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2009


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Improvement of interface structure and magnetic properties of Co on Si (100) by surfactant (Sb) mediated growth

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

{Applied Physics A}, 97(3):651-656, 2009 (article)

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DOI [BibTex]

2009


DOI [BibTex]


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Effect of severe plastic deformation on the coercivity of Co-Cu alloys

Straumal, B. B., Protasova, S. G., Mazilkin, A. A., Baretzky, B., Goll, D., Gunderov, D. V., Valiev, R. Z.

{Philosophical Magazine Letters}, 89(10):649-654, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic properties of cobalt-covered MgB2 films

Treiber, S., Stuhlhofer, B., Habermeier, H.-U., Albrecht, J.

{Superconductor Science and Technology}, 22, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Reconstruction of historic alloys for pipe organs brings true Baroque music back to life

Baretzky, B., Friesel, M., Straumal, B.

{Japan Organist}, 36, pages: 29-38, 2009 (article)

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[BibTex]

[BibTex]


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Exchange-coupled L10-FePt/Fe composite patterns with perpendicular magnetization

Breitling, A., Bublat, T., Goll, D.

{Physica Status Solidi - Rapid Research Letters}, 3(5):130-132, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Proton NMR studies of the NaAlH4 structure

Valiente-Banuet, L. E., Majer, G., Müller, K.

{Journal of Magnetic Resonance}, 200, pages: 280-284, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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A scaled bilateral control system for experimental one-dimensional teleoperated nanomanipulation

Onal, C. D., Sitti, M.

The International Journal of Robotics Research, 28(4):484-497, Sage Publications, 2009 (article)

pi

[BibTex]

[BibTex]


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A Swallowable Tethered Capsule Endoscope for Diagnosing Barrett’s Esophagus

Glass, P., Sitti, M., Pennathur, A., Appasamy, R.

Gastrointestinal Endoscopy, 69(5):AB106, Mosby, 2009 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Adhesion of biologically inspired polymer microfibers on soft surfaces

Cheung, E., Sitti, M.

Langmuir, 25(12):6613-6616, ACS Publications, 2009 (article)

pi

[BibTex]

[BibTex]


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Dangling chain elastomers as repeatable fibrillar adhesives

Sitti, M., Cusick, B., Aksak, B., Nese, A., Lee, H., Dong, H., Kowalewski, T., Matyjaszewski, K.

ACS applied materials \& interfaces, 1(10):2277-2287, American Chemical Society, 2009 (article)

pi

[BibTex]

[BibTex]


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A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior

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

Algorithms, 2(1):398-409, 2009 (article)

Abstract
Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short timescales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer timescales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot. By interacting with the real physical system formed by the robotic hardware and the environment, the controller achieves a sensitive and body-specific actuation of the robot.

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link (url) [BibTex]

link (url) [BibTex]


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A Curvature-Based Approach for Left Ventricular Shape Analysis from Cardiac Magnetic Resonance Imaging

Si Yong Yeo, Liang Zhong, Yi Su, Ru San Tan, Dhanjoo Ghista

Medical & Biological Engineering & Computing, 47(3):313-322, 2009 (article)

Abstract
It is believed that left ventricular (LV) regional shape is indicative of LV regional function, and cardiac pathologies are often associated with regional alterations in ventricular shape. In this article, we present a set of procedures for evaluating regional LV surface shape from anatomically accurate models reconstructed from cardiac magnetic resonance (MR) images. LV surface curvatures are computed using local surface fitting method, which enables us to assess regional LV shape and its variation. Comparisons are made between normal and diseased hearts. It is illustrated that LV surface curvatures at different regions of the normal heart are higher than those of the diseased heart. Also, the normal heart experiences a larger change in regional curvedness during contraction than the diseased heart. It is believed that with a wide range of dataset being evaluated, this approach will provide a new and efficient way of quantifying LV regional function.

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link (url) [BibTex]

link (url) [BibTex]


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Metal-Organic Frameworks

Panella, B., Hirscher, M.

In Encyclopedia of Electrochemical Power Sources, pages: 493-496, Elsevier, Amsterdam [et al.], 2009 (incollection)

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[BibTex]

[BibTex]


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Magnetism of FePt surface alloys

Honolka, J., Lee, T. Y., Kuhnke, K., Enders, A., Skomski, R., Bornemann, S., Mankovsky, S., Minár, J., Staunton, J., Ebert, H., Hessler, M., Fauth, K., Schütz, G., Buchsbaum, A., Schmid, M., Varga, P., Kern, K.

{Physical Review Letters}, 102(6), 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Enhanced 95Zr diffusion in grain boundaries of nano-crystalline ZrO2 \mbox⋅ 9.5 mol\textpercent Y2O3

Drings, H., Brossmann, U., Carstanjen, H. D., Szökefalvi-Nagy, A., Noll, C., Schaefer, H.-E.

{Physica Status Solidi (A)}, 206(1):54-58, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetism of nanostructured materials for advanced magnetic recording

Goll, D.

{International Journal of Materials Research}, 100, pages: 652-662, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Vortex core switching by coherent excitation with single in-plane magnetic field pulses

Weigand, M., van Waeyenberge, B., Vansteenkiste, A., Curcic, M., Sackmann, V., Stoll, H., Tyliszczak, T., Kaznatcheev, K., Bertwistle, D., Woltersdorf, G., Back, C. H., Schütz, G.

{Physical Review Letters}, 102, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Increase of Mn solubility with decreasing grain size in ZnO

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

{Journal of the European Ceramic Society}, 29(10):1963-1970, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Fe-C nanograined alloys obtained by high-pressure torsion: Structure and magnetic properties

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

{Materials Science and Engineering A}, 503, pages: 185-189, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Chiral symmetry breaking of magnetic vortices by sample roughness

Vansteenkiste, A., Weigand, M., Curcic, M., Stoll, H., Schütz, G., Van Waeyenberge, B.

{New Journal of Physics}, 11, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Extended s-d model for magnetization dynamics of strongly noncollinear configurations

De Angeli, L., Steiauf, D., Singer, R., Köberle, I., Dietermann, F., Fähnle, M.

{Physical Review B}, 79, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Incorporating Muscle Activation-Contraction dynamics to an optimal control framework for finger movements

Theodorou, Evangelos A., Valero-Cuevas, Francisco J.

Abstracts of Neural Control of Movement Conference (NCM 2009), 2009, clmc (article)

Abstract
Recent experimental and theoretical work [1] investigated the neural control of contact transition between motion and force during tapping with the index finger as a nonlinear optimization problem. Such transitions from motion to well-directed contact force are a fundamental part of dexterous manipulation. There are 3 alternative hypotheses of how this transition could be accomplished by the nervous system as a function of changes in direction and magnitude of the torque vector controlling the finger. These hypotheses are 1) an initial change in direction with a subsequent change in magnitude of the torque vector; 2) an initial change in magnitude with a subsequent directional change of the torque vector; and 3) a simultaneous and proportionally equal change of both direction and magnitude of the torque vector. Experimental work in [2] shows that the nervous system selects the first strategy, and in [1] we suggest that this may in fact be the optimal strategy. In [4] the framework of Iterative Linear Quadratic Optimal Regulator (ILQR) was extended to incorporate motion and force control. However, our prior simulation work assumed direct and instantaneous control of joint torques, which ignores the known delays and filtering properties of skeletal muscle. In this study, we implement an ILQR controller for a more biologically plausible biomechanical model of the index finger than [4], and add activation-contraction dynamics to the system to simulate muscle function. The planar biomechanical model includes the kinematics of the 3 joints while the applied torques are driven by activation?contraction dynamics with biologically plausible time constants [3]. In agreement with our experimental work [2], the task is to, within 500 ms, move the finger from a given resting configuration to target configuration with a desired terminal velocity. ILQR does not only stabilize the finger dynamics according to the objective function, but it also generates smooth joint space trajectories with minimal tuning and without an a-priori initial control policy (which is difficult to find for highly dimensional biomechanical systems). Furthemore, the use of this optimal control framework and the addition of activation-contraction dynamics considers the full nonlinear dynamics of the index finger and produces a sequence of postures which are compatible with experimental motion data [2]. These simulations combined with prior experimental results suggest that optimal control is a strong candidate for the generation of finger movements prior to abrupt motion-to-force transitions. This work is funded in part by grants NIH R01 0505520 and NSF EFRI-0836042 to Dr. Francisco J. Valero- Cuevas 1 Venkadesan M, Valero-Cuevas FJ. 
Effects of neuromuscular lags on controlling contact transitions. 
Philosophical Transactions of the Royal Society A: 2008. 2 Venkadesan M, Valero-Cuevas FJ. 
Neural Control of Motion-to-Force Transitions with the Fingertip. 
J. Neurosci., Feb 2008; 28: 1366 - 1373; 3 Zajac. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng, 17 4. Weiwei Li., Francisco Valero Cuevas: ?Linear Quadratic Optimal Control of Contact Transition with Fingertip ? ACC 2009

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PDF [BibTex]

PDF [BibTex]


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On-line learning and modulation of periodic movements with nonlinear dynamical systems

Gams, A., Ijspeert, A., Schaal, S., Lenarčič, J.

Autonomous Robots, 27(1):3-23, 2009, clmc (article)

Abstract
Abstract  The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

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link (url) [BibTex]

link (url) [BibTex]


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Reversible dry micro-fibrillar adhesives with thermally controllable adhesion

Kim, S., Sitti, M., Xie, T., Xiao, X.

Soft Matter, 5(19):3689-3693, Royal Society of Chemistry, 2009 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Enhanced reversible adhesion of dopamine methacrylamide-coated elastomer microfibrillar structures under wet conditions

Glass, P., Chung, H., Washburn, N. R., Sitti, M.

Langmuir, 25(12):6607-6612, ACS Publications, 2009 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Carbon Materials

Hirscher, M.

In Encyclopedia of Electrochemical Power Sources, pages: 484-487, Elsevier, Amsterdam [et al.], 2009 (incollection)

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[BibTex]

[BibTex]


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Superconducting phase formation in random neck syntheses: a study of the Y-Ba-Cu-O system by magneto-optics and magnetometry

Willems, J. B., Albrecht, J., Landau, I. L., Hulliger, J.

{Superconductor Science and Technology}, 22, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Determination of spin moments from magnetic EXAFS

Popescu, V., Gü\ssmann, M., Fähnle, M., Schütz, G.

{Physical Review B}, 79, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Linewidth of ferromagnetic resonance for systems with anisotropic damping

Seib, J., Steiauf, D., Fähnle, M.

{Physical Review B}, 79, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Structural and magnetic deconvolution of FePt/FeOx-nanoparticles using x-ray magnetic circular dichroism

Nolle, D., Goering, E., Tietze, T., Schütz, G., Figuerola, A., Manna, L.

{New Journal of Physics}, 11, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic imaging with femtosecond temporal resolution

Li, J., Lee, M.-S., He, W., Redeker, B., Remhof, A., Amaladass, E., Hassel, C., Eimüller, T.

{Review of Scientific Instruments}, 80(7), 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Elliott-Yafet mechanism and the discussion of femtosecond magnetization dynamics

Steiauf, D., Fähnle, M.

{Physical Review B}, 79, 2009 (article)

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DOI [BibTex]

DOI [BibTex]


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Investigation of the stability of Mn12 single molecule magnets

Voss, S., Fonin, M., Burova, L., Burgert, M., Dedkov, Y. S., Preobrajenski, A. B., Goering, E., Groth, U., Kaul, A. R., Ruediger, U.

{Applied Physics A}, 94(3):491-495, 2009 (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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Representing cyclic human motion using functional analysis

Ormoneit, D., Black, M. J., Hastie, T., Kjellström, H.

Image and Vision Computing, 23(14):1264-1276, December 2005 (article)

Abstract
We present a robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data. The pose of the body is represented by a time-series of joint angles which are automatically segmented into a sequence of motion cycles. The mean and the principal components of these cycles are computed using a new algorithm that enforces smooth transitions between the cycles by operating in the Fourier domain. Key to this method is its ability to automatically deal with noise and missing data. A learned walking model is then exploited for Bayesian tracking of 3D human motion.

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pdf pdf from publisher DOI [BibTex]

pdf pdf from publisher DOI [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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Correlation of EEG spectral entropy with regional cerebral blood flow during sevoflurane and propofol anaesthesia

Maksimow, A., Kaisti, K., Aalto, S., Mäenpää, M., Jääskeläinen, S., Hinkka, S., Martens, SMM., Särkelä, M., Viertiö-Oja, H., Scheinin, H.

Anaesthesia, 60(9):862-869, September 2005 (article)

Abstract
ENTROPY index monitoring, based on spectral entropy of the electroencephalogram, is a promising new method to measure the depth of anaesthesia. We examined the association between spectral entropy and regional cerebral blood flow in healthy subjects anaesthetised with 2%, 3% and 4% end-expiratory concentrations of sevoflurane and 7.6, 12.5 and 19.0 microg.ml(-1) plasma drug concentrations of propofol. Spectral entropy from the frequency band 0.8-32 Hz was calculated and cerebral blood flow assessed using positron emission tomography and [(15)O]-labelled water at baseline and at each anaesthesia level. Both drugs induced significant reductions in spectral entropy and cortical and global cerebral blood flow. Midfrontal-central spectral entropy was associated with individual frontal and whole brain blood flow values across all conditions, suggesting that this novel measure of anaesthetic depth can depict global changes in neuronal activity induced by the drugs. The cortical areas of the most significant associations were remarkably similar for both drugs.

ei

DOI [BibTex]

DOI [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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Analyzing microarray data using quantitative association rules

Georgii, E., Richter, L., Rückert, U., Kramer, S.

Bioinformatics, 21(Suppl. 2):123-129, September 2005 (article)

Abstract
Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.

ei

Web DOI [BibTex]

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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Large Margin Methods for Structured and Interdependent Output Variables

Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.

Journal of Machine Learning Research, 6, pages: 1453-1484, September 2005 (article)

Abstract
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

ei

PDF [BibTex]

PDF [BibTex]


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Contact Location Display for Haptic Perception of Curvature and Object Motion

Provancher, W. R., Cutkosky, M. R., Kuchenbecker, K. J., Niemeyer, G.

International Journal of Robotics Research, 24(9):691-702, sep 2005 (article)

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[BibTex]

[BibTex]


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Gene Expression Profiling of Serum- and Interleukin-1beta-Stimulated Primary Human Adult Articular Chondrocytes - A Molecular Analysis Based on Chondrocytes Isolated from One Donor

Aigner, T., McKenna, L., Zien, A., Fan, Z., Gebhard, P., Zimmer, R.

Cytokine, 31(3):227-240, August 2005 (article)

Abstract
In order to understand the cellular disease mechanisms of osteoarthritic cartilage degeneration it is of primary importance to understand both the anabolic and the catabolic processes going on in parallel in the diseased tissue. In this study, we have applied cDNA-array technology (Clontech) to study gene expression patterns of primary human normal adult articular chondrocytes isolated from one donor cultured under anabolic (serum) and catabolic (IL-1beta) conditions. Significant differences between the different in vitro cultures tested were detected. Overall, serum and IL-1beta significantly altered gene expression levels of 102 and 79 genes, respectively. IL-1beta stimulated the matrix metalloproteinases-1, -3, and -13 as well as members of its intracellular signaling cascade, whereas serum increased the expression of many cartilage matrix genes. Comparative gene expression analysis with previously published in vivo data (normal and osteoarthritic cartilage) showed significant differences of all in vitro s timulations compared to the changes detected in osteoarthritic cartilage in vivo. This investigation allowed us to characterize gene expression profiles of two classical anabolic and catabolic stimuli of human adult articular chondrocytes in vitro. No in vitro model appeared to be adequate to study overall gene expression alterations in osteoarthritic cartilage. Serum stimulated in vitro cultures largely reflected the results that were only consistent with the anabolic activation seen in osteoarthritic chondrocytes. In contrast, IL-1beta did not appear to be a good model for mimicking catabolic gene alterations in degenerating chondrocytes.

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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.

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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.

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PDF PostScript Web [BibTex]