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2015


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Efficient single-cell poration by microsecond laser pulses

Fan, Q., Hu, W., Ohta, A. T.

Lab on a Chip, 15(2):581-588, Royal Society of Chemistry, 2015 (article)

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[BibTex]

2015


[BibTex]


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Multilayer Fresnel zone plates for X-ray microscopy

Sanli, U. T., Keskinbora, K., Grévent, C., Szeghalmi, A., Knez, M., Schütz, G.

{Microscopy and Microanalysis}, 21(Suppl 3):1987-1988, Springer-Verlag New York, New York, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Interfacial dominated ferromagnetism in nanograined ZnO: a \muSR and DFT study

Tietze, T., Audehm, P., Chen, Y., Schütz, G., Straumal, B. B., Protasova, S. G., Mazilkin, A. A., Straumal, P. B., Prokscha, T., Luetkens, H., Salman, Z., Suter, A., Baretzky, B., Fink, K., Wenzel, W., Danilov, D., Goering, E.

{Scientific Reports}, 5, pages: 8871-8876, Nature Publishing Group, London, UK, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Preparation of a ferromagnetic barrier in YBa2Cu3O7-delta thinner than the coherence length

Soltan, S., Albrecht, J., Goering, E., Schütz, G., Mustafa, L., Keimer, B., Habermeier, H.

{Journal of Applied Physics}, 118(22), AIP Publishing, New York, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Microanalytical methods for in-situ high-resolution analysis of rock varnish at the micrometer to nanometer scale

Macholdt, D. S., Jochum, K. P., Pöhlker, C., Stoll, B., Weis, U., Weber, B., Müller, M., Kapl, M., Buhre, S., Kilcoyne, A. L. D., Weigand, M., Scholz, D., Al-Amri, A. M., Andreae, M. O.

{Chemical Geology}, 411, pages: 57-68, Elsevier, Amsterdam, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Chemical composition, microstructure, and hygroscopic properties of aerosol particles at the Zotino Tall Tower Observatory (ZOTTO), Siberia, during a summer campaign

Mikhailov, E. F., Mironov, G. N., Pöhlker, C., Chi, X., Krüger, M., Shiraiwa, M., Förster, J., Pöschl, U., Vlasenko, S. S., Ryshkevich, T. I., Weigand, M., Kilcoyne, A. L. D., Andreae, M.

{Atmospheric Chemistry and Physics}, 15(15):8847-8869, European Geosciences Union, Katlenburg-Lindau, Germany, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Orbital reflectometry of PrNiO3/PrAlO3 superlattices

Wu, M., Benckiser, E., Audehm, P., Goering, E., Wochner, P., Christiani, G., Logvenov, G., Habermeier, H., Keimer, B.

{Physical Review B}, 91(19), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Dynamic domain wall chirality rectification by rotating magnetic fields

Bisig, A., Mawass, M., Stärk, M., Moutafis, C., Rhensius, J., Heidler, J., Gliga, S., Weigand, M., Tyliszczak, T., Van Waeyenberge, B., Stoll, H., Schütz, G., Kläui, M.

{Applied Physics Letters}, 106(12), American Institute of Physics, Melville, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Imaging spin dynamics on the nanoscale using X-ray microscopy

Stoll, H., Noske, M., Weigand, M., Richter, K., Krüger, B., Reeve, R. M., Hänze, M., Adolff, C. F., Stein, F., Meier, G., Kläui, M., Schütz, G.

{Frontiers in Physics}, 3, Frontiers Media, Lausanne, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Structure Learning in Bayesian Sensorimotor Integration

Genewein, T, Hez, E, Razzaghpanah, Z, Braun, DA

PLoS Computational Biology, 11(8):1-27, August 2015 (article)

Abstract
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

ei

DOI [BibTex]

DOI [BibTex]


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Unique high-temperature performance of highly consensed MnBi permanent magnets

Chen, Y., Gregori, G., Leineweber, A., Qu, F., Chen, C., Tietze, T., Kronmüller, H., Schütz, G., Goering, E.

{Scripta Materialia}, 107, pages: 131-135, Pergamon, Tarrytown, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Quantifying Emergent Behavior of Autonomous Robots

Martius, G., Olbrich, E.

Entropy, 17(10):7266, 2015 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Electrical determination of vortex state in submicron magnetic elements

Gangwar, A., Bauer, H. G., Chauleau, J., Noske, M., Weigand, M., Stoll, H., Schütz, G., Back, C. H.

{Physical Review B}, 91(9), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Mechanisms for the symmetric and antisymmetric switching of a magnetic vortex core: Differences and common aspects

Noske, M., Stoll, H., Fähnle, M., Hertel, R., Schütz, G.

{Physical Review B}, 91(1), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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High resolution, high efficiency mulitlayer Fresnel zone plates for soft and hard X-rays

Sanli, U., Keskinbora, K., Gregorczyk, K., Leister, J., Teeny, N., Grévent, C., Knez, M., Schütz, G.

{Proceedings of SPIE}, 9592, SPIE, Bellingham, Washington, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Macroscopic drift current in the inverse Faraday effect

Hertel, R., Fähnle, M.

{Physical Review B}, 91(2), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Single-step 3D nanofabrication of kinoform optics via gray-scale focused ion beam lithography for efficient X-ray focusing

Keskinbora, K., Grévent, C., Hirscher, M., Weigand, M., Schütz, G.

{Advanced Optical Materials}, 3, pages: 792-800, WILEY-VCH Verlag GmbH Co. KGaA, Weinheim, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Band structure engineering of two-dimensional magnonic vortex crystals

Behncke, C., Hänze, M., Adolff, C. F., Weigand, M., Meier, G.

{Physical Review B}, 91(22), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter

Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., Fähnle, M.

{Ultramicroscopy}, 148, pages: 115-122, North-Holland, Amsterdam, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker

Leibfried, F, Braun, DA

Neural Computation, 27(8):1686-1720, July 2015 (article)

Abstract
Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

ei

DOI [BibTex]

DOI [BibTex]


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Magnetic moments induce strong phonon renormalization in FeSi

Krannich, S., Sidis, Y., Lamago, D., Heid, R., Mignot, J., von Löhneysen, H., Ivanov, A., Steffens, P., Keller, T., Wang, L., Goering, E., Weber, F.

{Nature Communications}, 6, Nature Publishing Group, London, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Model-based strategy selection learning

Lieder, F., Griffiths, T. L.

The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (article)

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Project Page [BibTex]

Project Page [BibTex]


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What is epistemic value in free energy models of learning and acting? A bounded rationality perspective

Ortega, PA, Braun, DA

Cognitive Neuroscience, 6(4):215-216, December 2015 (article)

Abstract
Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.

ei

DOI [BibTex]

DOI [BibTex]


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Perpendicular magnetisation from in-plane fields in nano-scaled antidot lattices

Gräfe, J., Haering, F., Tietze, T., Audehm, P., Weigand, M., Wiedwald, U., Ziemann, P., Gawronski, P., Schütz, G., Goering, E. J.

{Nanotechnology}, 26(22), IOP Pub., Bristol, UK, 2015 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Theory of ultrafast demagnetization after femtosecond laser pulses

Fähnle, M., Illg, C., Haag, M., Teeny, N.

{Acta Physica Polonica A}, 127(2):170-175, Państwowe Wydawnictwo Naukowe, Warszawa, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Non-linear radial spinwave modes in thin magnetic disks

Helsen, M., Gangwar, Ajay, De Clercq, J., Vansteenkiste, A., Weigand, M., Back, C. H., Van Waeyenberge, B.

{Applied Physics Letters}, 106(3), American Institute of Physics, Melville, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Hydrogen isotope separation in metal-organic frameworks: Kinetic or chemical affinity quantum-sieving?

Savchenko, I., Mavrandonakis, A., Heine, T., Oh, H., Teufel, J., Hirscher, M.

{Microporous and Mesoporous Materials}, 216, pages: 133-137, Elsevier, Amsterdam, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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High-resolution dichroic imaging of magnetic flux distributions in superconductors with scanning x-ray microscopy

Ruoß, S., Stahl, C., Weigand, M., Schütz, G., Albrecht, J.

{Applied Physics Letters}, 106, American Institute of Physics, Melville, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Preparation and characterisation of epitaxial Pt/Cu/FeMn/Co thin films on (100)-oriented MgO single crystals

Schmidt, M., Gräfe, J., Audehm, P., Phillipp, F., Schütz, G., Goering, E.

{Physica Status Solidi A}, 212(10):2114-2123, Wiley-VCH, Weinheim, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Probing the magnetic moments of [MnIII6CrIII]3+ single-molecule magnets - A cross comparison of XMCD and spin-resolved electron spectroscopy

Helmstedt, A., Dohmeier, N., Müller, N., Gryzia, A., Brechling, A., Heinzmann, U., Hoeke, V., Krickemeyer, E., Glaser, T., Leicht, P., Fonin, M., Tietze, T., Joly, L., Kuepper, K.

{Journal of Electron Spectroscopy and Related Phenomena}, 198, pages: 12-19, Elsevier B.V., Amsterdam, 2015 (article)

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DOI [BibTex]

DOI [BibTex]

2003


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Learning Control and Planning from the View of Control Theory and Imitation

Peters, J., Schaal, S.

NIPS Workshop "Planning for the Real World: The promises and challenges of dealing with uncertainty", December 2003 (talk)

Abstract
Learning control and planning in high dimensional continuous state-action systems, e.g., as needed in a humanoid robot, has so far been a domain beyond the applicability of generic planning techniques like reinforcement learning and dynamic programming. This talk describes an approach we have taken in order to enable complex robotics systems to learn to accomplish control tasks. Adaptive learning controllers equipped with statistical learning techniques can be used to learn tracking controllers -- missing state information and uncertainty in the state estimates are usually addressed by observers or direct adaptive control methods. Imitation learning is used as an ingredient to seed initial control policies whose output is a desired trajectory suitable to accomplish the task at hand. Reinforcement learning with stochastic policy gradients using a natural gradient forms the third component that allows refining the initial control policy until the task is accomplished. In comparison to general learning control, this approach is highly prestructured and thus more domain specific. However, it seems to be a theoretically clean and feasible strategy for control systems of the complexity that we need to address.

ei

Web [BibTex]

2003


Web [BibTex]


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Molecular phenotyping of human chondrocyte cell lines T/C-28a2, T/C-28a4, and C-28/I2

Finger, F., Schorle, C., Zien, A., Gebhard, P., Goldring, M., Aigner, T.

Arthritis & Rheumatism, 48(12):3395-3403, December 2003 (article)

ei

[BibTex]

[BibTex]


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A Study on Rainfall - Runoff Models for Improving Ensemble Streamflow Prediction: 1. Rainfallrunoff Models Using Artificial Neural Networks

Jeong, D., Kim, Y., Cho, S., Shin, H.

Journal of the Korean Society of Civil Engineers, 23(6B):521-530, December 2003 (article)

Abstract
The previous ESP (Ensemble Streamflow Prediction) studies conducted in Korea reported that the modeling error is a major source of the ESP forecast error in winter and spring (i.e. dry seasons), and thus suggested that improving the rainfall-runoff model would be critical to obtain more accurate probabilistic forecasts with ESP. This study used two types of Artificial Neural Networks (ANN), such as a Single Neural Network (SNN) and an Ensemble Neural Networks (ENN), to improve the simulation capability of the rainfall-runoff model of the ESP forecasting system for the monthly inflow to the Daecheong dam. Applied for the first time to Korean hydrology, ENN combines the outputs of member models so that it can control the generalization error better than SNN. Because the dry and the flood season in Korea shows considerably different streamflow characteristics, this study calibrated the rainfall-runoff model separately for each season. Therefore, four rainfall-runoff models were developed according to the ANN types and the seasons. This study compared the ANN models with a conceptual rainfall-runoff model called TANK and verified that the ANN models were superior to TANK. Among the ANN models, ENN was more accurate than SNN. The ANN model performance was improved when the model was calibrated separately for the dry and the flood season. The best ANN model developed in this article will be incorporated into the ESP system to increase the forecast capability of ESP for the monthly inflow to the Daecheong dam.

ei

[BibTex]

[BibTex]


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Quantitative Cerebral Blood Flow Measurements in the Rat Using a Beta-Probe and H215O

Weber, B., Spaeth, N., Wyss, M., Wild, D., Burger, C., Stanley, R., Buck, A.

Journal of Cerebral Blood Flow and Metabolism, 23(12):1455-1460, December 2003 (article)

Abstract
Beta-probes are a relatively new tool for tracer kinetic studies in animals. They are highly suited to evaluate new positron emission tomography tracers or measure physiologic parameters at rest and after some kind of stimulation or intervention. In many of these experiments, the knowledge of CBF is highly important. Thus, the purpose of this study was to evaluate the method of CBF measurements using a beta-probe and H215O. CBF was measured in the barrel cortex of eight rats at baseline and after acetazolamide challenge. Trigeminal nerve stimulation was additionally performed in five animals. In each category, three injections of 250 to 300 MBq H215O were performed at 10-minute intervals. Data were analyzed using a standard one-tissue compartment model (K1 = CBF, k2 = CBF/p, where p is the partition coefficient). Values for K1 were 0.35 plusminus 0.09, 0.58 plusminus 0.16, and 0.49 plusminus 0.03 mL dot min-1 dot mL-1 at rest, after acetazolamide challenge, and during trigeminal nerve stimulation, respectively. The corresponding values for k2 were 0.55 plusminus 0.12, 0.94 plusminus 0.16, and 0.85 plusminus 0.12 min-7, and for p were 0.64 plusminus 0.05, 0.61 plusminus 0.07, and 0.59 plusminus 0.06.The standard deviation of the difference between two successive experiments, a measure for the reproducibility of the method, was 10.1%, 13.0%, and 5.7% for K1, k2, and p, respectively. In summary, beta-probes in conjunction with H215O allow the reproducible quantitative measurement of CBF, although some systematic underestimation seems to occur, probably because of partial volume effects.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Recurrent neural networks from learning attractor dynamics

Schaal, S., Peters, J.

NIPS Workshop on RNNaissance: Recurrent Neural Networks, December 2003 (talk)

Abstract
Many forms of recurrent neural networks can be understood in terms of dynamic systems theory of difference equations or differential equations. Learning in such systems corresponds to adjusting some internal parameters to obtain a desired time evolution of the network, which can usually be characterized in term of point attractor dynamics, limit cycle dynamics, or, in some more rare cases, as strange attractor or chaotic dynamics. Finding a stable learning process to adjust the open parameters of the network towards shaping the desired attractor type and basin of attraction has remain a complex task, as the parameter trajectories during learning can lead the system through a variety of undesirable unstable behaviors, such that learning may never succeed. In this presentation, we review a recently developed learning framework for a class of recurrent neural networks that employs a more structured network approach. We assume that the canonical system behavior is known a priori, e.g., it is a point attractor or a limit cycle. With either supervised learning or reinforcement learning, it is possible to acquire the transformation from a simple representative of this canonical behavior (e.g., a 2nd order linear point attractor, or a simple limit cycle oscillator) to the desired highly complex attractor form. For supervised learning, one shot learning based on locally weighted regression techniques is possible. For reinforcement learning, stochastic policy gradient techniques can be employed. In any case, the recurrent network learned by these methods inherits the stability properties of the simple dynamic system that underlies the nonlinear transformation, such that stability of the learning approach is not a problem. We demonstrate the success of this approach for learning various skills on a humanoid robot, including tasks that require to incorporate additional sensory signals as coupling terms to modify the recurrent network evolution on-line.

ei

Web [BibTex]

Web [BibTex]


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Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation

Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K.

Journal of Machine Learning Research, 4(7-8):1319-1338, November 2003 (article)

Abstract
We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm--a powerful technique from non-parametric statistics--to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearities. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Correlated stage- and subfield-associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy

Becker, A., Chen, J., Zien, A., Sochivko, D., Normann, S., Schramm, J., Elger, C., Wiestler, O., Blumcke, I.

European Journal of Neuroscience, 18(10):2792-2802, November 2003 (article)

Abstract
Epileptic activity evokes profound alterations of hippocampal organization and function. Genomic responses may reflect immediate consequences of excitatory stimulation as well as sustained molecular processes related to neuronal plasticity and structural remodeling. Using oligonucleotide microarrays with 8799 sequences, we determined subregional gene expression profiles in rats subjected to pilocarpine-induced epilepsy (U34A arrays, Affymetrix, Santa Clara, CA, USA; P < 0.05, twofold change, n = 3 per stage). Patterns of gene expression corresponded to distinct stages of epilepsy development. The highest number of differentially expressed genes (dentate gyrus, approx. 400 genes and CA1, approx. 700 genes) was observed 3 days after status epilepticus. The majority of up-regulated genes was associated with mechanisms of cellular stress and injury - 14 days after status epilepticus, numerous transcription factors and genes linked to cytoskeletal and synaptic reorganization were differentially expressed and, in the stage of chronic spontaneous seizures, distinct changes were observed in the transcription of genes involved in various neurotransmission pathways and between animals with low vs. high seizure frequency. A number of genes (n = 18) differentially expressed during the chronic epileptic stage showed corresponding expression patterns in hippocampal subfields of patients with pharmacoresistant temporal lobe epilepsy (n = 5 temporal lobe epilepsy patients; U133A microarrays, Affymetrix; covering 22284 human sequences). These data provide novel insights into the molecular mechanisms of epileptogenesis and seizure-associated cellular and structural remodeling of the hippocampus.

ei

[BibTex]

[BibTex]


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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

PostScript [BibTex]


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YKL-39 (chitinase 3-like protein 2), but not YKL-40 (chitinase 3-like protein 1), is up regulated in osteoarthritic chondrocytes

Knorr, T., Obermayr, F., Bartnik, E., Zien, A., Aigner, T.

Annals of the Rheumatic Diseases, 62(10):995-998, October 2003 (article)

Abstract
OBJECTIVE: To investigate quantitatively the mRNA expression levels of YKL-40, an established marker of rheumatoid and osteoarthritic cartilage degeneration in synovial fluid and serum, and a closely related molecule YKL-39, in articular chondrocytes. METHODS: cDNA array and online quantitative polymerase chain reaction (PCR) were used to measure mRNA expression levels of YKL-39 and YKL-40 in chondrocytes in normal, early degenerative, and late stage osteoarthritic cartilage samples. RESULTS: Expression analysis showed high levels of both proteins in normal articular chondrocytes, with lower levels of YKL-39 than YKL-40. Whereas YKL-40 was significantly down regulated in late stage osteoarthritic chondrocytes, YKL-39 was significantly up regulated. In vitro both YKLs were down regulated by interleukin 1beta. CONCLUSIONS: The up regulation of YKL-39 in osteoarthritic cartilage suggests that YKL-39 may be a more accurate marker of chondrocyte activation than YKL-40, although it has yet to be established as a suitable marker in synovial fluid and serum. The decreased expression of YKL-40 by osteoarthritic chondrocytes is surprising as increased levels have been reported in rheumatoid and osteoarthritic synovial fluid, where it may derive from activated synovial cells or osteophytic tissue or by increased matrix destruction in the osteoarthritic joint. YKL-39 and YKL-40 are potentially interesting marker molecules for arthritic joint disease because they are abundantly expressed by both normal and osteoarthritic chondrocytes.

ei

[BibTex]

[BibTex]


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Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Learning the statistics of people in images and video

Sidenbladh, H., Black, M. J.

International Journal of Computer Vision, 54(1-3):183-209, August 2003 (article)

Abstract
This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a “background” distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.

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pdf pdf from publisher code DOI [BibTex]

pdf pdf from publisher code DOI [BibTex]


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A framework for robust subspace learning

De la Torre, F., Black, M. J.

International Journal of Computer Vision, 54(1-3):117-142, August 2003 (article)

Abstract
Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multi-linear models. These models have been widely used for the representation of shape, appearance, motion, etc., in computer vision applications. Methods for learning linear models can be seen as a special case of subspace fitting. One draw-back of previous learning methods is that they are based on least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. We review previous approaches for making linear learning methods robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Subspace Learning (RSL) for linear models within a continuous optimization framework based on robust M-estimation. The framework applies to a variety of linear learning problems in computer vision including eigen-analysis and structure from motion. Several synthetic and natural examples are used to develop and illustrate the theory and applications of robust subspace learning in computer vision.

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pdf code pdf from publisher Project Page [BibTex]

pdf code pdf from publisher Project Page [BibTex]


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Guest editorial: Computational vision at Brown

Black, M. J., Kimia, B.

International Journal of Computer Vision, 54(1-3):5-11, August 2003 (article)

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pdf pdf from publisher [BibTex]

pdf pdf from publisher [BibTex]


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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]


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Robust parameterized component analysis: Theory and applications to 2D facial appearance models

De la Torre, F., Black, M. J.

Computer Vision and Image Understanding, 91(1-2):53-71, July 2003 (article)

Abstract
Principal component analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion in images. In particular, PCA has been widely used to model the variation in the appearance of people's faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we consider person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in previous work, this has involved a time consuming and error-prone hand alignment and cropping process. Instead, the main contribution of this paper is to introduce parameterized component analysis to learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques achieving sub-pixel accuracy. We illustrate the use of the 2D PSFAM model with preliminary experiments relevant to applications including video-conferencing and avatar animation.

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pdf [BibTex]

pdf [BibTex]


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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

DOI [BibTex]


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Kernel-based nonlinear blind source separation

Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.

Neural Computation, 15(5):1089-1124, May 2003 (article)

Abstract
We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]