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2016


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Localized domain wall nucleation dynamics in asymmetric ferromagnetic rings revealed by direct time-resolved magnetic imaging

Richter, K., Krone, A., Mawass, M., Krüger, B., Weigand, M., Stoll, H., Schütz, G., Kläui, M.

{Physical Review B}, 94(2), American Physical Society, Woodbury, NY, 2016 (article)

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DOI [BibTex]

2016


DOI [BibTex]


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Observation of room-temperature magnetic skyrmions and their current-driven dynamics in ultrathin metallic ferromagnets

Woo, S., Litzius, K., Krüger, B., Im, M., Caretta, L., Richter, K., Mann, M., Krone, A., Reeve, R. M., Weigand, M., Agrawal, P., Lemesh, I., Mawass, M., Fischer, P., Kläui, M., Beach, G. S. D.

{Nature Materials}, 15(5):501-506, Nature Pub. Group, London, UK, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context: Experiments and an Information-Theoretic Ambiguity Model

Grau-Moya, J, Ortega, PA, Braun, DA

PLoS ONE, 11(4):1-21, April 2016 (article)

Abstract
A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects’ choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects’ choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain.

ei

DOI [BibTex]


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Outlook and challenges for hydrogen storage in nanoporous materials

Broom, D. P., Webb, C. J., Hurst, K. E., Parilla, P. A., Gennett, T., Brown, C. M., Zacharia, R., Tylianakis, E., Klontzas, E., Froudakis, G. E., Steriotis, T. A., Trikalitis, P. N., Anton, D. L., Hardy, B., Tamburello, D., Corgnale, C., van Hassel, B. A., Cossement, D., Chahine, R., Hirscher, M.

{Applied Physics A}, 122(3), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Quantum sieving for separation of hydrogen isotopes using MOFs

Oh, H., Hirscher, M.

{European Journal of Inorganic Chemistry}, 2016(27):4278-4289, Wiley-VCH, Weinheim, Germany, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Direct patterning of vortex generators on a fiber tip using a focused ion beam

Vayalamkuzhi, P., Bhattacharya, S., Eigenthaler, U., Keskinbora, K., Salman, C. T., Hirscher, M., Spatz, J. P., Viswanathan, N. K.

{Optics Letters}, 41(10):2133-2136, Optical Society of America, Washington, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Two-body problem of core-region coupled magnetic vortex stacks

Hänze, M., Adolff, C. F., Velten, S., Weigand, M., Meier, G.

{Physical Review B}, 93(5), American Physical Society, Woodbury, NY, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Irreproducibility in hydrogen storage material research

Broom, D. P., Hirscher, M.

{Energy \& Environmental Science}, 9(11):3368-3380, Royal Society of Chemistry, Cambridge, UK, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Effect of surface configurations on the room-temperature magnetism of pure ZnO

Chen, Y., Wang, Z., Leineweber, A., Baier, J., Tietze, T., Phillipp, F., Schütz, G., Goering, E.

{Journal of Materials Chemistry C}, 4(19):4166-4175, Royal Society of Chemistry, London, UK, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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On the synthesis and microstructure analysis of high performance MnBi

Chen, Y., Sawatzki, S., Ener, S., Sepehri-Amin, H., Leineweber, A., Gregori, G., Qu, F., Muralidhar, S., Ohkubo, T., Hono, K., Gutfleisch, O., Kronmüller, H., Schütz, G., Goering, E.

{AIP Advances}, 6(12), 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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The role of individual defects on the magnetic screening of HTSC films

Ruoß, S., Stahl, C., Weigand, M., Zahn, P., Bayer, J., Schütz, G., Albrecht, J.

{New Journal of Physics}, 18(10), IOP Publishing, Bristol, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic switching of nanoscale antidot lattices

Wiedwald, U., Gräfe, J., Lebecki, K. M., Skripnik, M., Haering, F., Schütz, G., Ziemann, P., Goering, E., Nowak, U.

{Beilstein Journal of Nanotechnology}, 7, pages: 733-750, Beilstein-Institut, Frankfurt am Main, 2016 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Hydrogen-based energy storage (IEA-HIA Task 32)

Buckley, C. E., Chen, P., van Hassel, B. A., Hirscher, M.

{Applied Physics A}, 122(2), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Local domain-wall velocity engineering via tailored potential landscapes in ferromagnetic rings

Richter, K., Krone, A., Mawass, M., Krüger, B., Weigand, M., Stoll, H., Schütz, G., Kläui, M.

{Physical Review Applied}, 5(2), American Physical Society, College Park, Md. [u.a.], 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Geometric control of the magnetization reversal in antidot lattices with perpendicular magnetic anisotropy

Gräfe, J., Weigand, M., Träger, N., Schütz, G., Goering, E. J., Skripnik, M., Nowak, U., Haering, F., Ziemann, P., Wiedwald, U.

{Physical Review B}, 93(10), American Physical Society, Woodbury, NY, 2016 (article)

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DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Growth and characterizationof large weak topological insulator Bi2Tel single crystal by Bismuth self-flux method

Ryu, G., Son, K., Schütz, G.

{Journal of Crystal Growth}, 440, pages: 26-30, North-Holland, Amsterdam, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Additive interfacial chiral interaction in multilayers for stabilization of small individual skyrmions at room temperature

Moreau-Luchaire, C., Moutafis, C., Reyren, N., Sampaio, J., Vaz, C. A. F., Van Horne, N., Bouzehouane, K., Garcia, K., Deranlot, C., Warnicke, P., Wohlhüter, P., George, J.-M., Weigand, M., Raabe, J., Cros, V., Fert, A.

{Nature Nanotechnology}, 11(5):444-448, Nature Publishing Group, London, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Surface defect free growth of a spin dimer TlCuCl3 compound crystals and investigations on its optical and magnetic properties

Ryu, G., Son, K.

{Journal of Solid State Chemistry}, 237, pages: 358-363, Academic Press, Orlando, Fla., 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Physical and mathematical justification of the numerical Brillouin zone integration of the Boltzmann rate equation by Gaussian smearing

Illg, C., Haag, M., Teeny, N., Wirth, J., Fähnle, M.

{Journal of Theoretical and Applied Physics}, 10(1):1-6, Springer, Berlin, Heidelberg, Tehran, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Pinned orbital moments - A new contribution to magnetic anisotropy

Audehm, P., Schmidt, M., Brück, S., Tietze, T., Gräfe, J., Macke, S., Schütz, G., Goering, E.

{Scientific Reports}, 6, Nature Publishing Group, London, UK, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Comparative study of ALD SiO2 thin films for optical applications

Pfeiffer, K., Shestaeva, S., Bingel, A., Munzert, P., Ghazaryan, L., van Helvoirt, C., Kessels, W. M. M., Sanli, U. T., Grévent, C., Schütz, G., Putkonen, M., Buchanan, I., Jensen, L., Ristau, D., Tünnermann, A., Szeghalmi, A.

{Optical materials express}, 6(2):660-670, OSA, Washington, DC, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Combined first-order reversal curve and x-ray microscopy investigation of magnetization reversal mechanisms in hexagonal antidot lattices

Gräfe, J., Weigand, M., Stahl, C., Träger, N., Kopp, M., Schütz, G., Goering, E. J., Haering, F., Ziemann, P., Wiedwald, U.

{Physical Review B}, 93(1), American Physical Society, Woodbury, NY, 2016 (article)

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DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Switching probabilities of magnetic vortex core reversal studied by table top magneto optic Kerr microscopy

Dieterle, G., Gangwar, A., Gräfe, J., Noske, M., Förster, J., Woltersdorf, G., Stoll, H., Back, C. H., Schütz, G.

{Applied Physics Letters}, 108(2), American Institute of Physics, Melville, NY, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Ultrafast demagnetization after femtosecond laser pulses: Transfer of angular momentum from the electronic system to magnetoelastic spin-phonon modes

Tsatsoulis, T., Illg, C., Haag, M., Müller, B. Y., Zhang, L., Fähnle, M.

{Physical Review B}, 93(13), American Physical Society, Woodbury, NY, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Developments in the Ni-Nb-Zr amorphous alloy membranes

Sarker, S., Chandra, D., Hirscher, M., Dolan, M., Isheim, D., Wermer, J., Viano, D., Baricco, M., Udovic, T. J., Grant, D., Palumbo, O., Paolone, A., Cantelli, R.

{Applied Physics A}, 122(3), Springer-Verlag Heidelberg, Heidelberg, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Resistance to the transport of H2 through the external surface of as-made and modified silicalite-1 (MFI)

Kalantzopoulos, G. N., Policicchio, A., Maccallini, E., Krkljus, I., Ciuchi, F., Hirscher, M., Agostino, R. G., Golemme, G.

{Microporous and Mesoporous Materials}, 220, pages: 290-297, Elsevier, Amsterdam, 2016 (article)

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DOI [BibTex]

DOI [BibTex]


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Observation of pseudopartial grain boundary wetting in the NdFeB-based alloy

Straumal, B. B., Mazilkin, A. A., Protasova, S. G., Schütz, G., Straumal, A. B., Baretzky, B.

{Journal of Materials Engineering and Performance}, 25(8):3303-3309, 2016 (article)

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DOI [BibTex]

DOI [BibTex]

2005


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Kernel Methods for Measuring Independence

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

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

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

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

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

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

ei

PDF [BibTex]

PDF [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

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

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

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Selective integration of multiple biological data for supervised network inference

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

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

ei

PDF [BibTex]

PDF [BibTex]


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Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

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

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

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

Abstract
Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

ei

PDF [BibTex]

PDF [BibTex]


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Support Vector Machines for 3D Shape Processing

Steinke, F., Schölkopf, B., Blanz, V.

Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

Abstract
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

ei

PDF [BibTex]

PDF [BibTex]


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Fast Protein Classification with Multiple Networks

Tsuda, K., Shin, H., Schölkopf, B.

Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

Abstract
Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced Lanckriet et al (2004). In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n^3), and produces a dense matrix of size n x n. We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similarly to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Abstract
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Phenotypic characterization of chondrosarcoma-derived cell lines

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T.

Cancer Letters, 226(2):143-154, August 2005 (article)

Abstract
Gene expression profiling of three chondrosarcoma derived cell lines (AD, SM, 105KC) showed an increased proliferative activity and a reduced expression of chondrocytic-typical matrix products compared to primary chondrocytes. The incapability to maintain an adequate matrix synthesis as well as a notable proliferative activity at the same time is comparable to neoplastic chondrosarcoma cells in vivo which cease largely cartilage matrix formation as soon as their proliferative activity increases. Thus, the investigated cell lines are of limited value as substitute of primary chondrocytes but might have a much higher potential to investigate the behavior of neoplastic chondrocytes, i.e. chondrosarcoma biology.

ei

Web [BibTex]

Web [BibTex]


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Local Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

Abstract
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Learning the Kernel with Hyperkernels

Ong, CS., Smola, A., Williamson, R.

Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

Abstract
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Image Reconstruction by Linear Programming

Tsuda, K., Rätsch, G.

IEEE Transactions on Image Processing, 14(6):737-744, June 2005 (article)

Abstract
One way of image denoising is to project a noisy image to the subspace of admissible images derived, for instance, by PCA. However, a major drawback of this method is that all pixels are updated by the projection, even when only a few pixels are corrupted by noise or occlusion. We propose a new method to identify the noisy pixels by l1-norm penalization and to update the identified pixels only. The identification and updating of noisy pixels are formulated as one linear program which can be efficiently solved. In particular, one can apply the upsilon trick to directly specify the fraction of pixels to be reconstructed. Moreover, we extend the linear program to be able to exploit prior knowledge that occlusions often appear in contiguous blocks (e.g., sunglasses on faces). The basic idea is to penalize boundary points and interior points of the occluded area differently. We are also able to show the upsilon property for this extended LP leading to a method which is easy to use. Experimental results demonstrate the power of our approach.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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RASE: recognition of alternatively spliced exons in C.elegans

Rätsch, G., Sonnenburg, S., Schölkopf, B.

Bioinformatics, 21(Suppl. 1):i369-i377, June 2005 (article)

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection

Tsuda, K., Rätsch, G., Warmuth, M.

Journal of Machine Learning Research, 6, pages: 995-1018, June 2005 (article)

Abstract
We address the problem of learning a symmetric positive definite matrix. The central issue is to design parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gradient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive definite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.

ei

PDF [BibTex]

PDF [BibTex]


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Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Journal of the Optical Society of America A, 22(5):801-809, May 2005 (article)

Abstract
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian inference for psychometric functions

Kuss, M., Jäkel, F., Wichmann, F.

Journal of Vision, 5(5):478-492, May 2005 (article)

Abstract
In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer’s ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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A gene expression map of Arabidopsis thaliana development

Schmid, M., Davison, T., Henz, S., Pape, U., Demar, M., Vingron, M., Schölkopf, B., Weigel, D., Lohmann, J.

Nature Genetics, 37(5):501-506, April 2005 (article)

Abstract
Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found in those of animals. Plants therefore provide an opportunity to study how transcriptional programs control multicellular development. We analyzed global gene expression during development of the reference plant Arabidopsis thaliana in samples covering many stages, from embryogenesis to senescence, and diverse organs. Here, we provide a first analysis of this data set, which is part of the AtGenExpress expression atlas. We observed that the expression levels of transcription factor genes and signal transduction components are similar to those of metabolic genes. Examining the expression patterns of large gene families, we found that they are often more similar than would be expected by chance, indicating that many gene families have been co-opted for specific developmental processes.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Experimentally optimal v in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., Smola, A.

Neural Networks, 18(2):205-205, March 2005 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Composite adaptive control with locally weighted statistical learning

Nakanishi, J., Farrell, J. A., Schaal, S.

Neural Networks, 18(1):71-90, January 2005, clmc (article)

Abstract
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.

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link (url) [BibTex]

link (url) [BibTex]


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Invariance of Neighborhood Relation under Input Space to Feature Space Mapping

Shin, H., Cho, S.

Pattern Recognition Letters, 26(6):707-718, 2005 (article)

Abstract
If the training pattern set is large, it takes a large memory and a long time to train support vector machine (SVM). Recently, we proposed neighborhood property based pattern selection algorithm (NPPS) which selects only the patterns that are likely to be near the decision boundary ahead of SVM training [Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence (LNAI 2637), Seoul, Korea, pp. 376–387]. NPPS tries to identify those patterns that are likely to become support vectors in feature space. Preliminary reports show its effectiveness: SVM training time was reduced by two orders of magnitude with almost no loss in accuracy for various datasets. It has to be noted, however, that decision boundary of SVM and support vectors are all defined in feature space while NPPS described above operates in input space. If neighborhood relation in input space is not preserved in feature space, NPPS may not always be effective. In this paper, we sh ow that the neighborhood relation is invariant under input to feature space mapping. The result assures that the patterns selected by NPPS in input space are likely to be located near decision boundary in feature space.

ei

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PDF PDF [BibTex]


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Theory of Classification: A Survey of Some Recent Advances

Boucheron, S., Bousquet, O., Lugosi, G.

ESAIM: Probability and Statistics, 9, pages: 323 , 2005 (article)

Abstract
The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have lead to these important recent developments.

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Moment Inequalities for Functions of Independent Random Variables

Boucheron, S., Bousquet, O., Lugosi, G., Massart, P.

To appear in Annals of Probability, 33, pages: 514-560, 2005 (article)

Abstract
A general method for obtaining moment inequalities for functions of independent random variables is presented. It is a generalization of the entropy method which has been used to derive concentration inequalities for such functions cite{BoLuMa01}, and is based on a generalized tensorization inequality due to Lata{l}a and Oleszkiewicz cite{LaOl00}. The new inequalities prove to be a versatile tool in a wide range of applications. We illustrate the power of the method by showing how it can be used to effortlessly re-derive classical inequalities including Rosenthal and Kahane-Khinchine-type inequalities for sums of independent random variables, moment inequalities for suprema of empirical processes, and moment inequalities for Rademacher chaos and $U$-statistics. Some of these corollaries are apparently new. In particular, we generalize Talagrands exponential inequality for Rademacher chaos of order two to any order. We also discuss applications for other complex functions of independent random variables, such as suprema of boolean polynomials which include, as special cases, subgraph counting problems in random graphs.

ei

PDF [BibTex]

PDF [BibTex]