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2012


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Bayesian flexible fitting of biomolecular structures into EM maps

Habeck, M.

Biophysical journal, 2012 (article) Submitted

[BibTex]

2012

[BibTex]


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Measurement and Calibration of Noise Bias in Weak Lensing Galaxy Shape Estimation

Kacprzak, T., Zuntz, J., Rowe, B., Bridle, S., Refregier, A., Amara, A., Voigt, L., Hirsch, M.

Monthly Notices of the Royal Astronomical Society (MNRAS), 2012 (article)

[BibTex]

[BibTex]


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LMM-Lasso: A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction

Rakitsch, B., Lippert, C., Stegle, O., Borgwardt, KM.

Bioinformatics, 29(2):206-214, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Existential neuroscience: a functional magnetic resonance imaging investigation of neural responses to reminders of one’s mortality

Quirin, M., Loktyushin, A., Arndt, J., Küstermann, E., Lo, Y., Kuhl, J., Eggert, L.

Social Cognitive and Affective Neuroscience, 7(2):193-198, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Active learning for domain adaptation in the supervised classification of remote sensing images

Persello, C., Bruzzone, L.

IEEE Transactions on Geoscience and Remote Sensing, 50(11):4468-4483, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Reinforcement learning to adjust parametrized motor primitives to new situations

Kober, J., Wilhelm, A., Oztop, E., Peters, J.

Autonomous Robots, 33(4):361-379, 2012 (article)

Abstract
Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

PDF PDF DOI [BibTex]


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On the Empirical Estimation of Integral Probability Metrics

Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B., Lanckriet, G.

Electronic Journal of Statistics, 6, pages: 1550-1599, 2012 (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging

Lois, C., Bezrukov, I., Schmidt, H., Schwenzer, N., Werner, M., Kupferschläger, J., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 39(11):1756-1766, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Multitask Learning in Computational Biology

Widmer, C., Rätsch, G.

JMLR W\&CP. ICML 2011 Unsupervised and Transfer Learning Workshop, 27, pages: 207-216, 2012 (article)

Abstract
Computational Biology provides a wide range of applications for Multitask Learning (MTL) methods. As the generation of labels often is very costly in the biomedical domain, combining data from different related problems or tasks is a promising strategy to reduce label cost. In this paper, we present two problems from sequence biology, where MTL was successfully applied. For this, we use regularization-based MTL methods, with a special focus on the case of a hierarchical relationship between tasks. Furthermore, we propose strategies to refine the measure of task relatedness, which is of central importance in MTL and finally give some practical guidelines, when MTL strategies are likely to pay off.

PDF [BibTex]

PDF [BibTex]


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Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis

Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., Jenkins, DJ., Penfold, CA., Baxter, Ll., Breeze, E., Kiddle, SJ., Rhodes, J., Atwell, S., Kliebenstein, D., Kim, Y-S., Stegle, O., Borgwardt, KM., others

The Plant Cell Online, 24(9):3530-3557, 2012, all authors: Oliver Windram,Priyadharshini Madhou,Stuart McHattie,Claire Hill,Richard Hickman,Emma Cooke,Dafyd J. Jenkins,Christopher A. Penfold,Laura Baxter,Emily Breeze,Steven J. Kiddle,Johanna Rhodes,Susanna Atwell,Daniel J. (article)

Web DOI [BibTex]

Web DOI [BibTex]


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Improved Linear Mixed Models for Genome-Wide Association Studies

Listgarten, J., Lippert, C., Kadie, CM., Davidson, RI., Eskin, E., Heckerman, D.

Nature Methods, 9, pages: 525–526, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Calibration of Boltzmann distribution priors in Bayesian data analysis

Mechelke, M., Habeck, M.

Physical Review E, 86(6):066705, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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CSB: A Python framework for computational structural biology

Kalev, I., Mechelke, M., Kopec, K., Holder, T., Carstens, S., Habeck, M.

Bioinformatics, 28(22):2996-2997, 2012 (article)

Abstract
Summary: Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. Availability: Stable release packages are available for download from the Python Package Index (PyPI), as well as from the project’s web site http://csb.codeplex.com.

Web DOI [BibTex]

Web DOI [BibTex]


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Design of a Haptic Interface for a Gastrointestinal Endoscopy Simulation

Yu, S., Woo, H. S., Son, H. I., Ahn, W., Jung, H., Lee, D. Y., Yi, S. Y.

Advanced Robotics, 26(18):2115-2143, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Significant global reduction of carbon uptake by water-cycle driven extreme vegetation anomalies

Zscheischler, J., Mahecha, M., von Buttlar, J., Harmeling, S., Jung, M., Randerson, J., Reichstein, M.

Nature Geoscience, 2012 (article) In revision

[BibTex]

[BibTex]


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Measurement and calibration of noise bias in weak lensing galaxy shape estimation

Kacprzak, T., Zuntz, J., Rowe, B., Bridle, S., Refregier, A., Amara, A., Voigt, L., Hirsch, M.

Monthly Notices of the Royal Astronomical Society, 427(4):2711-2722, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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Image analysis for cosmology: results from the GREAT10 Galaxy Challenge

Kitching, T. D., Balan, S. T., Bridle, S., Cantale, N., Courbin, F., Eifler, T., Gentile, M., Gill, M. S. S., Harmeling, S., Heymans, C., others,

Monthly Notices of the Royal Astronomical Society, 423(4):3163-3208, Oxford University Press, 2012 (article)

DOI [BibTex]

DOI [BibTex]


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First SN Discoveries from the Dark Energy Survey

Abbott, T., Abdalla, F., Achitouv, I., Ahn, E., Aldering, G., Allam, S., Alonso, D., Amara, A., Annis, J., Antonik, M., others,

The Astronomer's Telegram, 4668, pages: 1, 2012 (article)

[BibTex]

[BibTex]


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A sensorimotor paradigm for Bayesian model selection

Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 6(291):1-16, October 2012 (article)

Abstract
Sensorimotor control is thought to rely on predictive internal models in order to cope efficiently with uncertain environments. Recently, it has been shown that humans not only learn different internal models for different tasks, but that they also extract common structure between tasks. This raises the question of how the motor system selects between different structures or models, when each model can be associated with a range of different task-specific parameters. Here we design a sensorimotor task that requires subjects to compensate visuomotor shifts in a three-dimensional virtual reality setup, where one of the dimensions can be mapped to a model variable and the other dimension to the parameter variable. By introducing probe trials that are neutral in the parameter dimension, we can directly test for model selection. We found that model selection procedures based on Bayesian statistics provided a better explanation for subjects’ choice behavior than simple non-probabilistic heuristics. Our experimental design lends itself to the general study of model selection in a sensorimotor context as it allows to separately query model and parameter variables from subjects.

DOI [BibTex]

DOI [BibTex]


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Risk-Sensitivity in Bayesian Sensorimotor Integration

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

PLoS Computational Biology, 8(9):1-7, sep 2012 (article)

Abstract
Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

DOI [BibTex]

DOI [BibTex]

2011


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Causal Inference on Discrete Data using Additive Noise Models

Peters, J., Janzing, D., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12):2436-2450, December 2011 (article)

Abstract
Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. The case of two random variables is particularly challenging since no (conditional) independences can be exploited. Recent methods that are based on additive noise models suggest the following principle: Whenever the joint distribution {\bf P}^{(X,Y)} admits such a model in one direction, e.g., Y=f(X)+N, N \perp\kern-6pt \perp X, but does not admit the reversed model X=g(Y)+\tilde{N}, \tilde{N} \perp\kern-6pt \perp Y, one infers the former direction to be causal (i.e., X\rightarrow Y). Up to now, these approaches only dealt with continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work, we extend the notion of additive noise models to these cases. We prove that it almost never occurs that additive noise models can be fit in both directions. We further propose an efficient algorithm that is able to perform this way of causal inference on finite samples of discrete variables. We show that the algorithm works on both synthetic and real data sets.

PDF Web DOI [BibTex]

2011

PDF Web DOI [BibTex]


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Spontaneous epigenetic variation in the Arabidopsis thaliana methylome

Becker, C., Hagmann, J., Müller, J., Koenig, D., Stegle, O., Borgwardt, K., Weigel, D.

Nature, 480(7376):245-249, December 2011 (article)

Abstract
Heritable epigenetic polymorphisms, such as differential cytosine methylation, can underlie phenotypic variation1, 2. Moreover, wild strains of the plant Arabidopsis thaliana differ in many epialleles3, 4, and these can influence the expression of nearby genes1, 2. However, to understand their role in evolution5, it is imperative to ascertain the emergence rate and stability of epialleles, including those that are not due to structural variation. We have compared genome-wide DNA methylation among 10 A. thaliana lines, derived 30 generations ago from a common ancestor6. Epimutations at individual positions were easily detected, and close to 30,000 cytosines in each strain were differentially methylated. In contrast, larger regions of contiguous methylation were much more stable, and the frequency of changes was in the same low range as that of DNA mutations7. Like individual positions, the same regions were often affected by differential methylation in independent lines, with evidence for recurrent cycles of forward and reverse mutations. Transposable elements and short interfering RNAs have been causally linked to DNA methylation8. In agreement, differentially methylated sites were farther from transposable elements and showed less association with short interfering RNA expression than invariant positions. The biased distribution and frequent reversion of epimutations have important implications for the potential contribution of sequence-independent epialleles to plant evolution.

Web DOI [BibTex]

Web DOI [BibTex]


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HHfrag: HMM-based fragment detection using HHpred

Kalev, I., Habeck, M.

Bioinformatics, 27(22):3110-3116, November 2011 (article)

Abstract
Motivation: Over the last decade, both static and dynamic fragment libraries for protein structure prediction have been introduced. The former are built from clusters in either sequence or structure space and aim to extract a universal structural alphabet. The latter are tailored for a particular query protein sequence and aim to provide local structural templates that need to be assembled in order to build the full-length structure. Results: Here, we introduce HHfrag, a dynamic HMM-based fragment search method built on the profile–profile comparison tool HHpred. We show that HHfrag provides advantages over existing fragment assignment methods in that it: (i) improves the precision of the fragments at the expense of a minor loss in sequence coverage; (ii) detects fragments of variable length (6–21 amino acid residues); (iii) allows for gapped fragments and (iv) does not assign fragments to regions where there is no clear sequence conservation. We illustrate the usefulness of fragments detected by HHfrag on targets from most recent CASP.

Web DOI [BibTex]

Web DOI [BibTex]


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Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning

Hachiya, H., Peters, J., Sugiyama, M.

Neural Computation, 23(11):2798-2832, November 2011 (article)

Abstract
Direct policy search is a promising reinforcement learning framework, in particular for controlling continuous, high-dimensional systems. Policy search often requires a large number of samples for obtaining a stable policy update estimator, and this is prohibitive when the sampling cost is expensive. In this letter, we extend an expectation-maximization-based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, reward-weighted regression with sample reuse (R), is demonstrated through robot learning experiments.

Web DOI [BibTex]


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Model Learning in Robotics: a Survey

Nguyen-Tuong, D., Peters, J.

Cognitive Processing, 12(4):319-340, November 2011 (article)

Abstract
Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the in uence of an agent on this environment. In the context of model based learning control, we view the model from three di fferent perspectives. First, we need to study the di erent possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

PDF [BibTex]

PDF [BibTex]


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FaST linear mixed models for genome-wide association studies

Lippert, C., Listgarten, J., Liu, Y., Kadie, CM., Davidson, RI., Heckerman, D.

Nature Methods, 8(10):833–835, October 2011 (article)

Abstract
We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

PDF DOI [BibTex]

PDF DOI [BibTex]


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The effect of noise correlations in populations of diversely tuned neurons

Ecker, A., Berens, P., Tolias, A., Bethge, M.

Journal of Neuroscience, 31(40):14272-14283, October 2011 (article)

Abstract
The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.

Web DOI [BibTex]

Web DOI [BibTex]


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Analysis of Fixed-Point and Coordinate Descent Algorithms for Regularized Kernel Methods

Dinuzzo, F.

IEEE Transactions on Neural Networks, 22(10):1576-1587, October 2011 (article)

Abstract
In this paper, we analyze the convergence of two general classes of optimization algorithms for regularized kernel methods with convex loss function and quadratic norm regularization. The first methodology is a new class of algorithms based on fixed-point iterations that are well-suited for a parallel implementation and can be used with any convex loss function. The second methodology is based on coordinate descent, and generalizes some techniques previously proposed for linear support vector machines. It exploits the structure of additively separable loss functions to compute solutions of line searches in closed form. The two methodologies are both very easy to implement. In this paper, we also show how to remove non-differentiability of the objective functional by exactly reformulating a convex regularization problem as an unconstrained differentiable stabilization problem.

Web DOI [BibTex]

Web DOI [BibTex]


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A biomimetic approach to robot table tennis

Mülling, K., Kober, J., Peters, J.

Adaptive Behavior , 19(5):359-376 , October 2011 (article)

Abstract
Playing table tennis is a difficult motor task that requires fast movements, accurate control and adaptation to task parameters. Although human beings see and move slower than most robot systems, they significantly outperform all table tennis robots. One important reason for this higher performance is the human movement generation. In this paper, we study human movements during table tennis and present a robot system that mimics human striking behavior. Our focus lies on generating hitting motions capable of adapting to variations in environmental conditions, such as changes in ball speed and position. Therefore, we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model was evaluated both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAM™ robot arm.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Whole-genome sequencing of multiple Arabidopsis thaliana populations

Cao, J., Schneeberger, K., Ossowski, S., Günther, T., Bender, S., Fitz, J., Koenig, D., Lanz, C., Stegle, O., Lippert, C., Wang, X., Ott, F., Müller, J., Alonso-Blanco, C., Borgwardt, K., Schmid, K., Weigel, D.

Nature Genetics, 43(10):956–963, October 2011 (article)

Abstract
The plant Arabidopsis thaliana occurs naturally in many different habitats throughout Eurasia. As a foundation for identifying genetic variation contributing to adaptation to diverse environments, a 1001 Genomes Project to sequence geographically diverse A. thaliana strains has been initiated. Here we present the first phase of this project, based on population-scale sequencing of 80 strains drawn from eight regions throughout the species' native range. We describe the majority of common small-scale polymorphisms as well as many larger insertions and deletions in the A. thaliana pan-genome, their effects on gene function, and the patterns of local and global linkage among these variants. The action of processes other than spontaneous mutation is identified by comparing the spectrum of mutations that have accumulated since A. thaliana diverged from its closest relative 10 million years ago with the spectrum observed in the laboratory. Recent species-wide selective sweeps are rare, and potentially deleterious mutations are more common in marginal populations.

Web DOI [BibTex]

Web DOI [BibTex]


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Multiple reference genomes and transcriptomes for Arabidopsis thaliana

Gan, X., Stegle, O., Behr, J., Steffen, J., Drewe, P., Hildebrand, K., Lyngsoe, R., Schultheiss, S., Osborne, E., Sreedharan, V., Kahles, A., Bohnert, R., Jean, G., Derwent, P., Kersey, P., Belfield, E., Harberd, N., Kemen, E., Toomajian, C., Kover, P., Clark, R., Rätsch, G., Mott, R.

Nature, 477(7365):419–423, September 2011 (article)

Abstract
Genetic differences between Arabidopsis thaliana accessions underlie the plant’s extensive phenotypic variation, and until now these have been interpreted largely in the context of the annotated reference accession Col-0. Here we report the sequencing, assembly and annotation of the genomes of 18 natural A. thaliana accessions, and their transcriptomes. When assessed on the basis of the reference annotation, one-third of protein-coding genes are predicted to be disrupted in at least one accession. However, re-annotation of each genome revealed that alternative gene models often restore coding potential. Gene expression in seedlings differed for nearly half of expressed genes and was frequently associated with cis variants within 5 kilobases, as were intron retention alternative splicing events. Sequence and expression variation is most pronounced in genes that respond to the biotic environment. Our data further promote evolutionary and functional studies in A. thaliana, especially the MAGIC genetic reference population descended from these accessions.

Web DOI [BibTex]

Web DOI [BibTex]


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Weisfeiler-Lehman Graph Kernels

Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., Borgwardt, M.

Journal of Machine Learning Research, 12, pages: 2539-2561, September 2011 (article)

Abstract
In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this Weisfeiler-Lehman sequence of graphs, including a highly efficient kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimental evaluation, our kernels outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Our kernels open the door to large-scale applications of graph kernels in various disciplines such as computational biology and social network analysis.

PDF Web [BibTex]

PDF Web [BibTex]


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What are the Causes of Performance Variation in Brain-Computer Interfacing?

Grosse-Wentrup, M.

International Journal of Bioelectromagnetism, 13(3):115-116, September 2011 (article)

Abstract
While research on brain-computer interfacing (BCI) has seen tremendous progress in recent years, performance still varies substantially between as well as within subjects, with roughly 10 - 20% of subjects being incapable of successfully operating a BCI system. In this short report, I argue that this variation in performance constitutes one of the major obstacles that impedes a successful commercialization of BCI systems. I review the current state of research on the neuro-physiological causes of performance variation in BCI, discuss recent progress and open problems, and delineate potential research programs for addressing this issue.

PDF Web [BibTex]

PDF Web [BibTex]


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Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

Kitching, T., Amara, A., Gill, M., Harmeling, S., Heymans, C., Massey, R., Rowe, B., Schrabback, T., Voigt, L., Balan, S., Bernstein, G., Bethge, M., Bridle, S., Courbin, F., Gentile, M., Heavens, A., Hirsch, M., Hosseini, R., Kiessling, A., Kirk, D., Kuijken, K., Mandelbaum, R., Moghaddam, B., Nurbaeva, G., Paulin-Henriksson, S., Rassat, A., Rhodes, J., Schölkopf, B., Shawe-Taylor, J., Shmakova, M., Taylor, A., Velander, M., van Waerbeke, L., Witherick, D., Wittman, D.

Annals of Applied Statistics, 5(3):2231-2263, September 2011 (article)

Abstract
GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a variable convolution kernel, pixelization and noise. This is the second in a series of challenges set to the astronomy, computer science and statistics communities, providing a structured environment in which methods can be improved and tested in preparation for planned astronomical surveys. GREAT10 extends upon previous work by introducing variable fields into the challenge. The “Galaxy Challenge” involves the precise measurement of galaxy shape distortions, quantified locally by two parameters called shear, in the presence of a known convolution kernel. Crucially, the convolution kernel and the simulated gravitational lensing shape distortion both now vary as a function of position within the images, as is the case for real data. In addition, we introduce the “Star Challenge” that concerns the reconstruction of a variable convolution kernel, similar to that in a typical astronomical observation. This document details the GREAT10 Challenge for potential participants. Continually updated information is also available from www.greatchallenges.info.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

Hofmann, M., Bezrukov, I., Mantlik, F., Aschoff, P., Steinke, F., Beyer, T., Pichler, B., Schölkopf, B.

Journal of Nuclear Medicine, 52(9):1392-1399, September 2011 (article)

Abstract
PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). METHODS: Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. RESULTS: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method. CONCLUSION: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

Web DOI [BibTex]


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Semi-supervised kernel canonical correlation analysis with application to human fMRI

Blaschko, M., Shelton, J., Bartels, A., Lampert, C., Gretton, A.

Pattern Recognition Letters, 32(11):1572-1583 , August 2011 (article)

Abstract
Kernel canonical correlation analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying process that generates the data and can ignore high-variance noise directions. However, for data where acquisition in one or more modalities is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multi-variate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of KCCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Multi-subject learning for common spatial patterns in motor-imagery BCI

Devlaminck, D., Wyns, B., Grosse-Wentrup, M., Otte, G., Santens, P.

Computational Intelligence and Neuroscience, 2011(217987):1-9, August 2011 (article)

Abstract
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

PDF DOI [BibTex]

PDF DOI [BibTex]


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ccSVM: correcting Support Vector Machines for confounding factors in biological data classification

Li, L., Rakitsch, B., Borgwardt, K.

Bioinformatics, 27(13: ISMB/ECCB 2011):i342-i348, July 2011 (article)

Abstract
Motivation: Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification. Results: In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors. This is achieved by minimizing the statistical dependence between the classifier and the confounding factors. We prove that this formulation can be transformed into a standard Support Vector Machine with rescaled input data. In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

Web DOI [BibTex]

Web DOI [BibTex]


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Policy Search for Motor Primitives in Robotics

Kober, J., Peters, J.

Machine Learning, 84(1-2):171-203, July 2011 (article)

Abstract
Many motor skills in humanoid robotics can be learned using parametrized motor primitives. While successful applications to date have been achieved with imitation learning, most of the interesting motor learning problems are high-dimensional reinforcement learning problems. These problems are often beyond the reach of current reinforcement learning methods. In this paper, we study parametrized policy search methods and apply these to benchmark problems of motor primitive learning in robotics. We show that many well-known parametrized policy search methods can be derived from a general, common framework. This framework yields both policy gradient methods and expectation-maximization (EM) inspired algorithms. We introduce a novel EM-inspired algorithm for policy learning that is particularly well-suited for dynamical system motor primitives. We compare this algorithm, both in simulation and on a real robot, to several well-known parametrized policy search methods such as episodic REINFORCE, ‘Vanilla’ Policy Gradients with optimal baselines, episodic Natural Actor Critic, and episodic Reward-Weighted Regression. We show that the proposed method out-performs them on an empirical benchmark of learning dynamical system motor primitives both in simulation and on a real robot. We apply it in the context of motor learning and show that it can learn a complex Ball-in-a-Cup task on a real Barrett WAM™ robot arm.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

Kam-Thong, T., Pütz, B., Karbalai, N., Müller-Myhsok, B., Borgwardt, K.

Bioinformatics, 27(13: ISMB/ECCB 2011):i214-i221, July 2011 (article)

Abstract
Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene–gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested. Results: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert–Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

Web DOI [BibTex]

Web DOI [BibTex]


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Empirical Inference

Schölkopf, B.

International Journal of Materials Research, 2011(7):809-814, July 2011 (article)

Abstract
Empirical Inference is the process of drawing conclusions from observational data. For instance, the data can be measurements from an experiment, which are used by a researcher to infer a scientific law. Another kind of empirical inference is performed by living beings, continuously recording data from their environment and carrying out appropriate actions. Do these problems have anything in common, and are there underlying principles governing the extraction of regularities from data? What characterizes hard inference problems, and how can we solve them? Such questions are studied by a community of scientists from various fields, engaged in machine learning research. This short paper, which is based on the author’s lecture to the scientific council of the Max Planck Society in February 2010, will attempt to describe some of the main ideas and problems of machine learning. It will provide illustrative examples of real world machine learning applications, including the use of machine learning towards the design of intelligent systems.

Web DOI [BibTex]

Web DOI [BibTex]


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Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction

Hirsch, M., Harmeling, S., Sra, S., Schölkopf, B.

Astronomy & Astrophysics, 531(A9):11, July 2011 (article)

Abstract
Astronomical images taken by ground-based telescopes suffer degradation due to atmospheric turbulence. This degradation can be tackled by costly hardware-based approaches such as adaptive optics, or by sophisticated software-based methods such as lucky imaging, speckle imaging, or multi-frame deconvolution. Software-based methods process a sequence of images to reconstruct a deblurred high-quality image. However, existing approaches are limited in one or several aspects: (i) they process all images in batch mode, which for thousands of images is prohibitive; (ii) they do not reconstruct a super-resolved image, even though an image sequence often contains enough information; (iii) they are unable to deal with saturated pixels; and (iv) they are usually non-blind, i.e., they assume the blur kernels to be known. In this paper we present a new method for multi-frame deconvolution called online blind deconvolution (OBD) that overcomes all these limitations simultaneously. Encouraging results on simulated and real astronomical images demonstrate that OBD yields deblurred images of comparable and often better quality than existing approaches.

PDF DOI [BibTex]


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Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.

Journal of Neural Engineering, 8(3):1-12, June 2011 (article)

Abstract
The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

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Greedy Learning of Binary Latent Trees

Harmeling, S., Williams, C.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6):1087-1097, June 2011 (article)

Abstract
Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are hierarchical latent class (HLC) models. Zhang (2004) proposed a search algorithm for learning such models that can find good solutions but is often computationally expensive. As an alternative we investigate two greedy procedures: the BIN-G algorithm determines both the structure of the tree and the cardinality of the latent variables in a bottom-up fashion. The BIN-A algorithm first determines the tree structure using agglomerative hierarchical clustering, and then determines the cardinality of the latent variables as for BIN-G. We show that even with restricting ourselves to binary trees we obtain HLC models of comparable quality to Zhang‘s solutions, while being faster to compute. This claim is validated by a comprehensive comparison on several datasets. Furthermore, we demonstrate that our methods are able to estimate int erpretable latent structures on real-world data with a large number of variables. By applying our method to a restricted version of the 20 newsgroups data these models turn out to be related to topic models, and on data from the PASCAL Visual Object Classes (VOC) 2007 challenge we show how such tree-structured models help us understand how objects co-occur in images.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Learning Dynamic Tactile Sensing with Robust Vision-based Training

Kroemer, O., Lampert, C., Peters, J.

IEEE Transactions on Robotics, 27(3):545-557 , June 2011 (article)

Abstract
Dynamic tactile sensing is a fundamental ability to recognize materials and objects. However, while humans are born with partially developed dynamic tactile sensing and quickly master this skill, today's robots remain in their infancy. The development of such a sense requires not only better sensors but the right algorithms to deal with these sensors' data as well. For example, when classifying a material based on touch, the data are noisy, high-dimensional, and contain irrelevant signals as well as essential ones. Few classification methods from machine learning can deal with such problems. In this paper, we propose an efficient approach to infer suitable lower dimensional representations of the tactile data. In order to classify materials based on only the sense of touch, these representations are autonomously discovered using visual information of the surfaces during training. However, accurately pairing vision and tactile samples in real-robot applications is a difficult problem. The proposed approach, therefore, works with weak pairings between the modalities. Experiments show that the resulting approach is very robust and yields significantly higher classification performance based on only dynamic tactile sensing.

Web DOI [BibTex]

Web DOI [BibTex]


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Algebraic polynomials and moments of stochastic integrals

Langovoy, M.

Statistics & Probability Letters, 81(6):627-631, June 2011 (article)

Abstract
We propose an algebraic method for proving estimates on moments of stochastic integrals. The method uses qualitative properties of roots of algebraic polynomials from certain general classes. As an application, we give a new proof of a variation of the Burkholder–Davis–Gundy inequality for the case of stochastic integrals with respect to real locally square integrable martingales. Further possible applications and extensions of the method are outlined.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Inference for psychometric functions in the presence of nonstationary behavior

Fründ, I., Haenel, N., Wichmann, F.

Journal of Vision, 11(6):1-19, May 2011 (article)

Abstract
Measuring sensitivity is at the heart of psychophysics. Often, sensitivity is derived from estimates of the psychometric function. This function relates response probability to stimulus intensity. In estimating these response probabilities, most studies assume stationary observers: Responses are expected to be dependent only on the intensity of a presented stimulus and not on other factors such as stimulus sequence, duration of the experiment, or the responses on previous trials. Unfortunately, a number of factors such as learning, fatigue, or fluctuations in attention and motivation will typically result in violations of this assumption. The severity of these violations is yet unknown. We use Monte Carlo simulations to show that violations of these assumptions can result in underestimation of confidence intervals for parameters of the psychometric function. Even worse, collecting more trials does not eliminate this misestimation of confidence intervals. We present a simple adjustment of the confidence intervals that corrects for the underestimation almost independently of the number of trials and the particular type of violation.

Web DOI [BibTex]

Web DOI [BibTex]


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Transition from the locked in to the completely locked-in state: A physiological analysis

Ramos Murguialday, A., Hill, J., Bensch, M., Martens, S., Halder, S., Nijboer, F., Schölkopf, B., Birbaumer, N., Gharabaghi, A.

Clinical Neurophysiology, 122(5):925-933 , May 2011 (article)

Abstract
Objective To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain–computer-interface (BCI) communication. Methods Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS. Results At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brain potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response. Conclusions The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways. Significance Auditory and proprioceptive brain–computer-interface (BCI) systems are the only remaining communication channels in CLIS.

PDF DOI [BibTex]

PDF DOI [BibTex]