Header logo is


2007


no image
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)

mms

[BibTex]

2007



no image
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]


no image
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)

mms

[BibTex]

[BibTex]


no image
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)

mms

DOI [BibTex]

DOI [BibTex]


no image
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]


no image
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]


no image
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)

mms

DOI [BibTex]

DOI [BibTex]


no image
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.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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)

mms

DOI [BibTex]

DOI [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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)

mms

[BibTex]

[BibTex]


no image
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)

mms

DOI [BibTex]

DOI [BibTex]


no image
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]


no image
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)

mms

DOI [BibTex]

DOI [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]

2004


no image
On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, M.

International Journal of Humanoid Robotics, 1(4):613-636, December 2004 (article)

ei

[BibTex]

2004


[BibTex]


no image
Insect-inspired estimation of egomotion

Franz, MO., Chahl, JS., Krapp, HG.

Neural Computation, 16(11):2245-2260, November 2004 (article)

Abstract
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

ei

PDF PostScript Web DOI [BibTex]

PDF PostScript Web DOI [BibTex]


no image
Efficient face detection by a cascaded support-vector machine expansion

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

Proceedings of The Royal Society of London A, 460(2501):3283-3297, A, November 2004 (article)

Abstract
We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Fast Binary and Multi-Output Reduced Set Selection

Weston, J., Bakir, G.

(132), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2004 (techreport)

Abstract
We propose fast algorithms for reducing the number of kernel evaluations in the testing phase for methods such as Support Vector Machines (SVM) and Ridge Regression (RR). For non-sparse methods such as RR this results in significantly improved prediction time. For binary SVMs, which are already sparse in their expansion, the pay off is mainly in the cases of noisy or large-scale problems. However, we then further develop our method for multi-class problems where, after choosing the expansion to find vectors which describe all the hyperplanes jointly, we again achieve significant gains.

ei

PostScript [BibTex]

PostScript [BibTex]


no image
Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


no image
Semi-Supervised Induction

Yu, K., Tresp, V., Zhou, D.

(141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

Abstract
Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Learning kernels from biological networks by maximizing entropy

Tsuda, K., Noble, W.

Bioinformatics, 20(Suppl. 1):i326-i333, August 2004 (article)

Abstract
Motivation: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. Results: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein–protein interaction networks.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
On Hausdorff Distance Measures

Shapiro, MD., Blaschko, MB.

Department of Computer Science, University of Massachusetts Amherst, August 2004 (techreport)

ei

[BibTex]

[BibTex]


no image
Masking effect produced by Mach bands on the detection of narrow bars of random polarity

Henning, GB., Hoddinott, KT., Wilson-Smith, ZJ., Hill, NJ.

Journal of the Optical Society of America, 21(8):1379-1387, A, August 2004 (article)

ei

[BibTex]

[BibTex]


no image
Object categorization with SVM: kernels for local features

Eichhorn, J., Chapelle, O.

(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

ei

PDF [BibTex]

PDF [BibTex]


no image
Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and one image classification problem.

ei

PDF [BibTex]

PDF [BibTex]


no image
Kernels, Associated Structures and Generalizations

Hein, M., Bousquet, O.

(127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
This paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered.

ei

PDF [BibTex]

PDF [BibTex]


no image
Triangle Fixing Algorithms for the Metric Nearness Problem

Dhillon, I., Sra, S., Tropp, J.

Univ. of Texas at Austin, June 2004 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


no image
Support Vector Channel Selection in BCI

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

IEEE Transactions on Biomedical Engineering, 51(6):1003-1010, June 2004 (article)

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 and Zero-Norm Optimization which are based on the training of Support Vector Machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. 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

DOI [BibTex]

DOI [BibTex]


no image
Distance-Based Classification with Lipschitz Functions

von Luxburg, U., Bousquet, O.

Journal of Machine Learning Research, 5, pages: 669-695, June 2004 (article)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. To analyze the resulting algorithm, we prove several representer theorems. They state that there always exist solutions of the Lipschitz classifier which can be expressed in terms of distance functions to training points. We provide generalization bounds for Lipschitz classifiers in terms of the Rademacher complexities of some Lipschitz function classes. The generality of our approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz classifier, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

ei

PDF PostScript PDF [BibTex]

PDF PostScript PDF [BibTex]


Thumb xl woodtransbme04
On the variability of manual spike sorting

Wood, F., Black, M. J., Vargas-Irwin, C., Fellows, M., Donoghue, J. P.

IEEE Trans. Biomedical Engineering, 51(6):912-918, June 2004 (article)

ps

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


Thumb xl wutransbme04
Modeling and decoding motor cortical activity using a switching Kalman filter

Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J. P.

IEEE Trans. Biomedical Engineering, 51(6):933-942, June 2004 (article)

Abstract
We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A “hidden state” models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.

ps

pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


no image
cDNA-Microarray Technology in Cartilage Research - Functional Genomics of Osteoarthritis [in German]

Aigner, T., Finger, F., Zien, A., Bartnik, E.

Zeitschrift f{\"u}r Orthop{\"a}die und ihre Grenzgebiete, 142(2):241-247, April 2004 (article)

Abstract
Functional genomics represents a new challenging approach in order to analyze complex diseases such as osteoarthritis on a molecular level. The characterization of the molecular changes of the cartilage cells, the chondrocytes, enables a better understanding of the pathomechanisms of the disease. In particular, the identification and characterization of new target molecules for therapeutic intervention is of interest. Also, potential molecular markers for diagnosis and monitoring of osteoarthritis contribute to a more appropriate patient management. The DNA-microarray technology complements (but does not replace) biochemical and biological research in new disease-relevant genes. Large-scale functional genomics will identify molecular networks such as yet identified players in the anabolic-catabolic balance of articular cartilage as well as disease-relevant intracellular signaling cascades so far rather unknown in articular chondrocytes. However, at the moment it is also important to recognize the limitations of the microarray technology in order to avoid over-interpretation of the results. This might lead to misleading results and prevent to a significant extent a proper use of the potential of this technology in the field of osteoarthritis.

ei

[BibTex]

[BibTex]