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2011


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Applications of AFM Based Nanorobotic Systems

Xie, H., Onal, C., Régnier, S., Sitti, M.

In Atomic Force Microscopy Based Nanorobotics, pages: 313-342, Springer Berlin Heidelberg, 2011 (incollection)

pi

[BibTex]

2011


[BibTex]


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Modeling of stochastic motion of bacteria propelled spherical microbeads

Arabagi, V., Behkam, B., Cheung, E., Sitti, M.

Journal of Applied Physics, 109(11):114702, AIP, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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The effect of aspect ratio on adhesion and stiffness for soft elastic fibres

Aksak, B., Hui, C., Sitti, M.

Journal of The Royal Society Interface, 8(61):1166-1175, The Royal Society, 2011 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

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publisher site [BibTex]

publisher site [BibTex]


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Large hidden orbital moments in magnetite

Goering, E.

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

Streppel, B., Hirscher, M.

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Nanomechanics of AFM based nanomanipulation

Xie, H., Onal, C., Régnier, S., Sitti, M.

In Atomic Force Microscopy Based Nanorobotics, pages: 87-143, Springer Berlin Heidelberg, 2011 (incollection)

pi

[BibTex]

[BibTex]


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

Cheung, E., Sitti, M.

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

pi

Project Page [BibTex]

Project Page [BibTex]


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

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

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

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link (url) [BibTex]

link (url) [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Electron theory of magnetoelectric effects in metallic ferromagnetic nanostructures

Subkow, S., Fähnle, M.

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

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DOI [BibTex]

DOI [BibTex]


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Magnetic antivortex-core reversal by rotating magnetic fields

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

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

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DOI [BibTex]

DOI [BibTex]


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Magnetic properties of exchange-spring composite films

Kronmüller, H., Goll, D.

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Instrumentation Issues of an AFM Based Nanorobotic System

Xie, H., Onal, C., Régnier, S., Sitti, M.

In Atomic Force Microscopy Based Nanorobotics, pages: 31-86, Springer Berlin Heidelberg, 2011 (incollection)

pi

[BibTex]

[BibTex]


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Piezoelectric polymer fiber arrays for tactile sensing applications

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

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

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Project Page [BibTex]

Project Page [BibTex]


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

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

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

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[BibTex]

[BibTex]


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Projected Newton-type methods in machine learning

Schmidt, M., Kim, D., Sra, S.

In Optimization for Machine Learning, pages: 305-330, MIT Press, Cambridge, MA, USA, 2011 (incollection)

Abstract
{We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.}

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link (url) [BibTex]

link (url) [BibTex]


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

Bublat, T., Goll, D.

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

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DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Spin interactions in bcc and fcc Fe beyond the Heisenberg model

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Influence of texture on the ferromagnetic properties of nanograined ZnO films

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

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

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

mms

DOI [BibTex]

DOI [BibTex]

2006


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Structure validation of the Josephin domain of ataxin-3: Conclusive evidence for an open conformation

Nicastro, G., Habeck, M., Masino, L., Svergun, DI., Pastore, A.

Journal of Biomolecular NMR, 36(4):267-277, December 2006 (article)

Abstract
The availability of new and fast tools in structure determination has led to a more than exponential growth of the number of structures solved per year. It is therefore increasingly essential to assess the accuracy of the new structures by reliable approaches able to assist validation. Here, we discuss a specific example in which the use of different complementary techniques, which include Bayesian methods and small angle scattering, resulted essential for validating the two currently available structures of the Josephin domain of ataxin-3, a protein involved in the ubiquitin/proteasome pathway and responsible for neurodegenerative spinocerebellar ataxia of type 3. Taken together, our results demonstrate that only one of the two structures is compatible with the experimental information. Based on the high precision of our refined structure, we show that Josephin contains an open cleft which could be directly implicated in the interaction with polyubiquitin chains and other partners.

ei

Web DOI [BibTex]

2006


Web DOI [BibTex]


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A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression

Franz, M., Schölkopf, B.

Neural Computation, 18(12):3097-3118, December 2006 (article)

Abstract
Volterra and Wiener series are perhaps the best understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estimated. We show that Volterra and Wiener series can be represented implicitly as elements of a reproducing kernel Hilbert space by utilizing polynomial kernels. The estimation complexity of the implicit representation is linear in the input dimensionality and independent of the degree of nonlinearity. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Prediction of Protein Function from Networks

Shin, H., Tsuda, K.

In Semi-Supervised Learning, pages: 361-376, Adaptive Computation and Machine Learning, (Editors: Chapelle, O. , B. Schölkopf, A. Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
In computational biology, it is common to represent domain knowledge using graphs. Frequently there exist multiple graphs for the same set of nodes, representing information from different sources, and no single graph is sufficient to predict class labels of unlabelled nodes reliably. One way to enhance reliability is to integrate multiple graphs, since individual graphs are partly independent and partly complementary to each other for prediction. In this chapter, we describe an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins.When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph.When compared with the semidefinite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, our method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

ei

Web [BibTex]

Web [BibTex]


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Discrete Regularization

Zhou, D., Schölkopf, B.

In Semi-supervised Learning, pages: 237-250, Adaptive computation and machine learning, (Editors: O Chapelle and B Schölkopf and A Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
Many real-world machine learning problems are situated on finite discrete sets, including dimensionality reduction, clustering, and transductive inference. A variety of approaches for learning from finite sets has been proposed from different motivations and for different problems. In most of those approaches, a finite set is modeled as a graph, in which the edges encode pairwise relationships among the objects in the set. Consequently many concepts and methods from graph theory are adopted. In particular, the graph Laplacian is widely used. In this chapter we present a systemic framework for learning from a finite set represented as a graph. We develop discrete analogues of a number of differential operators, and then construct a discrete analogue of classical regularization theory based on those discrete differential operators. The graph Laplacian based approaches are special cases of this general discrete regularization framework. An important thing implied in this framework is that we have a wide choices of regularization on graph in addition to the widely-used graph Laplacian based one.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Statistical Analysis of Slow Crack Growth Experiments

Pfingsten, T., Glien, K.

Journal of the European Ceramic Society, 26(15):3061-3065, November 2006 (article)

Abstract
A common approach for the determination of Slow Crack Growth (SCG) parameters are the static and dynamic loading method. Since materials with small Weibull module show a large variability in strength, a correct statistical analysis of the data is indispensable. In this work we propose the use of the Maximum Likelihood method and a Baysian analysis, which, in contrast to the standard procedures, take into account that failure strengths are Weibull distributed. The analysis provides estimates for the SCG parameters, the Weibull module, and the corresponding confidence intervals and overcomes the necessity of manual differentiation between inert and fatigue strength data. We compare the methods to a Least Squares approach, which can be considered the standard procedure. The results for dynamic loading data from the glass sealing of MEMS devices show that the assumptions inherent to the standard approach lead to significantly different estimates.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Mining frequent stem patterns from unaligned RNA sequences

Hamada, M., Tsuda, K., Kudo, T., Kin, T., Asai, K.

Bioinformatics, 22(20):2480-2487, October 2006 (article)

Abstract
Motivation: In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly. Results: Our method RNAmine employs a graph theoretic representation of RNA sequences, and detects all the possible motifs exhaustively using a graph mining algorithm. The motif detection problem boils down to finding frequently appearing patterns in a set of directed and labeled graphs. In the tasks of common secondary structure prediction and local motif detection from long sequences, our method performed favorably both in accuracy and in efficiency with the state-of-the-art methods such as CMFinder.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Large-Scale Gene Expression Profiling Reveals Major Pathogenetic Pathways of Cartilage Degeneration in Osteoarthritis

Aigner, T., Fundel, K., Saas, J., Gebhard, P., Haag, J., Weiss, T., Zien, A., Obermayr, F., Zimmer, R., Bartnik, E.

Arthritis and Rheumatism, 54(11):3533-3544, October 2006 (article)

Abstract
Objective. Despite many research efforts in recent decades, the major pathogenetic mechanisms of osteo- arthritis (OA), including gene alterations occurring during OA cartilage degeneration, are poorly under- stood, and there is no disease-modifying treatment approach. The present study was therefore initiated in order to identify differentially expressed disease-related genes and potential therapeutic targets. Methods. This investigation consisted of a large gene expression profiling study performed based on 78 normal and disease samples, using a custom-made complementar y DNA array covering >4,000 genes. Results. Many differentially expressed genes were identified, including the expected up-regulation of ana- bolic and catabolic matrix genes. In particular, the down-regulation of important oxidative defense genes, i.e., the genes for superoxide dismutases 2 and 3 and glutathione peroxidase 3, was prominent. This indicates that continuous oxidative stress to the cells and the matrix is one major underlying pathogenetic mecha- nism in OA. Also, genes that are involved in the phenot ypic stabilit y of cells, a feature that is greatly reduced in OA cartilage, appeared to be suppressed. Conclusion. Our findings provide a reference data set on gene alterations in OA cartilage and, importantly, indicate major mechanisms underlying central cell bio- logic alterations that occur during the OA disease process. These results identify molecular targets that can be further investigated in the search for therapeutic interventions.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

Walder, C., Schölkopf, B., Chapelle, O.

Computer Graphics Forum, 25(3):635-644, September 2006 (article)

Abstract
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The contributions of the paper include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable interpolation properties previously only associated with fully supported bases. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem lying at the core of kernel-based machine learning methods. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data and four-dimensional interpolation between three-dimensional shapes.

ei

PDF GZIP DOI [BibTex]


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An Online Support Vector Machine for Abnormal Events Detection

Davy, M., Desobry, F., Gretton, A., Doncarli, C.

Signal Processing, 86(8):2009-2025, August 2006 (article)

Abstract
The ability to detect online abnormal events in signals is essential in many real-world Signal Processing applications. Previous algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. Corresponding implementation relies on maximum likelihood or on Bayes estimation theory with generally excellent performance. However, there are numerous cases where a robust and tractable model cannot be obtained, and model-free approaches need to be considered. In this paper, we investigate a machine learning, descriptor-based approach that does not require an explicit descriptors statistical model, based on Support Vector novelty detection. A sequential optimization algorithm is introduced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.

ei

PDF PostScript PDF DOI [BibTex]

PDF PostScript PDF DOI [BibTex]


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Integrating Structured Biological data by Kernel Maximum Mean Discrepancy

Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H., Schölkopf, B., Smola, A.

Bioinformatics, 22(4: ISMB 2006 Conference Proceedings):e49-e57, August 2006 (article)

Abstract
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology. Results: We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors. Conclusions: We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Large Scale Transductive SVMs

Collobert, R., Sinz, F., Weston, J., Bottou, L.

Journal of Machine Learning Research, 7, pages: 1687-1712, August 2006 (article)

Abstract
We show how the Concave-Convex Procedure can be applied to the optimization of Transductive SVMs, which traditionally requires solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach.

ei

PostScript PDF PDF [BibTex]

PostScript PDF PDF [BibTex]


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Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S., Chapelle, O., DeCoste, D.

Journal of Machine Learning Research, 7, pages: 1493-1515, July 2006 (article)

Abstract
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size ($dmax$) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as $O(ndmax^2)$ where $n$ is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

ei

PDF [BibTex]

PDF [BibTex]