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2007


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Bayesian Nonparametric Regression with Local Models

Ting, J., Schaal, S.

In Workshop on Robotic Challenges for Machine Learning, NIPS 2007, 2007, clmc (inproceedings)

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[BibTex]

2007


[BibTex]


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STRIDE: A highly maneuverable and non-tethered water strider robot

Song, Y. S., Sitti, M.

In Robotics and Automation, 2007 IEEE International Conference on, pages: 980-984, 2007 (inproceedings)

pi

[BibTex]

[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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Dry spinning polymeric nano/microfiber arrays using glass micropipettes with controlled porosities and fiber diameters

Nain, A. S., Gupta, A., Amon, C., Sitti, M.

In Nanotechnology, 2007. IEEE-NANO 2007. 7th IEEE Conference on, pages: 728-732, 2007 (inproceedings)

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)

mms

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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Micromagnetism-microstructure relations and the hysteresis loop

Goll, D.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 2: Micromagnetism, pages: 1023-1058, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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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Task space control with prioritization for balance and locomotion

Mistry, M., Nakanishi, J., Schaal, S.

In IEEE International Conference on Intelligent Robotics Systems (IROS 2007), San Diego, CA: Oct. 29 Ð Nov. 2, 2007, clmc (inproceedings)

Abstract
This paper addresses locomotion with active balancing, via task space control with prioritization. The center of gravity (COG) and foot of the swing leg are treated as task space control points. Floating base inverse kinematics with constraints is employed, thereby allowing for a mobile platform suitable for locomotion. Different techniques of task prioritization are discussed and we clarify differences and similarities of previous suggested work. Varying levels of prioritization for control are examined with emphasis on singularity robustness and the negative effects of constraint switching. A novel controller for task space control of balance and locomotion is developed which attempts to address singularity robustness, while minimizing discontinuities created by constraint switching. Controllers are evaluated using a quadruped robot simulator engaging in a locomotion task.

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link (url) [BibTex]

link (url) [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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Microrobotically fabricated biological scaffolds for tissue engineering

Nain, A. S., Chung, F., Rule, M., Jadlowiec, J. A., Campbell, P. G., Amon, C., Sitti, M.

In Robotics and Automation, 2007 IEEE International Conference on, pages: 1918-1923, 2007 (inproceedings)

pi

[BibTex]

[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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Bacterial flagella assisted propulsion of patterned latex particles: Effect of particle size

Behkam, B., Sitti, M.

In Nanotechnology, 2007. IEEE-NANO 2007. 7th IEEE Conference on, pages: 723-727, 2007 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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A scaled bilateral control system for experimental 1-D teleoperated nanomanipulation applications

Onal, C. D., Pawashe, C., Sitti, M.

In Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on, pages: 483-488, 2007 (inproceedings)

pi

[BibTex]

[BibTex]


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Guided Self-organisation for Autonomous Robot Development

Martius, G., Herrmann, J. M., Der, R.

In Advances in Artificial Life 9th European Conference, ECAL 2007, 4648, pages: 766-775, LNCS, Springer, 2007 (inproceedings)

al

[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)

mms

[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)

mms

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)

mms

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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Dissipative magnetization dynamics close to the adiabatic regime

Fähnle, M., Steiauf, D.

In Handbook of Magnetism and Advanced Magnetic Materials. Vol. 1: Fundamental and Theory, pages: 282-302, John Wiley & Sons Ltd., Chichester, UK, 2007 (incollection)

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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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]

2003


PostScript [BibTex]


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On the Complexity of Learning the Kernel Matrix

Bousquet, O., Herrmann, D.

In Advances in Neural Information Processing Systems 15, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors fix. This bound is only a logarithmic factor bigger than the complexity of the function class induced by a single kernel. However, optimizing the margin over such classes leads to overfitting. We thus propose a suitable way of constraining the class. We use an efficient algorithm to solve the resulting optimization problem, present preliminary experimental results, and compare them to an alignment-based approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Cluster Kernels for Semi-Supervised Learning

Chapelle, O., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 15, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Mismatch String Kernels for SVM Protein Classification

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

In Advances in Neural Information Processing Systems 15, pages: 1417-1424, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

In Advances in Neural Information Processing Systems 15, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

Franz, MO., Chahl, JS.

In Advances in Neural Information Processing Systems 15, pages: 1319-1326, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion 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 self-motion 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 turn out to be less reliable.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Clustering with the Fisher score

Tsuda, K., Kawanabe, M., Müller, K.

In Advances in Neural Information Processing Systems 15, pages: 729-736, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved in the space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithm specialized for the Fisher score, which can exploit important dimensions. This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Marginalized Kernels between Labeled Graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In 20th International Conference on Machine Learning, pages: 321-328, (Editors: Faucett, T. and N. Mishra), 20th International Conference on Machine Learning, August 2003 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Sparse Gaussian Processes: inference, subspace identification and model selection

Csato, L., Opper, M.

In Proceedings, pages: 1-6, (Editors: Van der Hof, , Wahlberg), The Netherlands, 13th IFAC Symposium on System Identifiaction, August 2003, electronical version; Index ThA02-2 (inproceedings)

Abstract
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent function. The inference is carried out using the Bayesian online learning and its extension to the more general iterative approach which we call TAP/EP learning. Sparsity is introduced in this context to make the TAP/EP method applicable to large datasets. We address the prohibitive scaling of the number of parameters by defining a subset of the training data that is used as the support the GP, thus the number of required parameters is independent of the training set, similar to the case of ``Support--‘‘ or ``Relevance--Vectors‘‘. An advantage of the full probabilistic treatment is that allows the computation of the marginal data likelihood or evidence, leading to hyper-parameter estimation within the GP inference. An EM algorithm to choose the hyper-parameters is proposed. The TAP/EP learning is the E-step and the M-step then updates the hyper-parameters. Due to the sparse E-step the resulting algorithm does not involve manipulation of large matrices. The presented algorithm is applicable to a wide variety of likelihood functions. We present results of applying the algorithm on classification and nonstandard regression problems for artificial and real datasets.

ei

PDF GZIP [BibTex]

PDF GZIP [BibTex]


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Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, CE., Girard, A.

In Proceedings of the 13th IFAC Symposium on System Identification, pages: 1195-1200, (Editors: Van den Hof, P., B. Wahlberg and S. Weiland), Proceedings of the 13th IFAC Symposium on System Identification, August 2003 (inproceedings)

Abstract
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

ei

PDF [BibTex]

PDF [BibTex]


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On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics

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

In Humanoids Proceedings, pages: 0-0, Humanoids Proceedings, July 2003, electronical version (inproceedings)

Abstract
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

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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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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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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A case based comparison of identification with neural network and Gaussian process models.

Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., Rasmussen, CE.

In Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003, 1, pages: 137-142, (Editors: Ruano, E.A.), Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS, April 2003 (inproceedings)

Abstract
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

ei

PDF [BibTex]

PDF [BibTex]


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On-Line One-Class Support Vector Machines. An Application to Signal Segmentation

Gretton, A., Desobry, ..

In IEEE ICASSP Vol. 2, pages: 709-712, IEEE ICASSP, April 2003 (inproceedings)

Abstract
In this paper, we describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.

ei

PostScript [BibTex]

PostScript [BibTex]