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2007


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Low-temperature thermal-desorption mass spectroscopy applied to investigate the hydrogen adsorption on porous materials

Panella, B., Hirscher, M., Ludescher, B.

{Microporous and Mesoporous Materials}, 103, pages: 230-234, 2007 (article)

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[BibTex]

2007



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Dependence of the critical temperature of YBCO thin films on spinpolarized quasiparticle injection

Habermeier, H.-U., Soltan, S., Albrecht, J.

{International Journal of Modern Physics B}, 21(18 \& 19):3303-3306, 2007 (article)

mms

[BibTex]

[BibTex]


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Copper alloys for the restoration of reed pipes in historic organs

Straumal, B. B., Baretzky, B., Kalnins, J., Aslund, A., Friesel, M.

{Journal of Functional Materials}, 1, pages: 4-10, 2007 (article)

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[BibTex]

[BibTex]


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Inhomogeneous vortex distribution and magnetic coupling in oxide superconductor-ferromagnet hybrids

Albrecht, J., Djupmyr, M., Soltan, S., Habermeier, H.-U., Connolly, M. R., Bending, S. J.

{New Journal of Physics}, 9, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Metal hydride materials for solid hydrogen storage: a review

Sakintuna, B., Lamari-Darkrim, F., Hirscher, M.

{International Journal of Hydrogen Energy}, 32, pages: 1121-1140, 2007 (article)

mms

[BibTex]

[BibTex]


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Thermal reversal of exchange spring composite media in magnetic fields

Goll, D., Macke, S., Bertram, H. N.

{Applied Physics Letters}, 90, 2007 (article)

mms

[BibTex]

[BibTex]


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Magnetism of Co-doped ZnO thin films

Gacic, M., Jakob, G., Herbort, C., Adrian, H., Tietze, T., Brück, S., Goering, E.

{Physical Review B}, 75, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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iCub - The Design and Realization of an Open Humanoid Platform for Cognitive and Neuroscience Research

Tsagarakis, N., Metta, G., Sandini, G., Vernon, D., Beira, R., Becchi, F., Righetti, L., Santos-Victor, J., Ijspeert, A., Carrozza, M., Caldwell, D.

Advanced Robotics, 21(10):1151-1175, 2007 (article)

Abstract
The development of robotic cognition and the advancement of understanding of human cognition form two of the current greatest challenges in robotics and neuroscience, respectively. The RobotCub project aims to develop an embodied robotic child (iCub) with the physical (height 90 cm and mass less than 23 kg) and ultimately cognitive abilities of a 2.5-year-old human child. The iCub will be a freely available open system which can be used by scientists in all cognate disciplines from developmental psychology to epigenetic robotics to enhance understanding of cognitive systems through the study of cognitive development. The iCub will be open both in software, but more importantly in all aspects of the hardware and mechanical design. In this paper the design of the mechanisms and structures forming the basic 'body' of the iCub are described. The papers considers kinematic structures dynamic design criteria, actuator specification and selection, and detailed mechanical and electronic design. The paper concludes with tests of the performance of sample joints, and comparison of these results with the design requirements and simulation projects.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Subfeature patterning of organic and inorganic materials using robotic assembly

Tafazzoli, A., Cheng, C., Pawashe, C., Sabo, E. K., Trofin, L., Sitti, M., LeDuc, P. R.

Journal of materials research, 22(06):1601-1608, Cambridge University Press, 2007 (article)

pi

[BibTex]

[BibTex]


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Effect of backing layer thickness on adhesion of single-level elastomer fiber arrays

Kim, S., Sitti, M., Hui, C., Long, R., Jagota, A.

Applied Physics Letters, 91(16):161905, AIP, 2007 (article)

pi

[BibTex]

[BibTex]


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Universal temperature scaling of flux line pinning in high-temperature superconducting thin films

Albrecht, J., Djupmyr, M., Brück, S.

{Journal of Physics: Condensed Matter}, 19, 2007 (article)

mms

[BibTex]

[BibTex]


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Dependence of the critical temperature of YBCO thin films on spin-polarized quasiparticle injection

Habermeier, H.-U., Soltan, S., Albrecht, J.

{Physica C}, 460-462, pages: 32-35, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Interaction of ferromagnetic LCMO layers through a superconducting YBCO spacer

Ravikumar, G., Yashwant, G., Singh, M. R., Gupta, S. K., Bhattacharya, S., Soltan, S., Albrecht, J., Habermeier, H.-U.

{Physica C}, 460-462, pages: 1375-1376, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Vortex dynamics in Permalloy disks with artificial defects: suppression of the gyrotropic mode

Kuepper, K., Bischoff, L., Akhmadaliev, C., Fassbinder, J., Stoll, H., Chou, K., Puzic, A., Fauth, K., Dolgos, D., Schütz, G., Van Waeyenberge, B., Tyliszczak, T., Neudecker, I., Woltersdorf, G., Back, C.

{Appplied Physics Letters}, 90, 2007 (article)

mms

[BibTex]

[BibTex]


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Vacancy-interstitial annihilation in titanomagnetite by thermal annealing

Walz, F., Brabers, V. A. M., Brabers, J. H. V. J., Kronmüller, H.

{Physica Status Solidi (A)}, 204(10):3514-3525, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Theory of X-ray absorption spectroscopy in solids: mixing of the core states by the aspherical effective potential

Kostoglou, C., Komelj, M., Fähnle, M.

{Physical Review B}, 75, 2007 (article)

mms

[BibTex]

[BibTex]


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Zinc oxide microcapsules obtained via a bio-inspired approach

Lipowsky, P., Hirscher, M., Hoffmann, R. C., Bill, J., Aldinger, F.

{Nanotechnology}, 18, 2007 (article)

mms

[BibTex]

[BibTex]


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Grain boundary phase observed in Al-5 at.\textpercent Zn alloy by using HREM

Straumal, B. B., Mazilkin, A. A., Kogtenkova, O. A., Protasova, S. G., Baretzky, B.

{Philosophical Magazine Letters}, 87(6):423-430, 2007 (article)

mms

[BibTex]

[BibTex]


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Transport current improvements of in situ MgB2 tapes by the addition of carbon nanotubes, silicon carbide or graphite

Kovac, P., Husek, I., Skakalova, V., Meyer, J., Dobrocka, E., Hirscher, M., Roth, S.

{Superconductor Science and Technology}, 20, pages: 105-111, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Learning an Outlier-Robust Kalman Filter

Ting, J., Theodorou, E., Schaal, S.

CLMC Technical Report: TR-CLMC-2007-1, Los Angeles, CA, 2007, clmc (techreport)

Abstract
We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step?s state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog.

am

PDF [BibTex]

PDF [BibTex]


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Adhesion and anisotropic friction enhancements of angled heterogeneous micro-fiber arrays with spherical and spatula tips

Murphy, M. P., Aksak, B., Sitti, M.

Journal of Adhesion Science and Technology, 21(12-13):1281-1296, Taylor & Francis Group, 2007 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Surface-tension-driven biologically inspired water strider robots: Theory and experiments

Song, Y. S., Sitti, M.

IEEE Transactions on robotics, 23(3):578-589, IEEE, 2007 (article)

pi

[BibTex]

[BibTex]


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Absorption spectroscopy and XMCD at the Verwey transition of Fe3O4

Goering, E., Lafkioti, M., Gold, S., Schütz, G.

{Journal of Magnetism and Magnetic Materials}, 310, pages: 249-251, 2007 (article)

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[BibTex]

[BibTex]


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Overcoming the Dipolar Disorder in Dense CoFe Nanoparticle Ensembles: Superferromagnetism

Bedanta, S., Eimüller, T., Kleemann, W., Rhensius, J., Stromberg, F., Amaladass, E., Cardoso, S., Freitas, P. P.

{Physical Review Letters}, 98, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Ultrafast nanomagnetic toggle switching of vortex cores

Hertel, R., Gliga, S., Fähnle, M., Schneider, C. M.

{Physical Review Letters}, 98, 2007 (article)

mms

[BibTex]

[BibTex]


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Element-specific spin and orbital momentum dynamics of Fe/Gd multilayers

Bartelt, A. F., Comin, A., Feng, J., Nasiatka, J. R., Eimüller, T., Ludescher, B., Schütz, G., Padmore, H. A., Young, A. T., Scholl, A.

{Applied Physics Letters}, 90, 2007 (article)

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DOI [BibTex]

DOI [BibTex]


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Slow relaxation of spin reorientation following ultrafast optical excitation

Eimüller, T., Scholl, A., Ludescher, B., Schütz, G., Thiele, J.

{Applied Physics Letters}, 91, 2007 (article)

mms

[BibTex]

[BibTex]


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One-pot synthesis of core-shell FeRh nanoparticles

Ciuculescu, D., Amiens, C., Respaud, M., Falqui, A., Lecante, P., Benfield, R. E., Jiang, L., Fauth, K., Chaudret, B.

{Chemistry of Materials}, 19(19):4624-4626, 2007 (article)

mms

[BibTex]

[BibTex]


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Spin-polarized quasiparticles injection effects in the normal state of YBCO thin films

Soltan, S., Albrecht, J., Habermeier, H.-U.

{Physica C}, 460-462, pages: 1088-1089, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Direct observation of the vortex core magnetization and its dynamics

Chou, K. W., Puzic, A., Stoll, H., Dolgos, D., Schütz, G., Van Waeyenberge, B., Vansteenkiste, A., Tyliszczak, T., Woltersdorf, G., Back, C. H.

{Applied Physics Letters}, 90, 2007 (article)

mms

[BibTex]

[BibTex]


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Superparamagnetism in small Fe clusters on Cu(111)

Ballentine, G., He\ssler, M., Kinza, M., Fauth, K.

{The European Physical Journal D}, 45, pages: 535-537, 2007 (article)

mms

DOI [BibTex]

DOI [BibTex]

2003


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Support Vector Channel Selection in BCI

Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.

(120), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, December 2003 (techreport)

Abstract
Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination [3] and Zero-Norm Optimization [13] which are based on the training of Support Vector Machines (SVM) [11]. These algorithms can provide more accurate solutions than standard filter methods for feature selection [14]. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.

ei

PDF Web [BibTex]

2003


PDF Web [BibTex]


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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

PostScript [BibTex]


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Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

(118), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, October 2003 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]


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Ranking on Data Manifolds

Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.

(113), Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, June 2003 (techreport)

Abstract
The Google search engine has had a huge success with its PageRank web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the World Wide Web using random walk. This algorithm can only be used for graph data, however. Here we propose a simple universal ranking algorithm for vectorial data, based on the exploration of the intrinsic global geometric structure revealed by a huge amount of data. Experimental results from image and text to bioinformatics illustrates the validity of our algorithm.

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis

Kim, K., Franz, M., Schölkopf, B.

(109), MPI f. biologische Kybernetik, Tuebingen, June 2003 (techreport)

Abstract
A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method, a convergence proof, and preliminary applications in image hyperresolution are presented. In addition, we discuss the extension of the method to the online learning of kernel principal components.

ei

PDF [BibTex]

PDF [BibTex]


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Learning with Local and Global Consistency

Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.

(112), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, June 2003 (techreport)

Abstract
We consider the learning problem in the transductive setting. Given a set of points of which only some are labeled, the goal is to predict the label of the unlabeled points. A principled clue to solve such a learning problem is the consistency assumption that a classifying function should be sufficiently smooth with respect to the structure revealed by these known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

ei

[BibTex]

[BibTex]


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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Implicit Wiener Series

Franz, M., Schölkopf, B.

(114), Max Planck Institute for Biological Cybernetics, June 2003 (techreport)

Abstract
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a neural system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose a new estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems. Numerical experiments show performance advantages in terms of convergence, interpretability and system size that can be handled.

ei

PDF [BibTex]

PDF [BibTex]


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Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms

Weston, J., Leslie, C., Elisseeff, A., Noble, W.

(111), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2003 (techreport)

Abstract
A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST.

ei

PDF [BibTex]

PDF [BibTex]


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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

DOI [BibTex]


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The Geometry Of Kernel Canonical Correlation Analysis

Kuss, M., Graepel, T.

(108), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2003 (techreport)

Abstract
Canonical correlation analysis (CCA) is a classical multivariate method concerned with describing linear dependencies between sets of variables. After a short exposition of the linear sample CCA problem and its analytical solution, the article proceeds with a detailed characterization of its geometry. Projection operators are used to illustrate the relations between canonical vectors and variates. The article then addresses the problem of CCA between spaces spanned by objects mapped into kernel feature spaces. An exact solution for this kernel canonical correlation (KCCA) problem is derived from a geometric point of view. It shows that the expansion coefficients of the canonical vectors in their respective feature space can be found by linear CCA in the basis induced by kernel principal component analysis. The effect of mappings into higher dimensional feature spaces is considered critically since it simplifies the CCA problem in general. Then two regularized variants of KCCA are discussed. Relations to other methods are illustrated, e.g., multicategory kernel Fisher discriminant analysis, kernel principal component regression and possible applications thereof in blind source separation.

ei

PDF [BibTex]

PDF [BibTex]


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The Kernel Mutual Information

Gretton, A., Herbrich, R., Smola, A.

Max Planck Institute for Biological Cybernetics, April 2003 (techreport)

Abstract
We introduce two new functions, the kernel covariance (KC) and the kernel mutual information (KMI), to measure the degree of independence of several continuous random variables. The former is guaranteed to be zero if and only if the random variables are pairwise independent; the latter shares this property, and is in addition an approximate upper bound on the mutual information, as measured near independence, and is based on a kernel density estimate. We show that Bach and Jordan‘s kernel generalised variance (KGV) is also an upper bound on the same kernel density estimate, but is looser. Finally, we suggest that the addition of a regularising term in the KGV causes it to approach the KMI, which motivates the introduction of this regularisation. The performance of the KC and KMI is verified in the context of instantaneous independent component analysis (ICA), by recovering both artificial and real (musical) signals following linear mixing.

ei

PostScript [BibTex]

PostScript [BibTex]


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Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., Winther, O.

Complexity, 8(4):64-68, April 2003 (article)

Abstract
We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

ei

PDF GZIP Web [BibTex]

PDF GZIP Web [BibTex]


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Feature selection and transduction for prediction of molecular bioactivity for drug design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.

Bioinformatics, 19(6):764-771, April 2003 (article)

Abstract
Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task. Results: Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.

ei

Web [BibTex]


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Use of the Zero-Norm with Linear Models and Kernel Methods

Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.

Journal of Machine Learning Research, 3, pages: 1439-1461, March 2003 (article)

Abstract
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature selection, minimizing training error and ensuring sparsity in solutions. We derive a simple but practical method for achieving these goals and discuss its relationship to existing techniques of minimizing the zero-norm. The method boils down to implementing a simple modification of vanilla SVM, namely via an iterative multiplicative rescaling of the training data. Applications we investigate which aid our discussion include variable and feature selection on biological microarray data, and multicategory classification.

ei

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


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An Introduction to Variable and Feature Selection.

Guyon, I., Elisseeff, A.

Journal of Machine Learning, 3, pages: 1157-1182, 2003 (article)

ei

[BibTex]

[BibTex]