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2014


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Low-amplitude magnetic vortex core reversal by non-linear interaction between azimuthal spin waves and the vortex gyromode

Sproll, M., Noske, M., Bauer, H., Kammerer, M., Gangwar, A., Dieterle, G., Weigand, M., Stoll, H., Woltersdorf, G., Back, C. H., Schütz, G.

{Applied Physics Letters}, 104(1), American Institute of Physics, Melville, NY, 2014 (article)

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DOI [BibTex]

2014


DOI [BibTex]


Thumb xl simulated annealing
Simulated Annealing

Gall, J.

In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)

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[BibTex]

[BibTex]


Thumb xl ijcvflow2
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them

Sun, D., Roth, S., Black, M. J.

International Journal of Computer Vision (IJCV), 106(2):115-137, 2014 (article)

Abstract
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical'' flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

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pdf full text code [BibTex]

pdf full text code [BibTex]


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Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 981-988, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting

Mason, S., Righetti, L., Schaal, S.

In 2014 IEEE-RAS International Conference on Humanoid Robots, pages: 374-379, IEEE, Madrid, Spain, 2014 (inproceedings)

Abstract
Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences

Peng, Z, Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)

Abstract
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects’ self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.

ei

DOI [BibTex]

DOI [BibTex]


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Curiosity-driven learning with Context Tree Weighting

Peng, Z, Braun, DA

pages: 366-367, IEEE, Piscataway, NJ, USA, 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), October 2014 (conference)

Abstract
In the first simulation, the intrinsic motivation of the agent was given by measuring learning progress through reduction in informational surprise (Figure 1 A-C). This way the agent should first learn the action that is easiest to learn (a1), and then switch to other actions that still allow for learning (a2) and ignore actions that cannot be learned at all (a3). This is exactly what we found in our simple environment. Compared to the original developmental learning algorithm based on learning progress proposed by Oudeyer [2], our Context Tree Weighting approach does not require local experts to do prediction, rather it learns the conditional probability distribution over observations given action in one structure. In the second simulation, the intrinsic motivation of the agent was given by measuring compression progress through improvement in compressibility (Figure 1 D-F). The agent behaves similarly: the agent first concentrates on the action with the most predictable consequence and then switches over to the regular action where the consequence is more difficult to predict, but still learnable. Unlike the previous simulation, random actions are also interesting to some extent because the compressed symbol strings use 8-bit representations, while only 2 bits are required for our observation space. Our preliminary results suggest that Context Tree Weighting might provide a useful representation to study problems of development.

ei

DOI [BibTex]

DOI [BibTex]


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Rotating Magnetic Miniature Swimming Robots With Multiple Flexible Flagella

Ye, Z., Régnier, S., Sitti, M.

IEEE Trans. on Robotics, 30(1):3-13, 2014 (article)

pi

[BibTex]

[BibTex]


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Structural optimization method towards synthesis of small scale flexure-based mobile grippers

Lum, G. Z., Diller, E., Sitti, M.

In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages: 2339-2344, 2014 (inproceedings)

pi

[BibTex]

[BibTex]


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Three-Dimensional Programmable Assembly by Untethered Magnetic Robotic Micro-Grippers

Diller, E., Sitti, M.

Advanced Functional Materials, 24, pages: 4397-4404, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Six-Degrees-of-Freedom Remote Actuation of Magnetic Microrobots.

Diller, E. D., Giltinan, J., Lum, G. Z., Ye, Z., Sitti, M.

In Robotics: Science and Systems, 2014 (inproceedings)

pi

[BibTex]

[BibTex]


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Mechanics of Load–Drag–Unload Contact Cleaning of Gecko-Inspired Fibrillar Adhesives

Abusomwan, U. A., Sitti, M.

Langmuir, 30(40):11913-11918, American Chemical Society, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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State Estimation for a Humanoid Robot

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 952-958, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

am mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Monte Carlo methods for exact & efficient solution of the generalized optimality equations

Ortega, PA, Braun, DA, Tishby, N

pages: 4322-4327, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (conference)

Abstract
Previous work has shown that classical sequential decision making rules, including expectimax and minimax, are limit cases of a more general class of bounded rational planning problems that trade off the value and the complexity of the solution, as measured by its information divergence from a given reference. This allows modeling a range of novel planning problems having varying degrees of control due to resource constraints, risk-sensitivity, trust and model uncertainty. However, so far it has been unclear in what sense information constraints relate to the complexity of planning. In this paper, we introduce Monte Carlo methods to solve the generalized optimality equations in an efficient \& exact way when the inverse temperatures in a generalized decision tree are of the same sign. These methods highlight a fundamental relation between inverse temperatures and the number of Monte Carlo proposals. In particular, it is seen that the number of proposals is essentially independent of the size of the decision tree.

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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The local magnetic properties of [MnIII6 CrIII]3+ and [FeIII6 CrIII]3+ single-molecule magnets deposited on surfaces studied by spin-polarized photoemission and XMCD with circularly polarized synchrotron radiation

Heinzmann, U., Helmstedt, A., Dohmeier, N., Müller, N., Gryzia, A., Brechling, A., Hoeke, V., Krickemeyer, E., Glaser, T., Fonin, M., Bouvron, S., Leicht, P., Tietze, T., Goering, E., Kuepper, K.

{Journal of Physics: Conference Series}, 488(13), IOP Publishing, Bristol, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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A fluorene based covalent triazine framework with high CO2 and H2 capture and storage capacities

Hug, S., Mesch, M. B., Oh, H., Popp, N., Hirscher, M., Senker, J., Lotsch, B. V.

{Journal of Materials Chemistry A}, 2(16):5928-5936, Royal Society of Chemistry, Cambridge, UK, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Ab-initio calculations and atomistic calculations on the magnetoelectric effects in metallic nanostructures

Fähnle, M., Subkow, S.

{Physica Status Solidi C}, 11(2):185-191, Wiley-VCH, Weinheim, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Role of electron-magnon scatterings in ultrafast demagnetization

Haag, M., Illg, C., Fähnle, M.

{Physical Review B}, 90(1), American Physical Society, Woodbury, NY, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Element specific monolayer depth profiling

Macke, S., Radi, A., Hamann-Borrero, J. E., Verna, A., Bluschke, M., Brück, S., Goering, E., Sutarto, R., He, F., Cristiani, G., Wu, M., Benckiser, E., Habermeier, H., Logvenov, G., Gauquelin, N., Botton, G. A., Kajdos, A. P., Stemmer, S., Sawatzky, G. A., Haverkort, M. W., Keimer, B., Hinkov, V.

{Advanced Materials}, 26(38):6554-6559, Wiley VCH, Weinheim, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Local modification of the magnetic vortex-core velocity by gallium implantation

Langner, H. H., Vogel, A., Beyersdorff, B., Weigand, M., Frömter, R., Oepen, H. P., Meier, G.

{Journal of Applied Physcis}, (10), American Institute of Physics, New York, NY, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Influence of magnetic fields on spin-mixing in transition metals

Haag, M., Illg, C., Fähnle, M.

{Physical Review B}, 90(13), American Physical Society, Woodbury, NY, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]

2006


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Conformal Multi-Instance Kernels

Blaschko, M., Hofmann, T.

In NIPS 2006 Workshop on Learning to Compare Examples, pages: 1-6, NIPS Workshop on Learning to Compare Examples, December 2006 (inproceedings)

Abstract
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.

ei

PDF Web [BibTex]

2006


PDF Web [BibTex]


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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]

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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Minimal Logical Constraint Covering Sets

Sinz, F., Schölkopf, B.

(155), Max Planck Institute for Biological Cybernetics, Tübingen, December 2006 (techreport)

Abstract
We propose a general framework for computing minimal set covers under class of certain logical constraints. The underlying idea is to transform the problem into a mathematical programm under linear constraints. In this sense it can be seen as a natural extension of the vector quantization algorithm proposed by Tipping and Schoelkopf. We show which class of logical constraints can be cast and relaxed into linear constraints and give an algorithm for the transformation.

ei

PDF [BibTex]

PDF [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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Adapting Spatial Filter Methods for Nonstationary BCIs

Tomioka, R., Hill, J., Blankertz, B., Aihara, K.

In IBIS 2006, pages: 65-70, 2006 Workshop on Information-Based Induction Sciences, November 2006 (inproceedings)

Abstract
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock the method is trained on and that the method is applied to. Assuming the joint distributions of the whitened signal and the class label to be identical in two blocks, where the whitening is done in each block independently, we propose a simple adaptation formula that is applicable to a broad class of spatial filtering methods including ICA, CSP, and logistic regression classifiers. We characterize the class of linear transformations for which the above assumption holds. Experimental results on 60 BCI datasets show improved classification accuracy compared to (a) fixed spatial filter approach (no adaptation) and (b) fixed spatial pattern approach (proposed by Hill et al., 2006 [1]).

ei

PDF [BibTex]

PDF [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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New Methods for the P300 Visual Speller

Biessmann, F.

(1), (Editors: Hill, J. ), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2006 (techreport)

ei

PDF [BibTex]

PDF [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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Geometric Analysis of Hilbert Schmidt Independence criterion based ICA contrast function

Shen, H., Jegelka, S., Gretton, A.

(PA006080), National ICT Australia, Canberra, Australia, October 2006 (techreport)

ei

Web [BibTex]

Web [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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A Linear Programming Approach for Molecular QSAR analysis

Saigo, H., Kadowaki, T., Tsuda, K.

In MLG 2006, pages: 85-96, (Editors: Gärtner, T. , G. C. Garriga, T. Meinl), International Workshop on Mining and Learning with Graphs, September 2006, Best Paper Award (inproceedings)

Abstract
Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity relationship (QSAR) analysis, the central task is to find a regression function that predicts the activity of the molecule in high accuracy. Setting a QSAR as a primal target, we propose a new linear programming approach to the graph-based regression problem. Our method extends the graph classification algorithm by Kudo et al. (NIPS 2004), which is a combination of boosting and graph mining. Instead of sequential multiplicative updates, we employ the linear programming boosting (LP) for regression. The LP approach allows to include inequality constraints for the parameter vector, which turns out to be particularly useful in QSAR tasks where activity values are sometimes unavailable. Furthermore, the efficiency is improved significantly by employing multiple pricing.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Incremental Aspect Models for Mining Document Streams

Surendran, A., Sra, S.

In PKDD 2006, pages: 633-640, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract
In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call “query-line tracking” i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.

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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PALMA: Perfect Alignments using Large Margin Algorithms

Rätsch, G., Hepp, B., Schulze, U., Ong, C.

In GCB 2006, pages: 104-113, (Editors: Huson, D. , O. Kohlbacher, A. Lupas, K. Nieselt, A. Zell), Gesellschaft für Informatik, Bonn, Germany, German Conference on Bioinformatics, September 2006 (inproceedings)

Abstract
Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. We present a novel approach based on large margin learning that combines kernel based splice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm -- called PALMA -- tunes the parameters of the model such that the true alignment scores higher than all other alignments. In an experimental study on the alignments of mRNAs containing artificially generated micro-exons, we show that our algorithm drastically outperforms all other methods: It perfectly aligns all 4358 sequences on an hold-out set, while the best other method misaligns at least 90 of them. Moreover, our algorithm is very robust against noise in the query sequence: when deleting, inserting, or mutating up to 50% of the query sequence, it still aligns 95% of all sequences correctly, while other methods achieve less than 36% accuracy. For datasets, additional results and a stand-alone alignment tool see http://www.fml.mpg.de/raetsch/projects/palma.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Graph Based Semi-Supervised Learning with Sharper Edges

Shin, H., Hill, N., Rätsch, G.

In ECML 2006, pages: 401-412, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning (ECML), September 2006 (inproceedings)

Abstract
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points‘ (often symmetric)relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours -- the point and its outgoing edges have been ``blunted.‘‘ We present an approach to ``sharpening‘‘ in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that it can improve performance on a number of publicly available benchmark data sets.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle

Deisenroth, MP., Ohtsuka, T., Weissel, F., Brunn, D., Hanebeck, UD.

In MFI 2006, pages: 371-376, (Editors: Hanebeck, U. D.), IEEE Service Center, Piscataway, NJ, USA, 6th IEEE International Conference on Multisensor Fusion and Integration, September 2006 (inproceedings)

Abstract
In this paper, an approach to the finite-horizon optimal state-feedback control problem of nonlinear, stochastic, discrete-time systems is presented. Starting from the dynamic programming equation, the value function will be approximated by means of Taylor series expansion up to second-order derivatives. Moreover, the problem will be reformulated, such that a minimum principle can be applied to the stochastic problem. Employing this minimum principle, the optimal control problem can be rewritten as a two-point boundary-value problem to be solved at each time step of a shrinking horizon. To avoid numerical problems, the two-point boundary-value problem will be solved by means of a continuation method. Thus, the curse of dimensionality of dynamic programming is avoided, and good candidates for the optimal state-feedback controls are obtained. The proposed approach will be evaluated by means of a scalar example system.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Uniform Convergence of Adaptive Graph-Based Regularization

Hein, M.

In COLT 2006, pages: 50-64, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract
The regularization functional induced by the graph Laplacian of a random neighborhood graph based on the data is adaptive in two ways. First it adapts to an underlying manifold structure and second to the density of the data-generating probability measure. We identify in this paper the limit of the regularizer and show uniform convergence over the space of Hoelder functions. As an intermediate step we derive upper bounds on the covering numbers of Hoelder functions on compact Riemannian manifolds, which are of independent interest for the theoretical analysis of manifold-based learning methods.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Regularised CSP for Sensor Selection in BCI

Farquhar, J., Hill, N., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 14-15, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract
The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10--20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Time-Dependent Demixing of Task-Relevant EEG Signals

Hill, N., Farquhar, J., Lal, T., Schölkopf, B.

In Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006, pages: 20-21, (Editors: GR Müller-Putz and C Brunner and R Leeb and R Scherer and A Schlögl and S Wriessnegger and G Pfurtscheller), Verlag der Technischen Universität Graz, Graz, Austria, 3rd International Brain-Computer Interface Workshop and Training Course, September 2006 (inproceedings)

Abstract
Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively robust to changes in other (task-irrelevant) brain activity. The idea is to keep spatial *patterns* fixed rather than spatial filters, when transferring from training to test sessions or from one time window to another. We show that a fixed spatial pattern (FSP) approach, using a moving-window estimate of signal covariances, can be more robust to non-stationarity than a fixed spatial filter (FSF) approach.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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A Sober Look at Clustering Stability

Ben-David, S., von Luxburg, U., Pal, D.

In COLT 2006, pages: 5-19, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

Abstract
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties. Quite surprisingly, the conclusion of our analysis is that for large sample size, stability is fully determined by the behavior of the objective function which the clustering algorithm is aiming to minimize. If the objective function has a unique global minimizer, the algorithm is stable, otherwise it is unstable. In particular we conclude that stability is not a well-suited tool to determine the number of clusters - it is determined by the symmetries of the data which may be unrelated to clustering parameters. We prove our results for center-based clusterings and for spectral clustering, and support our conclusions by many examples in which the behavior of stability is counter-intuitive.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Information Marginalization on Subgraphs

Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D.

In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract
Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers’ and ‘authors’ exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers’), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Bayesian Active Learning for Sensitivity Analysis

Pfingsten, T.

In ECML 2006, pages: 353-364, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 17th European Conference on Machine Learning, September 2006 (inproceedings)

Abstract
Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further. We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.

ei

PDF Web DOI [BibTex]

PDF Web 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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A tutorial on spectral clustering

von Luxburg, U.

(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

Abstract
In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Nevertheless, on the first glance spectral clustering looks a bit mysterious, and it is not obvious to see why it works at all and what it really does. This article is a tutorial introduction to spectral clustering. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

ei

PDF [BibTex]

PDF [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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Towards the Inference of Graphs on Ordered Vertexes

Zien, A., Raetsch, G., Ong, C.

(150), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

Abstract
We propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs.

ei

PDF [BibTex]

PDF [BibTex]