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2019


Soft-magnetic coatings as possible sensors for magnetic imaging of superconductors
Soft-magnetic coatings as possible sensors for magnetic imaging of superconductors

Ionescu, A., Simmendinger, J., Bihler, M., Miksch, C., Fischer, P., Soltan, S., Schütz, G., Albrecht, J.

Supercond. Sci. and Tech., 33, pages: 015002, IOP, December 2019 (article)

Abstract
Magnetic imaging of superconductors typically requires a soft-magnetic material placed on top of the superconductor to probe local magnetic fields. For reasonable results the influence of the magnet onto the superconductor has to be small. Thin YBCO films with soft-magnetic coatings are investigated using SQUID magnetometry. Detailed measurements of the magnetic moment as a function of temperature, magnetic field and time have been performed for different heterostructures. It is found that the modification of the superconducting transport in these heterostructures strongly depends on the magnetic and structural properties of the soft-magnetic material. This effect is especially pronounced for an inhomogeneous coating consisting of ferromagnetic nanoparticles.

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link (url) DOI [BibTex]

2019


link (url) DOI [BibTex]


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Robot Learning for Muscular Robots

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Real Time Probabilistic Models for Robot Trajectories

Gomez-Gonzalez, S.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

[BibTex]


Acoustic hologram enhanced phased arrays for ultrasonic particle manipulation
Acoustic hologram enhanced phased arrays for ultrasonic particle manipulation

Cox, L., Melde, K., Croxford, A., Fischer, P., Drinkwater, B.

Phys. Rev. Applied, 12, pages: 064055, November 2019 (article)

Abstract
The ability to shape ultrasound fields is important for particle manipulation, medical therapeutics and imaging applications. If the amplitude and/or phase is spatially varied across the wavefront then it is possible to project ‘acoustic images’. When attempting to form an arbitrary desired static sound field, acoustic holograms are superior to phased arrays due to their significantly higher phase fidelity. However, they lack the dynamic flexibility of phased arrays. Here, we demonstrate how to combine the high-fidelity advantages of acoustic holograms with the dynamic control of phased arrays in the ultrasonic frequency range. Holograms are used with a 64-element phased array, driven with continuous excitation. Moving the position of the projected hologram via phase delays which steer the output beam is demonstrated experimentally. This allows the creation of a much more tightly focused point than with the phased array alone, whilst still being reconfigurable. It also allows the complex movement at a water-air interface of a “phase surfer” along a phase track or the manipulation of a more arbitrarily shaped particle via amplitude traps. Furthermore, a particle manipulation device with two emitters and a single split hologram is demonstrated that allows the positioning of a “phase surfer” along a 1D axis. This paper opens the door for new applications with complex manipulation of ultrasound whilst minimising the complexity and cost of the apparatus.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


A Helical Microrobot with an Optimized Propeller-Shape for Propulsion in Viscoelastic Biological Media
A Helical Microrobot with an Optimized Propeller-Shape for Propulsion in Viscoelastic Biological Media

Li., D., Jeong, M., Oren, E., Yu, T., Qiu, T.

Robotics, 8, pages: 87, MDPI, October 2019 (article)

Abstract
One major challenge for microrobots is to penetrate and effectively move through viscoelastic biological tissues. Most existing microrobots can only propel in viscous liquids. Recent advances demonstrate that sub-micron robots can actively penetrate nanoporous biological tissue, such as the vitreous of the eye. However, it is still difficult to propel a micron-sized device through dense biological tissue. Here, we report that a special twisted helical shape together with a high aspect ratio in cross-section permit a microrobot with a diameter of hundreds-of-micrometers to move through mouse liver tissue. The helical microrobot is driven by a rotating magnetic field and localized by ultrasound imaging inside the tissue. The twisted ribbon is made of molybdenum and a sharp tip is chemically etched to generate a higher pressure at the edge of the propeller to break the biopolymeric network of the dense tissue.

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link (url) DOI [BibTex]


Acoustic Holographic Cell Patterning in a Biocompatible Hydrogel
Acoustic Holographic Cell Patterning in a Biocompatible Hydrogel

Ma, Z., Holle, A., Melde, K., Qiu, T., Poeppel, K., Kadiri, V., Fischer, P.

Adv. Mat., October 2019 (article)

Abstract
Acoustophoresis is promising as a rapid, biocompatible, non-contact cell manipulation method, where cells are arranged along the nodes or antinodes of the acoustic field. Typically, the acoustic field is formed in a resonator, which results in highly symmetric regular patterns. However, arbitrary, non-symmetrically shaped cell assemblies are necessary to obtain the irregular cellular arrangements found in biological tissues. We show that arbitrarily shaped cell patterns can be obtained from the complex acoustic field distribution defined by an acoustic hologram. Attenuation of the sound field induces localized acoustic streaming and the resultant convection flow gently delivers the suspended cells to the image plane where they form the designed pattern. We show that the process can be implemented in a biocompatible collagen solution, which can then undergo gelation to immobilize the cell pattern inside the viscoelastic matrix. The patterned cells exhibit F-actin-based protrusions, which indicates that the cells grow and thrive within the matrix. Cell viability assays and brightfield imaging after one week confirm cell survival and that the patterns persist. Acoustophoretic cell manipulation by holographic fields thus holds promise for non-contact, long-range, long-term cellular pattern formation, with a wide variety of potential applications in tissue engineering and mechanobiology.

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link (url) DOI [BibTex]


A High-Fidelity Phantom for the Simulation and Quantitative Evaluation of Transurethral Resection of the Prostate
A High-Fidelity Phantom for the Simulation and Quantitative Evaluation of Transurethral Resection of the Prostate

Choi, E., Adams, F., Gengenbacher, A., Schlager, D., Palagi, S., Müller, P., Wetterauer, U., Miernik, A., Fischer, P., Qiu, T.

Annals of Biomed. Eng., October 2019 (article)

Abstract
Transurethral resection of the prostate (TURP) is a minimally invasive endoscopic procedure that requires experience and skill of the surgeon. To permit surgical training under realistic conditions we report a novel phantom of the human prostate that can be resected with TURP. The phantom mirrors the anatomy and haptic properties of the gland and permits quantitative evaluation of important surgical performance indicators. Mixtures of soft materials are engineered to mimic the physical properties of the human tissue, including the mechanical strength, the electrical and thermal conductivity, and the appearance under an endoscope. Electrocautery resection of the phantom closely resembles the procedure on human tissue. Ultrasound contrast agent was applied to the central zone, which was not detectable by the surgeon during the surgery but showed high contrast when imaged after the surgery, to serve as a label for the quantitative evaluation of the surgery. Quantitative criteria for performance assessment are established and evaluated by automated image analysis. We present the workflow of a surgical simulation on a prostate phantom followed by quantitative evaluation of the surgical performance. Surgery on the phantom is useful for medical training, and enables the development and testing of endoscopic and minimally invasive surgical instruments.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Interactive Materials – Drivers of Future Robotic Systems
Interactive Materials – Drivers of Future Robotic Systems

Fischer, P.

Adv. Mat., October 2019 (article)

Abstract
A robot senses its environment, processes the sensory information, acts in response to these inputs, and possibly communicates with the outside world. Robots generally achieve these tasks with electronics-based hardware or by receiving inputs from some external hardware. In contrast, simple microorganisms can autonomously perceive, act, and communicate via purely physicochemical processes in soft material systems. A key property of biological systems is that they are built from energy-consuming ‘active’ units. Exciting developments in material science show that even very simple artificial active building blocks can show surprisingly rich emergent behaviors. Active non-equilibrium systems are therefore predicted to play an essential role to realize interactive materials. A major challenge is to find robust ways to couple and integrate the energy-consuming building blocks to the mechanical structure of the material. However, success in this endeavor will lead to a new generation of sophisticated micro- and soft-robotic systems that can operate autonomously.

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[BibTex]


Arrays of plasmonic nanoparticle dimers with defined nanogap spacers
Arrays of plasmonic nanoparticle dimers with defined nanogap spacers

Jeong, H., Adams, M. C., Guenther, J., Alarcon-Correa, M., Kim, I., Choi, E., Miksch, C., Mark, A. F. M., Mark, A. G., Fischer, P.

ACS Nano, September 2019 (article)

Abstract
Plasmonic molecules are building blocks of metallic nanostructures that give rise to intriguing optical phenomena with similarities to those seen in molecular systems. The ability to design plasmonic hybrid structures and molecules with nanometric resolution would enable applications in optical metamaterials and sensing that presently cannot be demonstrated, because of a lack of suitable fabrication methods allowing the structural control of the plasmonic atoms on a large scale. Here we demonstrate a wafer-scale “lithography-free” parallel fabrication scheme to realize nanogap plasmonic meta-molecules with precise control over their size, shape, material, and orientation. We demonstrate how we can tune the corresponding coupled resonances through the entire visible spectrum. Our fabrication method, based on glancing angle physical vapor deposition with gradient shadowing, permits critical parameters to be varied across the wafer and thus is ideally suited to screen potential structures. We obtain billions of aligned dimer structures with controlled variation of the spectral properties across the wafer. We spectroscopically map the plasmonic resonances of gold dimer structures and show that they not only are in good agreement with numerically modeled spectra, but also remain functional, at least for a year, in ambient conditions.

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link (url) DOI [BibTex]


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Convolutional neural networks: A magic bullet for gravitational-wave detection?

Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.

Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

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

ei

DOI [BibTex]

DOI [BibTex]


Genetically modified M13 bacteriophage nanonets for enzyme catalysis and recovery
Genetically modified M13 bacteriophage nanonets for enzyme catalysis and recovery

Kadiri, V. M., Alarcon-Correa, M., Guenther, J. P., Ruppert, J., Bill, J., Rothenstein, D., Fischer, P.

Catalysts, 9, pages: 723, August 2019 (article)

Abstract
Enzyme-based biocatalysis exhibits multiple advantages over inorganic catalysts, including the biocompatibility and the unchallenged specificity of enzymes towards their substrate. The recovery and repeated use of enzymes is essential for any realistic application in biotechnology, but is not easily achieved with current strategies. For this purpose, enzymes are often immobilized on inorganic scaffolds, which could entail a reduction of the enzymes’ activity. Here, we show that immobilization to a nano-scaled biological scaffold, a nanonetwork of end-to-end cross-linked M13 bacteriophages, ensures high enzymatic activity and at the same time allows for the simple recovery of the enzymes. The bacteriophages have been genetically engineered to express AviTags at their ends, which permit biotinylation and their specific end-to-end self-assembly while allowing space on the major coat protein for enzyme coupling. We demonstrate that the phages form nanonetwork structures and that these so-called nanonets remain highly active even after re-using the nanonets multiple times in a flow-through reactor.

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link (url) DOI [BibTex]


Light-controlled micromotors and soft microrobots
Light-controlled micromotors and soft microrobots

Palagi, S., Singh, D. P., Fischer, P.

Adv. Opt. Mat., 7, pages: 1900370, August 2019 (article)

Abstract
Mobile microscale devices and microrobots can be powered by catalytic reactions (chemical micromotors) or by external fields. This report is focused on the role of light as a versatile means for wirelessly powering and controlling such microdevices. Recent advances in the development of autonomous micromotors are discussed, where light permits their actuation with unprecedented control and thereby enables advances in the field of active matter. In addition, structuring the light field is a new means to drive soft microrobots that are based on (photo‐) responsive polymers. The behavior of the two main classes of thermo‐ and photoresponsive polymers adopted in microrobotics (poly(N‐isopropylacrylamide) and liquid‐crystal elastomers) is analyzed, and recent applications are reported. The advantages and limitations of controlling micromotors and microrobots by light are reviewed, and some of the remaining challenges in the development of novel photo‐active materials for micromotors and microrobots are discussed.

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link (url) DOI [BibTex]


Self-Assembled Phage-Based Colloids for High Localized Enzymatic Activity
Self-Assembled Phage-Based Colloids for High Localized Enzymatic Activity

Alarcon-Correa, M., Guenther, J., Troll, J., Kadiri, V. M., Bill, J., Fischer, P., Rothenstein, D.

ACS Nano, March 2019 (article)

Abstract
Catalytically active colloids are model systems for chemical motors and active matter. It is desirable to replace the inorganic catalysts and the toxic fuels that are often used, with biocompatible enzymatic reactions. However, compared to inorganic catalysts, enzyme-coated colloids tend to exhibit less activity. Here, we show that the self-assembly of genetically engineered M13 bacteriophages that bind enzymes to magnetic beads ensures high and localized enzymatic activity. These phage-decorated colloids provide a proteinaceous environment for directed enzyme immobilization. The magnetic properties of the colloidal carrier particle permit repeated enzyme recovery from a reaction solution, while the enzymatic activity is retained. Moreover, localizing the phage-based construct with a magnetic field in a microcontainer allows the enzyme-phage-colloids to function as an enzymatic micropump, where the enzymatic reaction generates a fluid flow. This system shows the fastest fluid flow reported to date by a biocompatible enzymatic micropump. In addition, it is functional in complex media including blood where the enzyme driven micropump can be powered at the physiological blood-urea concentration.

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link (url) DOI [BibTex]


Absolute diffusion measurements of active enzyme solutions by NMR
Absolute diffusion measurements of active enzyme solutions by NMR

Guenther, J., Majer, G., Fischer, P.

J. Chem. Phys., 150(124201), March 2019 (article)

Abstract
The diffusion of enzymes is of fundamental importance for many biochemical processes. Enhanced or directed enzyme diffusion can alter the accessibility of substrates and the organization of enzymes within cells. Several studies based on fluorescence correlation spectroscopy (FCS) report enhanced diffusion of enzymes upon interaction with their substrate or inhibitor. In this context, major importance is given to the enzyme fructose-bisphosphate aldolase, for which enhanced diffusion has been reported even though the catalysed reaction is endothermic. Additionally, enhanced diffusion of tracer particles surrounding the active aldolase enzymes has been reported. These studies suggest that active enzymes can act as chemical motors that self-propel and give rise to enhanced diffusion. However, fluorescence studies of enzymes can, despite several advantages, suffer from artefacts. Here we show that the absolute diffusion coefficients of active enzyme solutions can be determined with Pulsed Field Gradient Nuclear Magnetic Resonance (PFG-NMR). The advantage of PFG-NMR is that the motion of the molecule of interest is directly observed in its native state without the need for any labelling. Further, PFG-NMR is model-free and thus yields absolute diffusion constants. Our PFG-NMR experiments of solutions containing active fructose-bisphosphate aldolase from rabbit muscle do not show any diffusion enhancement for the active enzymes nor the surrounding molecules. Additionally, we do not observe any diffusion enhancement of aldolase in the presence of its inhibitor pyrophosphate.

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link (url) DOI [BibTex]


Chemical Nanomotors at the Gram Scale Form a Dense Active Optorheological Medium
Chemical Nanomotors at the Gram Scale Form a Dense Active Optorheological Medium

Choudhury, U., Singh, D. P., Qiu, T., Fischer, P.

Adv. Mat., (1807382), Febuary 2019 (article)

Abstract
The rheological properties of a colloidal suspension are a function of the concentration of the colloids and their interactions. While suspensions of passive colloids are well studied and have been shown to form crystals, gels, and glasses, examples of energy‐consuming “active” colloidal suspensions are still largely unexplored. Active suspensions of biological matter, such as motile bacteria or dense mixtures of active actin–motor–protein mixtures have, respectively, reveals superfluid‐like and gel‐like states. Attractive inanimate systems for active matter are chemically self‐propelled particles. It has so far been challenging to use these swimming particles at high enough densities to affect the bulk material properties of the suspension. Here, it is shown that light‐triggered asymmetric titanium dioxide that self‐propel, can be obtained in large quantities, and self‐organize to make a gram‐scale active medium. The suspension shows an activity‐dependent tenfold reversible change in its bulk viscosity.

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link (url) DOI [BibTex]


First Observation of Optical Activity in Hyper-Rayleigh Scattering
First Observation of Optical Activity in Hyper-Rayleigh Scattering

Collins, J., Rusimova, K., Hooper, D., Jeong, H. H., Ohnoutek, L., Pradaux-Caggiano, F., Verbiest, T., Carbery, D., Fischer, P., Valev, V.

Phys. Rev. X, 9(011024), January 2019 (article)

Abstract
Chiral nano- or metamaterials and surfaces enable striking photonic properties, such as negative refractive index and superchiral light, driving promising applications in novel optical components, nanorobotics, and enhanced chiral molecular interactions with light. In characterizing chirality, although nonlinear chiroptical techniques are typically much more sensitive than their linear optical counterparts, separating true chirality from anisotropy is a major challenge. Here, we report the first observation of optical activity in second-harmonic hyper-Rayleigh scattering (HRS). We demonstrate the effect in a 3D isotropic suspension of Ag nanohelices in water. The effect is 5 orders of magnitude stronger than linear optical activity and is well pronounced above the multiphoton luminescence background. Because of its sensitivity, isotropic environment, and straightforward experimental geometry, HRS optical activity constitutes a fundamental experimental breakthrough in chiral photonics for media including nanomaterials, metamaterials, and chemical molecules.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]


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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)

ei

DOI [BibTex]

DOI [BibTex]


Multidimensional Contrast Limited Adaptive Histogram Equalization
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)

ei

arXiv link (url) DOI [BibTex]

arXiv link (url) DOI [BibTex]


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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)

ei

DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

ei

[BibTex]

[BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
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.

ei

Arxiv Video [BibTex]


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Inferring causation from time series with perspectives in Earth system sciences

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.

Nature Communications, 2019 (article) In revision

ei

[BibTex]

[BibTex]


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Quantum mean embedding of probability distributions

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

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

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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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)

ei

DOI [BibTex]

DOI [BibTex]

2009


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Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Lampert, C., Blaschko, M., Hofmann, T.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2129-2142, December 2009 (article)

Abstract
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object‘s location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the chi^2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.

ei

PDF Web DOI [BibTex]

2009


PDF Web DOI [BibTex]


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Generation of three-dimensional random rotations in fitting and matching problems

Habeck, M.

Computational Statistics, 24(4):719-731, December 2009 (article)

Abstract
An algorithm is developed to generate random rotations in three-dimensional space that follow a probability distribution arising in fitting and matching problems. The rotation matrices are orthogonally transformed into an optimal basis and then parameterized using Euler angles. The conditional distributions of the three Euler angles have a very simple form: the two azimuthal angles can be decoupled by sampling their sum and difference from a von Mises distribution; the cosine of the polar angle is exponentially distributed and thus straighforward to generate. Simulation results are shown and demonstrate the effectiveness of the method. The algorithm is compared to other methods for generating random rotations such as a random walk Metropolis scheme and a Gibbs sampling algorithm recently introduced by Green and Mardia. Finally, the algorithm is applied to a probabilistic version of the Procrustes problem of fitting two point sets and applied in the context of protein structure superposition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning

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

Neural Networks, 22(10):1399-1410, December 2009 (article)

Abstract
Off-policy reinforcement learning is aimed at efficiently using data samples gathered from a policy that is different from the currently optimized policy. A common approach is to use importance sampling techniques for compensating for the bias of value function estimators caused by the difference between the data-sampling policy and the target policy. However, existing off-policy methods often do not take the variance of the value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a variant of cross-validation. We demonstrate the usefulness of the proposed approach through simulations.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Guest editorial: special issue on structured prediction

Parker, C., Altun, Y., Tadepalli, P.

Machine Learning, 77(2-3):161-164, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Structured prediction by joint kernel support estimation

Lampert, CH., Blaschko, MB.

Machine Learning, 77(2-3):249-269, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A note on ethical aspects of BCI

Haselager, P., Vlek, R., Hill, J., Nijboer, F.

Neural Networks, 22(9):1352-1357, November 2009 (article)

Abstract
This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Model Learning with Local Gaussian Process Regression

Nguyen-Tuong, D., Seeger, M., Peters, J.

Advanced Robotics, 23(15):2015-2034, November 2009 (article)

Abstract
Precise models of robot inverse dynamics allow the design of significantly more accurate, energy-efficient and compliant robot control. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse dynamics model from measured data poses an interesting alternative. Nonparametric regression methods, such as Gaussian process regression (GPR) or locally weighted projection regression (LWPR), are not as restrictive as parametric models and, thus, offer a more flexible framework for approximating unknown nonlinearities. In this paper, we propose a local approximation to the standard GPR, called local GPR (LGP), for real-time model online learning by combining the strengths of both regression methods, i.e., the high accuracy of GPR and the fast speed of LWPR. The approach is shown to have competitive learning performance for hig h-dimensional data while being sufficiently fast for real-time learning. The effectiveness of LGP is exhibited by a comparison with the state-of-the-art regression techniques, such as GPR, LWPR and ν-support vector regression. The applicability of the proposed LGP method is demonstrated by real-time online learning of the inverse dynamics model for robot model-based control on a Barrett WAM robot arm.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Inferring textual entailment with a probabilistically sound calculus

Harmeling, S.

Natural Language Engineering, 15(4):459-477, October 2009 (article)

Abstract
We introduce a system for textual entailment that is based on a probabilistic model of entailment. The model is defined using a calculus of transformations on dependency trees, which is characterized by the fact that derivations in that calculus preserve the truth only with a certain probability. The calculus is successfully evaluated on the datasets of the PASCAL Challenge on Recognizing Textual Entailment.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures

Rasmussen, CE., de la Cruz, BJ., Ghahramani, Z., Wild, DL.

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6(4):615-628, October 2009 (article)

Abstract
Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture models provide a non-parametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model based clustering methods have been to short time series data. In this paper we present a case study of the application of non-parametric Bayesian clustering methods to the clustering of high-dimensional non-time series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a Dirichlet process mixture model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract
This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

ei

PDF [BibTex]

PDF [BibTex]


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Thermodynamic efficiency of information and heat flow

Allahverdyan, A., Janzing, D., Mahler, G.

Journal of Statistical Mechanics: Theory and Experiment, 2009(09):P09011, September 2009 (article)

Abstract
A basic task of information processing is information transfer (flow). P0 Here we study a pair of Brownian particles each coupled to a thermal bath at temperatures T1 and T2 . The information flow in such a system is defined via the time-shifted mutual information. The information flow nullifies at equilibrium, and its efficiency is defined as the ratio of the flow to the total entropy production in the system. For a stationary state the information flows from higher to lower temperatures, and its efficiency is bounded from above by (max[T1 , T2 ])/(|T1 − T2 |). This upper bound is imposed by the second law and it quantifies the thermodynamic cost for information flow in the present class of systems. It can be reached in the adiabatic situation, where the particles have widely different characteristic times. The efficiency of heat flow—defined as the heat flow over the total amount of dissipated heat—is limited from above by the same factor. There is a complementarity between heat and information flow: the set-up which is most efficient for the former is the least efficient for the latter and vice versa. The above bound for the efficiency can be (transiently) overcome in certain non-stationary situations, but the efficiency is still limited from above. We study yet another measure of information processing (transfer entropy) proposed in the literature. Though this measure does not require any thermodynamic cost, the information flow and transfer entropy are shown to be intimately related for stationary states.

ei

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PDF DOI [BibTex]


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Does Cognitive Science Need Kernels?

Jäkel, F., Schölkopf, B., Wichmann, F.

Trends in Cognitive Sciences, 13(9):381-388, September 2009 (article)

Abstract
Kernel methods are among the most successful tools in machine learning and are used in challenging data analysis problems in many disciplines. Here we provide examples where kernel methods have proven to be powerful tools for analyzing behavioral data, especially for identifying features in categorization experiments. We also demonstrate that kernel methods relate to perceptrons and exemplar models of categorization. Hence, we argue that kernel methods have neural and psychological plausibility, and theoretical results concerning their behavior are therefore potentially relevant for human category learning. In particular, we believe kernel methods have the potential to provide explanations ranging from the implementational via the algorithmic to the computational level.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Inference algorithms and learning theory for Bayesian sparse factor analysis

Rattray, M., Stegle, O., Sharp, K., Winn, J.

Journal of Physics: Conference Series , IW-SMI 2009, 197(1: International Workshop on Statistical-Mechanical Informatics 2009):1-10, (Editors: Inoue, M. , S. Ishii, Y. Kabashima, M. Okada), Institute of Physics, Bristol, UK, International Workshop on Statistical-Mechanical Informatics (IW-SMI), September 2009 (article)

Abstract
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

ei

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PDF Web DOI [BibTex]


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A PAC-Bayesian Approach to Structure Learning

Seldin, Y.

Biologische Kybernetik, The Hebrew University of Jerusalem, Israel, September 2009 (phdthesis)

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PDF [BibTex]

PDF [BibTex]


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Finite-time output stabilization with second order sliding modes

Dinuzzo, F., Ferrara, A.

Automatica, 45(9):2169-2171, September 2009 (article)

Abstract
In this note, a class of discontinuous feedback laws that switch over branches of parabolas in the auxiliary state plane is analyzed. Conditions are provided under which controllers belonging to this class are second order sliding-mode algorithms: they ensure uniform global finite-time output stability for uncertain systems of relative degree two.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Robot Learning

Peters, J., Morimoto, J., Tedrake, R., Roy, N.

IEEE Robotics and Automation Magazine, 16(3):19-20, September 2009 (article)

Abstract
Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the Defense Advanced Research Projects Agency (DARPA) challenges, and the growing number of research programs funded by governments around the world.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]