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2018


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Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex

Kapoor, V., Besserve, M., Logothetis, N. K., Panagiotaropoulos, T. I.

Communications Biology, 1(215):1-12, December 2018 (article)

ei

link (url) DOI Project Page [BibTex]

2018


link (url) DOI Project Page [BibTex]


Thumb xl screen shot 2019 01 07 at 12.05.00
Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract
Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

ei

RAL18final link (url) DOI Project Page [BibTex]

RAL18final link (url) DOI Project Page [BibTex]


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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F.

IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G.

Frontiers in Neurorobotics, 12, May 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Manschitz, S., Gienger, M., Kober, J., Peters, J.

IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Probabilistic movement primitives under unknown system dynamics

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

Advanced Robotics, 32(6):297-310, April 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.

Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

Autonomous Robots, 42(3):529-551, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.

Autonomous Robots, 42(3):581-600, March 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.

IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

ei slt

arXiv link (url) Project Page Project Page [BibTex]

arXiv link (url) Project Page Project Page [BibTex]


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A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

ei

Project Page [BibTex]

Project Page [BibTex]


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Does universal controllability of physical systems prohibit thermodynamic cycles?

Janzing, D., Wocjan, P.

Open Systems and Information Dynamics, 25(3):1850016, 2018 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning Causality and Causality-Related Learning: Some Recent Progress

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.

National Science Review, 5(1):26-29, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Online optimal trajectory generation for robot table tennis

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

Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

ei

PDF link (url) DOI Project Page [BibTex]

PDF link (url) DOI Project Page [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

ei pn

arXiv [BibTex]

arXiv [BibTex]


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Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Osa, T., Peters, J., Neumann, G.

Advanced Robotics, 32(18):955-968, 2018 (article)

ei

DOI Project Page [BibTex]


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Autofocusing-based phase correction

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Case series: Slowing alpha rhythm in late-stage ALS patients

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Clinical Neurophysiology, 129(2):406-408, 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.

Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Quantum machine learning: a classical perspective

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L.

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209):20170551, 2018 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 15(6):066011, 2018 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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f-Divergence constrained policy improvement

Belousov, B., Peters, J.

Journal of Machine Learning Research, 2018 (article) Submitted

ei

Project Page [BibTex]

Project Page [BibTex]


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Phylogenetic convolutional neural networks in metagenomics

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C.

BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

ei

DOI [BibTex]

DOI [BibTex]


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Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A.

International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Linking imaging to omics utilizing image-guided tissue extraction

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J.

Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M.

Neuron, 100(5):1224-1240, 2018 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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In-Hand Object Stabilization by Independent Finger Control

Veiga, F. F., Edin, B. B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

ei

Project Page [BibTex]

Project Page [BibTex]


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Visualizing and understanding Sum-Product Networks

Vergari, A., Di Mauro, N., Esposito, F.

Machine Learning, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Krüger, M, Braun, DA

Entropy, 20(1:1):1-28, January 2018 (article)

Abstract
Living organisms from single cells to humans need to adapt continuously to respond to changes in their environment. The process of behavioural adaptation can be thought of as improving decision-making performance according to some utility function. Here, we consider an abstract model of organisms as decision-makers with limited information-processing resources that trade off between maximization of utility and computational costs measured by a relative entropy, in a similar fashion to thermodynamic systems undergoing isothermal transformations. Such systems minimize the free energy to reach equilibrium states that balance internal energy and entropic cost. When there is a fast change in the environment, these systems evolve in a non-equilibrium fashion because they are unable to follow the path of equilibrium distributions. Here, we apply concepts from non-equilibrium thermodynamics to characterize decision-makers that adapt to changing environments under the assumption that the temporal evolution of the utility function is externally driven and does not depend on the decision-maker’s action. This allows one to quantify performance loss due to imperfect adaptation in a general manner and, additionally, to find relations for decision-making similar to Crooks’ fluctuation theorem and Jarzynski’s equality. We provide simulations of several exemplary decision and inference problems in the discrete and continuous domains to illustrate the new relations.

ei

DOI [BibTex]

DOI [BibTex]

2011


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Combined whole-body PET/MR imaging: MR contrast agents do not affect the quantitative accuracy of PET following attenuation correction

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

(SST15-05 ), 97th Scientific Assemble and Annual Meeting of the Radiological Society of North America (RSNA), December 2011 (talk)

Abstract
PURPOSE Combined PET/MR imaging entails the use of MR contrast agents (MRCA) as part of integrated protocols. We assess additional attenuation of the PET emission signals in the presence of oral and intraveneous (iv) MRCA made up of iron oxide and Gd-chelates, respectively. METHOD AND MATERIALS Phantom scans were performed on a clinical PET/CT (Biograph HiRez16, Siemens) and integrated whole-body PET/MR (Biograph mMR, Siemens) using oral (Lumirem) and intraveneous (Gadovist) MRCA. Reference PET attenuation values were determined on a small-animal PET (Inveon, Siemens) using standard PET transmission imaging (TX). Seven syringes of 5mL were filled with (a) Water, (b) Lumirem_100 (100% conc.), (c) Gadovist_100 (100%), (d) Gadovist_18 (18%), (e) Gadovist_02 (0.2%), (f) Imeron-400 CT iv-contrast (100%) and (g) Imeron-400 (2.4%). The same set of syringes was scanned on CT (Sensation16, Siemens) at 120kVp and 160mAs. The effect of MRCA on the attenuation of PET emission data was evaluated using a 20cm cylinder filled uniformly with [18F]-FDG (FDG) in water (BGD). Three 4.5cm diameter cylinders were inserted into the phantom: (C1) Teflon, (C2) Water+FDG (2:1) and (C3) Lumirem_100+FDG (2:1). Two 50mL syringes filled with Gadovist_02+FDG (Sy1) and water+FDG (Sy2) were attached to the sides of (C1) to mimick the effects of iv-contrast in vessels near bone. Syringe-to-background activity ratio was 4-to-1. PET emission data were acquired for 10min each using the PET/CT and the PET/MR. Images were reconstructed using CT- and MR-based attenuation correction. RESULTS Mean linear PET attenuation (cm-1) on TX was (a) 0.098, (b) 0.098, (c) 0.300, (d) 0.134, (e) 0.095, (f) 0.397 and (g) 0.105. Corresponding CT attenuation (HU) was: (a) 5, (b) 14, (c) 3070, (d) 1040, (e) 13, (f) 3070 and (g) 347. Lumirem had little effect on PET attenuation with (C3) being 13% and 10% higher than (C2) on PET/CT and PET/MR, respectively. Gadovist_02 had even smaller effects with (Sy1) being 2.5% lower than (Sy2) on PET/CT and 1.2% higher than (Sy2) on PET/MR. CONCLUSION MRCA in high and clinically relevant concentrations have attenuation values similar to that of CT contrast and water, respectively. In clinical PET/MR scenarios MRCA are not expected to lead to significant attenuation of the PET emission signals.

ei

Web [BibTex]

2011


Web [BibTex]


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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]

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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Cooperative Cuts: a new use of submodularity in image segmentation

Jegelka, S.

Second I.S.T. Austria Symposium on Computer Vision and Machine Learning, October 2011 (talk)

ei

Web [BibTex]

Web [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., Pichler, B., Kupferschläger, J., Beyer, T.

2011(MIC3-3), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Abstract
Combined whole-body PET/MR systems are being tested in clinical practice today. Integrated imaging protocols entail the use of MR contrast agents (MRCA) that could bias PET attenuation correction. In this work, we assess the effect of MRCA in PET/MR imaging. We analyze the effect of oral and intravenous MRCA on PET activity after attenuation correction. We conclude that in clinical scenarios, MRCA are not expected to lead to significant attenuation of PET signals, and that attenuation maps are not biased after the ingestion of adequate oral contrasts.

ei

Web [BibTex]

Web [BibTex]


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First Results on Patients and Phantoms of a Fully Integrated Clinical Whole-Body PET/MRI

Schmidt, H., Schwenzer, N., Bezrukov, I., Kolb, A., Mantlik, F., Kupferschläger, J., Lois, C., Sauter, A., Brendle, C., Pfannenberg, C., Pichler, B.

2011(J2-8), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Abstract
First clinical fully integrated whole-body PET/MR scanners are just entering the field. Here, we present studies toward quantification accuracy and variation within the PET field of view of small lesions from our BrainPET/MRI, a dedicated clinical brain scanner which was installed three years ago in Tbingen. Also, we present first results for patient and phantom scans of a fully integral whole-body PET/MRI, which was installed two months ago at our department. The quantification accuracy and homogeneity of the BrainPET-Insert (Siemens Medical Solutions, Germany) installed inside the magnet bore of a clinical 3T MRI scanner (Magnetom TIM Trio, Siemens Medical Solutions, Germany) was evaluated by using eight hollow spheres with inner diameters from 3.95 to 7.86 mm placed at different positions inside a homogeneous cylinder phantom with an 9:1 and 6:1 sphere to background ratio. The quantification accuracy for small lesions at different positions in the PET FoV shows a standard deviation of up to 11% and is acceptable for quantitative brain studies where the homogeneity of quantification on the entire FoV is essental. Image quality and resolution of the new Siemens whole-body PET/MR system (Biograph mMR, Siemens Medical Solutions, Germany) was evaluated according to the NEMA NU2 2007 protocol using a body phantom containing six spheres with inner diameter from 10 to 37 mm at sphere to background ratios of 8:1 and 4:1 and the F-18 point sources located at different positions inside the PET FoV, respectively. The evaluation of the whole-body PET/MR system reveals a good PET image quality and resolution comparable to state-of-the-art clinical PET/CT scanners. First images of patient studies carried out at the whole-body PET/MR are presented highlighting the potency of combined PET/MR imaging.

ei

Web [BibTex]

Web [BibTex]