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2012


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Feature Selection via Dependence Maximization

Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K.

Journal of Machine Learning Research, 13, pages: 1393-1434, May 2012 (article)

Abstract
We introduce a framework of feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.

ei

PDF [BibTex]

2012


PDF [BibTex]


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High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces

Grosse-Wentrup, M., Schölkopf, B.

Journal of Neural Engineering, 9(4):046001, May 2012 (article)

Abstract
Subjects operating a brain–computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.

ei

Web DOI [BibTex]


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ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections

Karaletsos, T., Stegle, O., Dreyer, C., Winn, J., Borgwardt, K.

Bioinformatics, 28(7):1001-1008, April 2012 (article)

Abstract
Motivation: Accurate large-scale phenotyping has recently gained considerable importance in biology. For example, in genome wide association studies technological advances have rendered genotyping cheap, leaving phenotype acquisition as the major bottleneck. Automatic image analysis is one major strategy to phenotype individuals in large numbers. Current approaches for visual phenotyping focus predominantly on summarizing statistics and geometric measures, such as height and width of an individual, or color histograms and patterns. However, more subtle, but biologically informative phenotypes, such as the local deformation of the shape of an individual with respect to the population mean cannot be automatically extracted and quantified by current techniques. Results: We propose a probabilistic machine learning model that allows for the extraction of deformation phenotypes from biological images, making them available as quantitative traits for downstream analysis. Our approach jointly models a collection of images using a learned common template that is mapped onto each image through a deformable smooth transformation. In a case study we analyze the shape deformations of 388 guppy fish (Poecilia reticulata). We find that the flexible shape phenotypes our model extracts are complementary to basic geometric measures. Moreover, these quantitative traits assort the observations into distinct groups and can be mapped to polymorphic genetic loci of the sample set.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses

Putzeys, T., Bethge, M., Wichmann, F., Wagemans, J., Goris, R.

PLoS Computational Biology, 8(4):1-13, April 2012 (article)

Abstract
Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Patterns of cis regulatory variation in diverse human populations

Stranger, BE., Montgomery, SB., Dimas, AS., Parts, L., Stegle, O., Ingle, CE., Sekowska, M., Smith, GD., Evans, D., Gutierrez-Arcelus, M., others

PLoS genetics, 8(4):e1002639, April 2012 (article)

Abstract
he genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A Kernel Two-Sample Test

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

Journal of Machine Learning Research, 13, pages: 723-773, March 2012 (article)

Abstract
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD). We present two distribution-free tests based on large deviation bounds for the MMD, and a third test based on the asymptotic distribution of this statistic. The MMD can be computed in quadratic time, although efficient linear time approximations are available. Our statistic is an instance of an integral probability metric, and various classical metrics on distributions are obtained when alternative function classes are used in place of an RKHS. We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Technical performance evaluation of a human brain PET/MRI system

Kolb, A., Wehrl, H., Hofmann, M., Judenhofer, M., Eriksson, L., Ladebeck, R., Lichy, M., Byars, L., Michel, C., Schlemmer, H., Schmand, M., Claussen, C., Sossi, V., Pichler, B.

European Radiology, 22(8):1776-1788, March 2012 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components

Lampert, C., Peters, J.

Journal of Real-Time Image Processing, 7(1):31-41, March 2012 (article)

Abstract
We describe RTblob, a high speed vision system that detects objects in cluttered scenes based on their color and shape at a speed of over 800 frames/s. Because the system is available as open-source software and relies only on off-the-shelf PC hardware components, it can provide the basis for multiple application scenarios. As an illustrative example, we show how RTblob can be used in a robotic table tennis scenario to estimate ball trajectories through 3D space simultaneously from four cameras images at a speed of 200 Hz.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x)

Sra, S.

Computational Statistics, 27(1):177-190, March 2012 (article)

Abstract
In high-dimensional directional statistics one of the most basic probability distributions is the von Mises-Fisher (vMF) distribution. Maximum likelihood estimation for the vMF distribution turns out to be surprisingly hard because of a difficult transcendental equation that needs to be solved for computing the concentration parameter κ. This paper is a followup to the recent paper of Tanabe et al. (Comput Stat 22(1):145–157, 2007), who exploited inequalities about Bessel function ratios to obtain an interval in which the parameter estimate for κ should lie; their observation lends theoretical validity to the heuristic approximation of Banerjee et al. (JMLR 6:1345–1382, 2005). Tanabe et al. (Comput Stat 22(1):145–157, 2007) also presented a fixed-point algorithm for computing improved approximations for κ. However, their approximations require (potentially significant) additional computation, and in this short paper we show that given the same amount of computation as their method, one can achieve more accurate approximations using a truncated Newton method. A more interesting contribution of this paper is a simple algorithm for computing I s (x): the modified Bessel function of the first kind. Surprisingly, our naïve implementation turns out to be several orders of magnitude faster for large arguments common to high-dimensional data, than the standard implementations in well-established software such as Mathematica ©, Maple ©, and Gp/Pari.

ei

PDF PDF DOI [BibTex]


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An online brain–computer interface based on shifting attention to concurrent streams of auditory stimuli

Hill, N., Schölkopf, B.

Journal of Neural Engineering, 9(2):026011, February 2012 (article)

Abstract
We report on the development and online testing of an electroencephalogram-based brain–computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare 'oddball' stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

ei

PDF DOI [BibTex]


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A non-monotonic method for large-scale non-negative least squares

Kim, D., Sra, S., Dhillon, I. S.

Optimization Methods and Software, 28(5):1012-1039, Febuary 2012 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

ACM Transactions on Knowledge Discovery from Data, 5(4:21), February 2012 (article)

Abstract
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

Stegle, O., Parts, L., Piipari, M., Winn, J., Durbin, R.

Nature Protocols, 7(3):500–507, February 2012 (article)

Abstract
We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment

Zander, TO., Jatzev, S.

Journal of Neural Engineering, 9(1):016003, 10, February 2012 (article)

Abstract
Brain–computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human–machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human–machine system. The EEG data from this experiment are available for public download at www.phypa.org.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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How the initialization affects the stability of the k-means algorithm

Bubeck, S., Meila, M., von Luxburg, U.

ESAIM: Probability and Statistics, 16, pages: 436-452, January 2012 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

Fusi, N., Stegle, O., Lawrence, ND.

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

Abstract
Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Bayesian flexible fitting of biomolecular structures into EM maps

Habeck, M.

Biophysical journal, 2012 (article) Submitted

ei

[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 (MNRAS), 2012 (article)

ei

[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)

ei

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)

ei

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)

ei

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.

ei

PDF PDF DOI [BibTex]

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)

ei

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)

ei

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.

ei

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)

ei

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)

ei

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)

ei

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.

ei

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)

ei

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

ei

[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)

ei

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)

ei

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)

ei

[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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

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/).

ei

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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

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.

ei

PDF Web [BibTex]

PDF Web [BibTex]