Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Babbar, R., Schölkopf, B.

Data scarcity, robustness and extreme multi-label classification

Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

Babbar, R., Schölkopf, B. Data scarcity, robustness and extreme multi-label classification Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

Rojas-Carulla, M. Learning Transferable Representations University of Cambridge, UK, 2019 (phdthesis)

Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

Gu, S. Sample-efficient deep reinforcement learning for continuous control University of Cambridge, UK, 2019 (phdthesis)

A 32-channel multi-coil setup optimized for human brain shimming at 9.4T

Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K.

Magnetic Resonance in Medicine, 2019, (Early View) (article)

Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K. A 32-channel multi-coil setup optimized for human brain shimming at 9.4T Magnetic Resonance in Medicine, 2019, (Early View) (article)

DOI Project Page Project Page [BibTex]

Multidimensional Contrast Limited Adaptive Histogram Equalization

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P.

IEEE Access, 7, pages: 165437-165447, 2019 (article)

arXiv link (url) DOI [BibTex]

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P. Multidimensional Contrast Limited Adaptive Histogram Equalization IEEE Access, 7, pages: 165437-165447, 2019 (article)

arXiv link (url) DOI [BibTex]

Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M. Enhancing Human Learning via Spaced Repetition Optimization Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI Project Page Project Page [BibTex]

Formally justified and modular Bayesian inference for probabilistic programs

Ścibior*, A.

University of Cambridge, UK, 2019 (phdthesis)

Ścibior*, A. Formally justified and modular Bayesian inference for probabilistic programs University of Cambridge, UK, 2019 (phdthesis)

Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

Xu, J. Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing Technical University of Munich, Germany, 2019 (mastersthesis)

Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

2019 (article) Submitted

Abstract

High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Arxiv Video [BibTex]

Büchler, D., Calandra, R., Peters, J. Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots 2019 (article) Submitted

Arxiv Video [BibTex]

Pragmatism and Variable Transformations in Causal Modelling

Weichwald, S.

ETH Zurich, 2019 (phdthesis)

Weichwald, S. Pragmatism and Variable Transformations in Causal Modelling ETH Zurich, 2019 (phdthesis)

Inferring causation from time series with perspectives in Earth system sciences

Nature Communications, 2019 (article) In revision

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M. S. B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J. Inferring causation from time series with perspectives in Earth system sciences Nature Communications, 2019 (article) In revision

Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

Katiyar, P. Quantification of tumor heterogeneity using PET/MRI and machine learning Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

Kübler, J. M., Muandet, K., Schölkopf, B.

Quantum mean embedding of probability distributions

Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

Kübler, J. M., Muandet, K., Schölkopf, B. Quantum mean embedding of probability distributions Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

Reliable Real-Time Ball Tracking for Robot Table Tennis

Gomez-Gonzalez, S., Nemmour, Y., Schölkopf, B., Peters, J.

Robotics, 8(4):90, 2019 (article)

Gomez-Gonzalez, S., Nemmour, Y., Schölkopf, B., Peters, J. Reliable Real-Time Ball Tracking for Robot Table Tennis Robotics, 8(4):90, 2019 (article)

Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

Bauer, M.

University of Cambridge, UK, 2019 (phdthesis)

Bauer, M. Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning University of Cambridge, UK, 2019 (phdthesis)

arXiv link (url) DOI Project Page [BibTex]

Optimizing the Execution of Dynamic Robot Movements With Learning Control

Koc, O., Maeda, G., Peters, J.

IEEE Transactions on Robotics, pages: 1-16, 2019 (article)

Koc, O., Maeda, G., Peters, J. Optimizing the Execution of Dynamic Robot Movements With Learning Control IEEE Transactions on Robotics, pages: 1-16, 2019 (article)

arXiv link (url) DOI Project Page [BibTex]

Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework

Koc, O., Peters, J.

IEEE Robotics and Automation Letters, 4(2):1784-1791, 2019 (article)

link (url) DOI Project Page [BibTex]

Koc, O., Peters, J. Learning to Serve: An Experimental Study for a New Learning From Demonstrations Framework IEEE Robotics and Automation Letters, 4(2):1784-1791, 2019 (article)

link (url) DOI Project Page [BibTex]

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

Klus, S., Schuster, I., Muandet, K. Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

2013

Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF [BibTex]

2013

Schober, M. Camera-specific Image Denoising Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF [BibTex]

Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N.

Investigative Radiology, 48(5):247-255, May 2013 (article)

Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N. Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System Investigative Radiology, 48(5):247-255, May 2013 (article)

Replacing Causal Faithfulness with Algorithmic Independence of Conditionals

Lemeire, J., Janzing, D.

Minds and Machines, 23(2):227-249, May 2013 (article)

Abstract

Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms.

Lemeire, J., Janzing, D. Replacing Causal Faithfulness with Algorithmic Independence of Conditionals Minds and Machines, 23(2):227-249, May 2013 (article)

What can neurons do for their brain? Communicate selectivity with bursts

Balduzzi, D., Tononi, G.

Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

Abstract

Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.

Balduzzi, D., Tononi, G. What can neurons do for their brain? Communicate selectivity with bursts Theory in Biosciences , 132(1):27-39, Springer, March 2013 (article)

Apprenticeship Learning with Few Examples

Boularias, A., Chaib-draa, B.

Neurocomputing, 104, pages: 83-96, March 2013 (article)

Abstract

We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship learning via inverse reinforcement learning provides an efficient tool for generalizing the examples, based on the assumption that the expert's policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorithms use only simple empirical averages of the features in the demonstrations as a statistics of the expert's policy. However, this method is efficient only when the number of examples is sufficiently large to cover most of the states, or the dynamics of the system is nearly deterministic. In this paper, we show that the quality of the learned policies is sensitive to the error in estimating the averages of the features when the dynamics of the system is stochastic. To reduce this error, we introduce two new approaches for bootstrapping the demonstrations by assuming that the expert is near-optimal and the dynamics of the system is known. In the first approach, the expert's examples are used to learn a reward function and to generate furthermore examples from the corresponding optimal policy. The second approach uses a transfer technique, known as graph homomorphism, in order to generalize the expert's actions to unvisited regions of the state space. Empirical results on simulated robot navigation problems show that our approach is able to learn sufficiently good policies from a significantly small number of examples.

Boularias, A., Chaib-draa, B. Apprenticeship Learning with Few Examples Neurocomputing, 104, pages: 83-96, March 2013 (article)

website+code pdf link (url) [BibTex]

Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

Abstract

Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

Hennig, P., Kiefel, M. Quasi-Newton Methods: A New Direction Journal of Machine Learning Research, 14(1):843-865, March 2013 (article)

website+code pdf link (url) [BibTex]

Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling

Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K.

Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

Abstract

Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37% when plug flow is assumed as in the standard model.

Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K. Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling Magnetic Resonance in Medicine, 69(2):524-530, Febuary 2013 (article)

The multivariate Watson distribution: Maximum-likelihood estimation and other aspects

Sra, S., Karp, D.

Journal of Multivariate Analysis, 114, pages: 256-269, February 2013 (article)

Abstract

This paper studies fundamental aspects of modelling data using multivariate Watson distributions. Although these distributions are natural for modelling axially symmetric data (i.e., unit vectors where View the MathML source are equivalent), for high-dimensions using them can be difficult—largely because for Watson distributions even basic tasks such as maximum-likelihood are numerically challenging. To tackle the numerical difficulties some approximations have been derived. But these are either grossly inaccurate in high-dimensions [K.V. Mardia, P. Jupp, Directional Statistics, second ed., John Wiley & Sons, 2000] or when reasonably accurate [A. Bijral, M. Breitenbach, G.Z. Grudic, Mixture of Watson distributions: a generative model for hyperspherical embeddings, in: Artificial Intelligence and Statistics, AISTATS 2007, 2007, pp. 35–42], they lack theoretical justification. We derive new approximations to the maximum-likelihood estimates; our approximations are theoretically well-defined, numerically accurate, and easy to compute. We build on our parameter estimation and discuss mixture-modelling with Watson distributions; here we uncover a hitherto unknown connection to the “diametrical clustering” algorithm of Dhillon et al. [I.S. Dhillon, E.M. Marcotte, U. Roshan, Diametrical clustering for identifying anticorrelated gene clusters, Bioinformatics 19 (13) (2003) 1612–1619].

Sra, S., Karp, D. The multivariate Watson distribution: Maximum-likelihood estimation and other aspects Journal of Multivariate Analysis, 114, pages: 256-269, February 2013 (article)

How the result of graph clustering methods depends on the construction of the graph

Maier, M., von Luxburg, U., Hein, M.

ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Abstract

We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to innity. It turns out that the limit values of the same objective function are systematically dierent on dierent types of graphs. This implies that clustering results systematically depend on the graph and can be very dierent for dierent types of graph. We provide examples to illustrate the implications on spectral clustering.

Maier, M., von Luxburg, U., Hein, M. How the result of graph clustering methods depends on the construction of the graph ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Explicit eigenvalues of certain scaled trigonometric matrices

Sra, S.

Linear Algebra and its Applications, 438(1):173-181, January 2013 (article)

Sra, S. Explicit eigenvalues of certain scaled trigonometric matrices Linear Algebra and its Applications, 438(1):173-181, January 2013 (article)

How Sensitive Is the Human Visual System to the Local Statistics of Natural Images?

Gerhard, H., Wichmann, F., Bethge, M.

PLoS Computational Biology, 9(1):e1002873, January 2013 (article)

Abstract

Several aspects of primate visual physiology have been identified as adaptations to local regularities of natural images. However, much less work has measured visual sensitivity to local natural image regularities. Most previous work focuses on global perception of large images and shows that observers are more sensitive to visual information when image properties resemble those of natural images. In this work we measure human sensitivity to local natural image regularities using stimuli generated by patch-based probabilistic natural image models that have been related to primate visual physiology. We find that human observers can learn to discriminate the statistical regularities of natural image patches from those represented by current natural image models after very few exposures and that discriminability depends on the degree of regularities captured by the model. The quick learning we observed suggests that the human visual system is biased for processing natural images, even at very fine spatial scales, and that it has a surprisingly large knowledge of the regularities in natural images, at least in comparison to the state-of-the-art statistical models of natural images.

Gerhard, H., Wichmann, F., Bethge, M. How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? PLoS Computational Biology, 9(1):e1002873, January 2013 (article)

A neural population model for visual pattern detection

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

Psychological Review, 120(3):472–496, 2013 (article)

Goris, R., Putzeys, T., Wagemans, J., Wichmann, F. A neural population model for visual pattern detection Psychological Review, 120(3):472–496, 2013 (article)

Accurate indel prediction using paired-end short reads

Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, KM.

BMC Genomics, 14(132), 2013 (article)

Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, KM. Accurate indel prediction using paired-end short reads BMC Genomics, 14(132), 2013 (article)

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D., Chickering, D., Portugualy, E., Ray, D., Simard, P., Snelson, E.

Journal of Machine Learning Research, 14, pages: 3207-3260, 2013 (article)

Web link (url) [BibTex]

Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D., Chickering, D., Portugualy, E., Ray, D., Simard, P., Snelson, E. Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising Journal of Machine Learning Research, 14, pages: 3207-3260, 2013 (article)

Web link (url) [BibTex]

When luminance increment thresholds depend on apparent lightness

Maertens, M., Wichmann, F.

Journal of Vision, 13(6):1-11, 2013 (article)

Maertens, M., Wichmann, F. When luminance increment thresholds depend on apparent lightness Journal of Vision, 13(6):1-11, 2013 (article)

Efficient network-guided multi-locus association mapping with graph cuts

Azencott, C., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K.

Bioinformatics, 29(13):i171-i179, 2013 (article)

Azencott, C., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K. Efficient network-guided multi-locus association mapping with graph cuts Bioinformatics, 29(13):i171-i179, 2013 (article)

Janzing, D., Balduzzi, D., Grosse-Wentrup, M., Schölkopf, B.

Quantifying causal influences

Annals of Statistics, 41(5):2324-2358, 2013 (article)

Janzing, D., Balduzzi, D., Grosse-Wentrup, M., Schölkopf, B. Quantifying causal influences Annals of Statistics, 41(5):2324-2358, 2013 (article)

Probabilistic movement modeling for intention inference in human-robot interaction

Wang, Z., Mülling, K., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.

International Journal of Robotics Research, 32(7):841-858, 2013 (article)

Wang, Z., Mülling, K., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J. Probabilistic movement modeling for intention inference in human-robot interaction International Journal of Robotics Research, 32(7):841-858, 2013 (article)

Blind Retrospective Motion Correction of MR Images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine (MRM), 70(6):1608–1618, 2013 (article)

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B. Blind Retrospective Motion Correction of MR Images Magnetic Resonance in Medicine (MRM), 70(6):1608–1618, 2013 (article)

Modeling fixation locations using spatial point processes

Barthelmé, S., Trukenbrod, H., Engbert, R., Wichmann, F.

Journal of Vision, 13(12):1-34, 2013 (article)

Abstract

Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.

Barthelmé, S., Trukenbrod, H., Engbert, R., Wichmann, F. Modeling fixation locations using spatial point processes Journal of Vision, 13(12):1-34, 2013 (article)

A probabilistic model for secondary structure prediction from protein chemical shifts

Mechelke, M., Habeck, M.

Proteins: Structure, Function, and Bioinformatics, 81(6):984–993, 2013 (article)

Mechelke, M., Habeck, M. A probabilistic model for secondary structure prediction from protein chemical shifts Proteins: Structure, Function, and Bioinformatics, 81(6):984–993, 2013 (article)

Climate Extremes and the Carbon Cycle

Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M., Seneviratne, S., Zscheischler, J., Beer, C., Buchmann, N., Frank, D., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., Wattenbach, M.

Nature, 500, pages: 287-295, 2013 (article)

Identification of stimulus cues in narrow-band tone-in-noise detection using sparse observer models

Schönfelder, V., Wichmann, F.

Journal of the Acoustical Society of America, 134(1):447-463, 2013 (article)

Schönfelder, V., Wichmann, F. Identification of stimulus cues in narrow-band tone-in-noise detection using sparse observer models Journal of the Acoustical Society of America, 134(1):447-463, 2013 (article)

Probabilistic Model-based Imitation Learning

Englert, P., Paraschos, A., Peters, J., Deisenroth, M.

Adaptive Behavior Journal, 21(5):388-403, 2013 (article)

Englert, P., Paraschos, A., Peters, J., Deisenroth, M. Probabilistic Model-based Imitation Learning Adaptive Behavior Journal, 21(5):388-403, 2013 (article)

Metabolic cost as an organizing principle for cooperative learning

Balduzzi, D., Ortega, P., Besserve, M.

Advances in Complex Systems, 16(02n03):1350012, 2013 (article)

Balduzzi, D., Ortega, P., Besserve, M. Metabolic cost as an organizing principle for cooperative learning Advances in Complex Systems, 16(02n03):1350012, 2013 (article)

MR-based PET Attenuation Correction for PET/MR Imaging

Bezrukov, I., Mantlik, F., Schmidt, H., Schölkopf, B., Pichler, B.

Seminars in Nuclear Medicine, 43(1):45-59, 2013 (article)

Bezrukov, I., Mantlik, F., Schmidt, H., Schölkopf, B., Pichler, B. MR-based PET Attenuation Correction for PET/MR Imaging Seminars in Nuclear Medicine, 43(1):45-59, 2013 (article)

MR-based Attenuation Correction Methods for Improved PET Quantification in Lesions within Bone and Susceptibility Artifact Regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Schölkopf, B., Pichler, B.

Journal of Nuclear Medicine, 54(10):1768-1774, 2013 (article)

Abstract

Hybrid PET/MR systems have recently entered clinical practice. Thus, the accuracy of MR-based attenuation correction in simultaneously acquired data can now be investigated. We assessed the accuracy of 4 methods of MR-based attenuation correction in lesions within soft tissue, bone, and MR susceptibility artifacts: 2 segmentation-based methods (SEG1, provided by the manufacturer, and SEG2, a method with atlas-based susceptibility artifact correction); an atlas- and pattern recognition–based method (AT&PR), which also used artifact correction; and a new method combining AT&PR and SEG2 (SEG2wBONE). Methods: Attenuation maps were calculated for the PET/MR datasets of 10 patients acquired on a whole-body PET/MR system, allowing for simultaneous acquisition of PET and MR data. Eighty percent iso-contour volumes of interest were placed on lesions in soft tissue (n = 21), in bone (n = 20), near bone (n = 19), and within or near MR susceptibility artifacts (n = 9). Relative mean volume-of-interest differences were calculated with CT-based attenuation correction as a reference. Results: For soft-tissue lesions, none of the methods revealed a significant difference in PET standardized uptake value relative to CT-based attenuation correction (SEG1, −2.6% ± 5.8%; SEG2, −1.6% ± 4.9%; AT&PR, −4.7% ± 6.5%; SEG2wBONE, 0.2% ± 5.3%). For bone lesions, underestimation of PET standardized uptake values was found for all methods, with minimized error for the atlas-based approaches (SEG1, −16.1% ± 9.7%; SEG2, −11.0% ± 6.7%; AT&PR, −6.6% ± 5.0%; SEG2wBONE, −4.7% ± 4.4%). For lesions near bone, underestimations of lower magnitude were observed (SEG1, −12.0% ± 7.4%; SEG2, −9.2% ± 6.5%; AT&PR, −4.6% ± 7.8%; SEG2wBONE, −4.2% ± 6.2%). For lesions affected by MR susceptibility artifacts, quantification errors could be reduced using the atlas-based artifact correction (SEG1, −54.0% ± 38.4%; SEG2, −15.0% ± 12.2%; AT&PR, −4.1% ± 11.2%; SEG2wBONE, 0.6% ± 11.1%). Conclusion: For soft-tissue lesions, none of the evaluated methods showed statistically significant errors. For bone lesions, significant underestimations of −16% and −11% occurred for methods in which bone tissue was ignored (SEG1 and SEG2). In the present attenuation correction schemes, uncorrected MR susceptibility artifacts typically result in reduced attenuation values, potentially leading to highly reduced PET standardized uptake values, rendering lesions indistinguishable from background. While AT&PR and SEG2wBONE show accurate results in both soft tissue and bone, SEG2wBONE uses a two-step approach for tissue classification, which increases the robustness of prediction and can be applied retrospectively if more precision in bone areas is needed.

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Schölkopf, B., Pichler, B. MR-based Attenuation Correction Methods for Improved PET Quantification in Lesions within Bone and Susceptibility Artifact Regions Journal of Nuclear Medicine, 54(10):1768-1774, 2013 (article)

Learning output kernels for multi-task problems

Dinuzzo, F.

Neurocomputing, 118, pages: 119-126, 2013 (article)

Dinuzzo, F. Learning output kernels for multi-task problems Neurocomputing, 118, pages: 119-126, 2013 (article)

Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

Burger, HC. Modelling and Learning Approaches to Image Denoising Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

Analytical probabilistic modeling for radiation therapy treatment planning

Bangert, M., Hennig, P., Oelfke, U.

Physics in Medicine and Biology, 58(16):5401-5419, 2013 (article)

Bangert, M., Hennig, P., Oelfke, U. Analytical probabilistic modeling for radiation therapy treatment planning Physics in Medicine and Biology, 58(16):5401-5419, 2013 (article)

Imaging Findings and Therapy Response Monitoring in Chronic Sclerodermatous Graft-Versus-Host Disease: Preliminary Data of a Simultaneous PET/MRI Approach

Sauter, A., Schmidt, H., Mantlik, F., Kolb, A., Federmann, B., Pfannenberg, C., Reimold, M., Pichler, B., Bethge, W., Horger, M.

Clinical Nuclear Medicine, 38(8):e309-e317, 2013 (article)

Abstract

PURPOSE: Our objective was a multifunctional imaging approach of chronic sclerodermatous graft-versus-host disease (ScGVHD) and its course during therapy using PET/MRI. METHODS: We performed partial-body PET/CT and PET/MRI of the calf in 6 consecutively recruited patients presenting with severe ScGVHD. The patients were treated with different immunosuppressive regimens and supportive therapies. PET/CT scanning started 60.5 +/- 3.3 minutes, PET/MRI imaging 139.5 +/- 16.7 minutes after F-FDG application. MRI acquisition included T1- (precontrast and postcontrast) and T2-weighted sequences. SUVmean, T1 contrast enhancement, and T2 signal intensity from region-of-interest analysis were calculated for different fascial and muscular compartments. In addition, musculoskeletal MRI findings and the modified Rodnan skin score were assessed. All patients underwent imaging follow-up. RESULTS: At baseline PET/MRI, ScGVHD-related musculoskeletal abnormalities consisted of increased signal and/or thickening of involved anatomical structures on T2-weighted and T1 postcontrast images as well as an increased FDG uptake. At follow-up, ScGVHD-related imaging findings decreased (SUVmean n = 4, mean T1 contrast enhancement n = 5, mean T2 signal intensity n = 3) or progressed (SUVmean n = 3, mean T1 contrast enhancement n = 2, mean T2 signal intensity n = 4). Clinically modified Rodnan skin score improved for 5 follow-ups and progressed for 2. SUVmean values correlated between PET/CT and PET/MRI acquisition (r = 0.660, P = 0.014), T1 contrast enhancement, and T2 signal (r = 0.668, P = 0.012), but not between the SUVmean values and the MRI parameters. CONCLUSIONS: PET/MRI as a combined morphological and functional technique seems to assess the inflammatory processes from different points of view and provides therefore in part complementary information

Sauter, A., Schmidt, H., Mantlik, F., Kolb, A., Federmann, B., Pfannenberg, C., Reimold, M., Pichler, B., Bethge, W., Horger, M. Imaging Findings and Therapy Response Monitoring in Chronic Sclerodermatous Graft-Versus-Host Disease: Preliminary Data of a Simultaneous PET/MRI Approach Clinical Nuclear Medicine, 38(8):e309-e317, 2013 (article)