

Rasmussen, CE., de la Cruz, BJ., Ghahramani, Z., Wild, DL.

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6(4):615-628, October 2009 (article)

Abstract
Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture models provide a non-parametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model based clustering methods have been to short time series data. In this paper we present a case study of the application of non-parametric Bayesian clustering methods to the clustering of high-dimensional non-time series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a Dirichlet process mixture model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract
This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

ei

PDF [BibTex]

PDF [BibTex]


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Thermodynamic efficiency of information and heat flow

Allahverdyan, A., Janzing, D., Mahler, G.

Journal of Statistical Mechanics: Theory and Experiment, 2009(09):P09011, September 2009 (article)

Abstract
A basic task of information processing is information transfer (flow). P0 Here we study a pair of Brownian particles each coupled to a thermal bath at temperatures T1 and T2 . The information flow in such a system is defined via the time-shifted mutual information. The information flow nullifies at equilibrium, and its efficiency is defined as the ratio of the flow to the total entropy production in the system. For a stationary state the information flows from higher to lower temperatures, and its efficiency is bounded from above by (max[T1 , T2 ])/(|T1 − T2 |). This upper bound is imposed by the second law and it quantifies the thermodynamic cost for information flow in the present class of systems. It can be reached in the adiabatic situation, where the particles have widely different characteristic times. The efficiency of heat flow—defined as the heat flow over the total amount of dissipated heat—is limited from above by the same factor. There is a complementarity between heat and information flow: the set-up which is most efficient for the former is the least efficient for the latter and vice versa. The above bound for the efficiency can be (transiently) overcome in certain non-stationary situations, but the efficiency is still limited from above. We study yet another measure of information processing (transfer entropy) proposed in the literature. Though this measure does not require any thermodynamic cost, the information flow and transfer entropy are shown to be intimately related for stationary states.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Does Cognitive Science Need Kernels?

Jäkel, F., Schölkopf, B., Wichmann, F.

Trends in Cognitive Sciences, 13(9):381-388, September 2009 (article)

Abstract
Kernel methods are among the most successful tools in machine learning and are used in challenging data analysis problems in many disciplines. Here we provide examples where kernel methods have proven to be powerful tools for analyzing behavioral data, especially for identifying features in categorization experiments. We also demonstrate that kernel methods relate to perceptrons and exemplar models of categorization. Hence, we argue that kernel methods have neural and psychological plausibility, and theoretical results concerning their behavior are therefore potentially relevant for human category learning. In particular, we believe kernel methods have the potential to provide explanations ranging from the implementational via the algorithmic to the computational level.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Robot Learning

Peters, J., Morimoto, J., Tedrake, R., Roy, N.

IEEE Robotics and Automation Magazine, 16(3):19-20, September 2009 (article)

Abstract
Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the Defense Advanced Research Projects Agency (DARPA) challenges, and the growing number of research programs funded by governments around the world.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Kernel Methods in Computer Vision

Lampert, CH.

Foundations and Trends in Computer Graphics and Vision, 4(3):193-285, September 2009 (article)

Abstract
Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Fast Kernel-Based Independent Component Analysis

Shen, H., Jegelka, S., Gretton, A.

IEEE Transactions on Signal Processing, 57(9):3498-3511, September 2009 (article)

Abstract
Recent approaches to independent component analysis (ICA) have used kernel independence measures to obtain highly accurate solutions, particularly where classical methods experience difficulty (for instance, sources with near-zero kurtosis). FastKICA (fast HSIC-based kernel ICA) is a new optimization method for one such kernel independence measure, the Hilbert-Schmidt Independence Criterion (HSIC). The high computational efficiency of this approach is achieved by combining geometric optimization techniques, specifically an approximate Newton-like method on the orthogonal group, with accurate estimates of the gradient and Hessian based on an incomplete Cholesky decomposition. In contrast to other efficient kernel-based ICA algorithms, FastKICA is applicable to any twice differentiable kernel function. Experimental results for problems with large numbers of sources and observations indicate that FastKICA provides more accurate solutions at a given cost than gradient descent on HSIC. Comparing with other recently published ICA methods, FastKICA is competitive in terms of accuracy, relatively insensitive to local minima when initialized far from independence, and more robust towards outliers. An analysis of the local convergence properties of FastKICA is provided.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Guest editorial: Special issue on robot learning, Part B

Peters, J., Ng, A.

Autonomous Robots, 27(2):91-92, August 2009 (article)

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Policy Search for Motor Primitives

Peters, J., Kober, J.

KI - Zeitschrift K{\"u}nstliche Intelligenz, 23(3):38-40, August 2009 (article)

Abstract
Many motor skills in humanoid robotics can be learned using parametrized motor primitives from demonstrations. However, most interesting motor learning problems require self-improvement often beyond the reach of current reinforcement learning methods due to the high dimensionality of the state-space. We develop an EM-inspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several well-known parametrized policy search methods and show that it outperforms them. We apply it to motor learning problems and show that it can learn the complex Ball-in-a-Cup task using a real Barrett WAM robot arm.

ei

Web [BibTex]

Web [BibTex]