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2004


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m-Alternative-Forced-Choice: Improving the Efficiency of the Method of Constant Stimuli

Jäkel, F., Hill, J., Wichmann, F.

7, pages: 118, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
We explored several ways to improve the efficiency of measuring psychometric functions without resorting to adaptive procedures. a) The number m of alternatives in an m-alternative-forced-choice (m-AFC) task improves the efficiency of the method of constant stimuli. b) When alternatives are presented simultaneously on different positions on a screen rather than sequentially time can be saved and memory load for the subject can be reduced. c) A touch-screen can further help to make the experimental procedure more intuitive. We tested these ideas in the measurement of contrast sensitivity and compared them to results obtained by sequential presentation in two-interval-forced-choice (2-IFC). Qualitatively all methods (m-AFC and 2-IFC) recovered the characterictic shape of the contrast sensitivity function in three subjects. The m-AFC paradigm only took about 60% of the time of the 2-IFC task. We tried m=2,4,8 and found 4-AFC to give the best model fits and 2-AFC to have the least bias.

ei

Web [BibTex]

2004


Web [BibTex]


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Efficient Approximations for Support Vector Classifiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100%. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

ei

Web [BibTex]

Web [BibTex]


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Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm

Hill, N., Lal, T., Schröder, M., Hinterberger, T., Birbaumer, N., Schölkopf, B.

7, pages: 102, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
During the last 20 years several paradigms for Brain Computer Interfaces have been proposed— see [1] for a recent review. They can be divided into (a) stimulus-driven paradigms, using e.g. event-related potentials or visual evoked potentials from an EEG signal, and (b) patient-driven paradigms such as those that use premotor potentials correlated with imagined action, or slow cortical potentials (e.g. [2]). Our aim is to develop a stimulus-driven paradigm that is applicable in practice to patients. Due to the unreliability of visual perception in “locked-in” patients in the later stages of disorders such as Amyotrophic Lateral Sclerosis, we concentrate on the auditory modality. Speci- cally, we look for the effects, in the EEG signal, of selective attention to one of two concurrent auditory stimulus streams, exploiting the increased activation to attended stimuli that is seen under some circumstances [3]. We present the results of our preliminary experiments on normal subjects. On each of 400 trials, two repetitive stimuli (sequences of drum-beats or other pulsed stimuli) could be heard simultaneously. The two stimuli were distinguishable from one another by their acoustic properties, by their source location (one from a speaker to the left of the subject, the other from the right), and by their differing periodicities. A visual cue preceded the stimulus by 500 msec, indicating which of the two stimuli to attend to, and the subject was instructed to count the beats in the attended stimulus stream. There were up to 6 beats of each stimulus: with equal probability on each trial, all 6 were played, or the fourth was omitted, or the fth was omitted. The 40-channel EEG signals were analyzed ofine to reconstruct which of the streams was attended on each trial. A linear Support Vector Machine [4] was trained on a random subset of the data and tested on the remainder. Results are compared from two types of pre-processing of the signal: for each stimulus stream, (a) EEG signals at the stream's beat periodicity are emphasized, or (b) EEG signals following beats are contrasted with those following missing beats. Both forms of pre-processing show promising results, i.e. that selective attention to one or the other auditory stream yields signals that are classiable signicantly above chance performance. In particular, the second pre-processing was found to be robust to reduction in the number of features used for classication (cf. [5]), helping us to eliminate noise.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Texture and Haptic Cues in Slant Discrimination: Measuring the Effect of Texture Type

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

7, pages: 165, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich, F. A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
In a number of models of depth cue combination the depth percept is constructed via a weighted average combination of independent depth estimations. The inuence of each cue in such average depends on the reliability of the source of information [1,5]. In particular, Ernst and Banks (2002) formulate such combination as that of the minimum variance unbiased estimator that can be constructed from the available cues. We have observed systematic differences in slant discrimination performance of human observers when different types of textures were used as cue to slant [4]. If the depth percept behaves as described above, our measurements of the slopes of the psychometric functions provide the predicted weights for the texture cue for the ranked texture types. However, the results for slant discrimination obtained when combining these texture types with object motion results are difcult to reconcile with the minimum variance unbiased estimator model [3]. This apparent failure of such model might be explained by the existence of a coupling of texture and motion, violating the assumption of independence of cues. Hillis, Ernst, Banks, and Landy (2002) [2] have shown that while for between-modality combination the human visual system has access to the single-cue information, for withinmodality combination (visual cues) the single-cue information is lost. This suggests a coupling between visual cues and independence between visual and haptic cues. Then, in the present study we combined the different texture types with haptic information in a slant discrimination task, to test whether in the between-modality condition these cues are combined as predicted by an unbiased, minimum variance estimator model. The measured weights for the cues were consistent with a combination rule sensitive to the reliability of the sources of information, but did not match the predictions of a statistically optimal combination.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Efficient Approximations for Support Vector Classiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100%. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

ei

Web [BibTex]

Web [BibTex]


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EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods

Schröder, M., Lal, T., Bogdan, M., Schölkopf, B.

7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids or environmental control systems supporting paralyzed patients that have lost motor control completely. The design of an EEG based BCI system requires good answers for the problem of selecting useful features during the performance of a mental task as well as for the problem of classifying these features. For the special case of choosing appropriate EEG channels from several available channels, we propose the application of variants of the Support Vector Machine (SVM) for both problems. Although these algorithms do not rely on prior knowledge they can provide more accurate solutions than standard lter methods [1] for feature selection which usually incorporate prior knowledge about neural activity patterns during the performed mental tasks. For judging the importance of features we introduce a new relevance measure and apply it to EEG channels. Although we base the relevance measure for this purpose on the previously introduced algorithms, it does in general not depend on specic algorithms but can be derived using arbitrary combinations of feature selectors and classifiers.

ei

Web [BibTex]

Web [BibTex]


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Learning Depth

Sinz, F., Franz, MO.

pages: 69, (Editors: H.H.Bülthoff, H.A.Mallot, R.Ulrich,F.A.Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract
The depth of a point in space can be estimated by observing its image position from two different viewpoints. The classical approach to stereo vision calculates depth from the two projection equations which together form a stereocamera model. An unavoidable preparatory work for this solution is a calibration procedure, i.e., estimating the external (position and orientation) and internal (focal length, lens distortions etc.) parameters of each camera from a set of points with known spatial position and their corresponding image positions. This is normally done by iteratively linearizing the single camera models and reestimating their parameters according to the error on the known datapoints. The advantage of the classical method is the maximal usage of prior knowledge about the underlying physical processes and the explicit estimation of meaningful model parameters such as focal length or camera position in space. However, the approach neglects the nonlinear nature of the problem such that the results critically depend on the choice of the initial values for the parameters. In this study, we approach the depth estimation problem from a different point of view by applying generic machine learning algorithms to learn the mapping from image coordinates to spatial position. These algorithms do not require any domain knowledge and are able to learn nonlinear functions by mapping the inputs into a higher-dimensional space. Compared to classical calibration, machine learning methods give a direct solution to the depth estimation problem which means that the values of the stereocamera parameters cannot be extracted from the learned mapping. On the poster, we compare the performance of classical camera calibration to that of different machine learning algorithms such as kernel ridge regression, gaussian processes and support vector regression. Our results indicate that generic learning approaches can lead to higher depth accuracies than classical calibration although no domain knowledge is used.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D.

January 2004 (talk)

Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

ei

PDF [BibTex]


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

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

Neural Networks, 17(1):127-141, January 2004 (article)

Abstract
In Support Vector (SV) regression, a parameter ν controls the number of Support Vectors and the number of points that come to lie outside of the so-called var epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression. As a side effect of the experiments, valuable information about the generalization behavior of the remaining SVM parameters and their dependencies is gained. The experimental findings are valid even for complex ‘real-world’ data sets. Based on our results on the role of the ν-SVM parameters, we discuss various model selection methods.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract
A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.

ei

PDF [BibTex]

PDF [BibTex]


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Protein ranking: from local to global structure in the protein similarity network

Weston, J., Elisseeff, A., Zhou, D., Leslie, C., Noble, W.

Proceedings of the National Academy of Science, 101(17):6559-6563, 2004 (article)

Abstract
Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a pre-computed, weighted network. The resulting ranking algorithm, evaluated using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as PSI-BLAST.

ei

Web [BibTex]

Web [BibTex]


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Gaussian Processes in Machine Learning

Rasmussen, CE.

In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

Abstract
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Neural mechanisms underlying control of a Brain-Computer-Interface (BCI): Simultaneous recording of bold-response and EEG

Hinterberger, T., Wilhelm, B., Veit, R., Weiskopf, N., Lal, TN., Birbaumer, N.

2004 (poster)

Abstract
Brain computer interfaces (BCI) enable humans or animals to communicate or activate external devices without muscle activity using electric brain signals. The BCI Thought Translation Device (TTD) uses learned regulation of slow cortical potentials (SCPs), a skill most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain macro-potentials are well understood, but the neural mechanisms underlying learning of the self-regulation skill for BCI-use are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the TTD was combined with functional MRI and EEG was recorded inside the MRI scanner in twelve healthy participants who have learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill: successf! ul control of cortical positivity allowing a person to activate an external device was closely related to an increase of BOLD (blood oxygen level dependent) response in the basal ganglia and frontal premotor deactivation indicating learned regulation of a cortical-striatal loop responsible for local excitation thresholds of cortical assemblies. The data suggest that human users of a BCI learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite of direct brain communication: the learning of this skill depends critically on an intact and flexible interaction between these cortico-basal ganglia-circuits. Supported by the Deutsche Forschungsgemeinschaft (DFG) and the National Institute of Health (NIH).

ei

[BibTex]

[BibTex]


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Statistical Performance of Support Vector Machines

Blanchard, G., Bousquet, O., Massart, P.

2004 (article)

ei

PostScript [BibTex]


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Asymptotic Properties of the Fisher Kernel

Tsuda, K., Akaho, S., Kawanabe, M., Müller, K.

Neural Computation, 16(1):115-137, 2004 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Some observations on the effects of slant and texture type on slant-from-texture

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

Vision Research, 44(13):1511-1535, 2004 (article)

Abstract
We measure the performance of five subjects in a slant-discrimination task for differently textured planes. As textures we used uniform lattices, randomly displaced lattices, circles (polka dots), Voronoi tessellations, plaids, 1/f noise, “coherent” noise and a leopard skin-like texture. Our results show: (1) Improving performance with larger slants for all textures. (2) Thus, following from (1), cases of “non-symmetrical” performance around a particular orientation. (3) For orientations sufficiently slanted, the different textures do not elicit major differences in performance, (4) while for orientations closer to the vertical plane there are marked differences between them. (5) These differences allow a rank-order of textures to be formed according to their “helpfulness”– that is, how easy the discrimination task is when a particular texture is mapped on the plane. Polka dots tend to allow the best slant discrimination performance, noise patterns the worst. Two additional experiments were conducted to test the generality of the obtained rank-order. First, the tilt of the planes was rotated to break the axis of gravity present in the original discrimination experiment. Second, the task was changed to a slant report task via probe adjustment. The results of both control experiments confirmed the texture-based rank-order previously obtained. We comment on the importance of these results for depth perception research in general, and in particular the implications our results have for studies of cue combination (sensor fusion) using texture as one of the cues involved.

ei

PDF [BibTex]

PDF [BibTex]


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Masking by plaid patterns revisited

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 285, 2004 (poster)

ei

[BibTex]

[BibTex]


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Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

Tsuda, K., Uda, S., Kin, T., Asai, K.

Neural Processing Letters, 19, pages: 63-72, 2004 (article)

ei

PDF [BibTex]

PDF [BibTex]


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A Tutorial on Support Vector Regression

Smola, A., Schölkopf, B.

Statistics and Computing, 14(3):199-222, 2004 (article)

ei

Web [BibTex]

Web [BibTex]


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Protein Classification via Kernel Matrix Completion

Kin, T., Kato, T., Tsuda, K.

In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Early visual processing—data, theory, models

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 24, 2004 (poster)

ei

[BibTex]

[BibTex]


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Bayesian analysis of the Scatterometer Wind Retrieval Inverse Problem: Some New Approaches

Cornford, D., Csato, L., Evans, D., Opper, M.

Journal of the Royal Statistical Society B, 66, pages: 1-17, 3, 2004 (article)

Abstract
The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem.A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters.We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer.We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution.We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes.This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets.We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

ei

PDF [BibTex]

PDF [BibTex]


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Feature Selection for Support Vector Machines Using Genetic Algorithms

Fröhlich, H., Chapelle, O., Schölkopf, B.

International Journal on Artificial Intelligence Tools (Special Issue on Selected Papers from the 15th IEEE International Conference on Tools with Artificial Intelligence 2003), 13(4):791-800, 2004 (article)

ei

Web [BibTex]

Web [BibTex]


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Introduction to Statistical Learning Theory

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

In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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A Primer on Kernel Methods

Vert, J., Tsuda, K., Schölkopf, B.

In Kernel Methods in Computational Biology, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Phenotypic Characterization of Human Chondrocyte Cell Line C-20/A4: A Comparison between Monolayer and Alginate Suspension Culture

Finger, F., Schorle, C., Söder, S., Zien, A., Goldring, M., Aigner, T.

Cells Tissues Organs, 178(2):65-77, 2004 (article)

Abstract
DNA microarray analysis was used to investigate the molecular phenotype of one of the first human chondrocyte cell lines, C-20/A4, derived from juvenile costal chondrocytes by immortalization with origin-defective simian virus 40 large T antigen. Clontech Human Cancer Arrays 1.2 and quantitative PCR were used to examine gene expression profiles of C-20/A4 cells cultured in the presence of serum in monolayer and alginate beads. In monolayer cultures, genes involved in cell proliferation were strongly upregulated compared to those expressed by human adult articular chondrocytes in primary culture. Of the cell cycle-regulated genes, only two, the CDK regulatory subunit and histone H4, were downregulated after culture in alginate beads, consistent with the ability of these cells to proliferate in suspension culture. In contrast, the expression of several genes that are involved in pericellular matrix formation, including MMP-14, COL6A1, fibronectin, biglycan and decorin, was upregulated when the C-20/A4 cells were transferred to suspension culture in alginate. Also, nexin-1, vimentin, and IGFBP-3, which are known to be expressed by primary chondrocytes, were differentially expressed in our study. Consistent with the proliferative phenotype of this cell line, few genes involved in matrix synthesis and turnover were highly expressed in the presence of serum. These results indicate that immortalized chondrocyte cell lines, rather than substituting for primary chondrocytes, may serve as models for extending findings on chondrocyte function not achievable by the use of primary chondrocytes.

ei

[BibTex]

[BibTex]


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Kernel Methods and their Potential Use in Signal Processing

Perez-Cruz, F., Bousquet, O.

IEEE Signal Processing Magazine, (Special issue on Signal Processing for Mining), 2004 (article) Accepted

ei

PostScript [BibTex]

PostScript [BibTex]


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Concentration Inequalities

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

In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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Kernels for graphs

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

In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

ei

PDF [BibTex]

PDF [BibTex]


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A primer on molecular biology

Zien, A.

In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

Abstract
Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (``compartmentalization''); (3) the way cells produce proteins (``protein expression''); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Implicit Wiener series for capturing higher-order interactions in images

Franz, M., Schölkopf, B.

Sensory coding and the natural environment, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

Abstract
The information about the objects in an image is almost exclusively described by the higher-order interactions of its pixels. The Wiener series is one of the standard methods to systematically characterize these interactions. However, the classical estimation method of the Wiener expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear signals such as images. We propose an estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems using polynomial kernels as known from Support Vector Machines and other kernel-based methods. Numerical experiments show performance advantages in terms of convergence, interpretability and system sizes that can be handled. By the time of the conference, we will be able to present first results on the higher-order structure of natural images.

ei

[BibTex]

[BibTex]


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Classification and Memory Behaviour of Man Revisited by Machine

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

CSHL Meeting on Computational & Systems Neuroscience (COSYNE), 2004 (poster)

ei

[BibTex]

[BibTex]


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Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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E. Coli Inspired Propulsion for Swimming Microrobots

Behkam, Bahareh, Sitti, Metin

pages: 1037–1041, 2004 (article)

Abstract
Medical applications are among the most fascinating areas of microrobotics. For long, scientists have dreamed of miniature smart devices that can travel inside the human body and carry out a host of complex operations such as minimally invasive surgery (MIS), highly localized drug delivery, and screening for diseases that are in their very early stages. Still a distant dream, significant progress in micro and nanotechnology brings us closer to materializing it. For such a miniature device to be injected into the body, it has to be 800 μm or smaller in diameter. Miniature, safe and energy efficient propulsion systems hold the key to maturing this technology but they pose significant challenges. Scaling the macroscale natation mechanisms to micro/nano length scales is unfeasible. It has been estimated that a vibrating-fin driven swimming robot shorter than 6 mm can not overcome the viscous drag forces in water. In this paper, the authors propose a new type of propulsion inspired by the motility mechanism of bacteria with peritrichous flagellation, such as Escherichia coli, Salmonella typhimurium and Serratia marcescens. The perfomance of the propulsive mechanism is estimated by modeling the dynamics of the motion. The motion of the moving organelle is simulated and key parameters such as velocity, distribution of force and power requirments for different configurations of the tail are determined theoretically. In order to validate the theoretical result, a scaled up model of the swimming robot is fabricated and characterized in silicone oil using the Buckingham PI theorem for scaling. The results are compared with the theoretically computed values. These robots are intended to swim in stagnation/low velocity biofluid and reach currently inaccessible areas of the human body for disease inspection and possibly treatment. Potential target regions to use these robots include eyeball cavity, cerebrospinal fluid and the urinary system.

pi

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Discovering optimal imitation strategies

Billard, A., Epars, Y., Calinon, S., Cheng, G., Schaal, S.

Robotics and Autonomous Systems, 47(2-3):68-77, 2004, clmc (article)

Abstract
This paper develops a general policy for learning relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or of gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different imitative tasks and controls task reproduction by a full body humanoid robot.

am

[BibTex]

[BibTex]


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Rhythmic movement is not discrete

Schaal, S., Sternad, D., Osu, R., Kawato, M.

Nature Neuroscience, 7(10):1137-1144, 2004, clmc (article)

Abstract
Rhythmic movements, like walking, chewing, or scratching, are phylogenetically old mo-tor behaviors found in many organisms, ranging from insects to primates. In contrast, discrete movements, like reaching, grasping, or kicking, are behaviors that have reached sophistication primarily in younger species, particularly in primates. Neurophysiological and computational research on arm motor control has focused almost exclusively on dis-crete movements, essentially assuming similar neural circuitry for rhythmic tasks. In con-trast, many behavioral studies focused on rhythmic models, subsuming discrete move-ment as a special case. Here, using a human functional neuroimaging experiment, we show that in addition to areas activated in rhythmic movement, discrete movement in-volves several higher cortical planning areas, despite both movement conditions were confined to the same single wrist joint. These results provide the first neuroscientific evi-dence that rhythmic arm movement cannot be part of a more general discrete movement system, and may require separate neurophysiological and theoretical treatment.

am

link (url) [BibTex]

link (url) [BibTex]


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Uncompensated spins in a micro-patterned CoFeB / MnIr exchange bias system

Eimüller, T., Kato, T., Mizuno, T., Tsunashima, S., Quitmann, C., Ramsvik, T., Iwata, S., Schütz, G.

{Applied Physics Letters}, 85(12):2310-2312, 2004 (article)

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[BibTex]

[BibTex]


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Comparative study of magnetization reversal in isolated and strayfield coupled microcontacts

Meier, G., Eiselt, R., Bolte, M., Barthelmess, M., Eimüller, T., Fischer, P.

{Applied Physics Letters}, 85(7):1193-1195, 2004 (article)

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[BibTex]

[BibTex]


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Grain boundary faceting close to the \Sigma3 coincidence misorientation in copper

Straumal, B., Polyakov, S., Bischoff, E., Mittemeijer, E.

{Zeitschrift f\"ur Metallkunde}, 95, pages: 939-944, 2004 (article)

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[BibTex]

[BibTex]


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Photoelectron spectroscopy on Pt atoms and clusters deposited on C(0001)

Fauth, K., Schneider, N., He\ssler, M., Schütz, G.

{European Physical Journal D}, 29(1):57-61, 2004 (article)

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[BibTex]

[BibTex]


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Enhanced flux line pinning by substrate induced defects in YBCO thin films

Albrecht, J., Leonhardt, S., Habermeier, H.-U., Brück, S., Spolenak, R., Kronmüller, H.

{Physica C}, 404, pages: 18-21, 2004 (article)

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[BibTex]

[BibTex]


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Amorphous carbon nanotubes produced by a temperature controlled DC arc discharge

Liu, Y., Song, X., Zhao, T., Zhu, J., Hirscher, M., Phillipp, F.

{Carbon}, 42(8-9):1852-1855, 2004 (article)

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[BibTex]

[BibTex]


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Initial oxidation of quasicrystals - an investigation by high-resolution RBS and ERDA

Plachke, D., Khellaf, A., Kurth, M., Szökefalvy-Nagy, A., Carstanjen, H. D.

{Journal of Non-Crystalline Solids}, 334-335, pages: 524-531, 2004 (article)

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[BibTex]

[BibTex]


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First-principles modeling of magnetism and phase equilibria in binary alloys

Diaz-Ortiz, A., Drautz, R., Fähnle, M., Dosch, H.

{Journal of Alloys and Compounds}, 369, pages: 27-32, 2004 (article)

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[BibTex]

[BibTex]


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Critical thicknesses of domain formations in cubic particles and thin films

Kronmüller, H., Goll, D., Hertel, R., Schütz, G.

{Physica B}, 343(1-4):229-235, 2004 (article)

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[BibTex]

[BibTex]


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Learning from demonstration and adaptation of biped locomotion

Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., Kawato, M.

Robotics and Autonomous Systems, 47(2-3):79-91, 2004, clmc (article)

Abstract
In this paper, we introduce a framework for learning biped locomotion using dynamical movement primitives based on non-linear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithmbased on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotioncontroller.

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link (url) [BibTex]

link (url) [BibTex]


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Critical sizes for ferromagnetic spherical hollow nanoparticles

Goll, D., Berkowitz, A. E., Bertram, H. N.

{Physical Review B}, 70(18), 2004 (article)

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[BibTex]

[BibTex]


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Grain boundary wetting by a solid phase; microstructural development in a Zn-5 wt\textpercent Al alloy

Lopez, G. A., Mittemeijer, E. J., Straumal, B. B.

{Acta Materialia}, 52(15):4537-4545, 2004 (article)

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[BibTex]

[BibTex]


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Electronic correlations, magnetism, and structure of Fe-Al subsystems: An LDA+U study

Lechermann, F., Fähnle, M., Meyer, B., Elsässer, C.

{Physical Review B}, 69, 2004 (article)

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[BibTex]

[BibTex]