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2014


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Magnetic reflectometry of heterostructures (Topical Review)

Macke, S., Goering, E.

{Journal of Physics: Condensed Matter}, 26(36), IOP Publishing, Bristol, 2014 (article)

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DOI [BibTex]

2014


DOI [BibTex]


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Optimizing the fabrication of diffractive optical elements using a focused ion beam system

Vijayakumar, A., Eigenthaler, U., Keskinbora, K., Sridharan, G. M., Pramitha, V., Hirscher, M., Spatz, J. P., Bhattacharya, S.

{Proceedings of SPIE}, 9130, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Direct observation of internal vortex domain-wall dynamics

Stein, F.-U., Bocklage, L., Weigand, M., Meier, G.

{Physical Review B}, 89(2), American Physical Society, Woodbury, NY, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Synchronous precessional motion of multiple domain walls in a ferromagnetic nanowire by perpendicular field pulses

Kim, J., Mawass, M., Bisig, A., Krüger, B., Reeve, R. M., Schulz, T., Büttner, F., Yoon, J., You, C., Weigand, M., Stoll, H., Schütz, G., Swagten, H. J. M., Koopmans, B., Eisebitt, S., Kläui, M.

{Nature Communications}, 5, Nature Publishing Group, London, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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An autonomous manipulation system based on force control and optimization

Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., Sukhatme, G. S., Schaal, S.

Autonomous Robots, 36(1-2):11-30, January 2014 (article)

Abstract
In this paper we present an architecture for autonomous manipulation. Our approach is based on the belief that contact interactions during manipulation should be exploited to improve dexterity and that optimizing motion plans is useful to create more robust and repeatable manipulation behaviors. We therefore propose an architecture where state of the art force/torque control and optimization-based motion planning are the core components of the system. We give a detailed description of the modules that constitute the complete system and discuss the challenges inherent to creating such a system. We present experimental results for several grasping and manipulation tasks to demonstrate the performance and robustness of our approach.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Occam’s Razor in sensorimotor learning

Genewein, T, Braun, D

Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014 (article)

Abstract
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.

ei

DOI [BibTex]

DOI [BibTex]


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Generalized Thompson sampling for sequential decision-making and causal inference

Ortega, PA, Braun, DA

Complex Adaptive Systems Modeling, 2(2):1-23, March 2014 (article)

Abstract
Purpose Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling. Methods Although mostly applied to bandit problems, Thompson sampling can also be used to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution over actions can then be constructed by a Bayesian superposition of the policies weighted by their posterior probability of being optimal. Results Here we discuss two important features of this approach. First, we show in how far such generalized Thompson sampling can be regarded as an optimal strategy under limited information processing capabilities that constrain the sampling complexity of the decision-making process. Second, we show how such Thompson sampling can be extended to solve causal inference problems when interacting with an environment in a sequential fashion. Conclusion In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.

ei

DOI [BibTex]

DOI [BibTex]


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Liftoff of a Motor-Driven, Flapping-Wing Microaerial Vehicle Capable of Resonance

Hines, L., Campolo, D., Sitti, M.

IEEE Trans. on Robotics, 30(1):220-232, IEEE, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Untethered micro-robotic coding of three-dimensional material composition

Tasoglu, S, Diller, E, Guven, S, Sitti, M, Demirci, U

Nature Communications, 5, pages: DOI-10, Nature Publishing Group, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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The optimal shape of elastomer mushroom-like fibers for high and robust adhesion

Aksak, B., Sahin, K., Sitti, M.

Beilstein journal of nanotechnology, 5(1):630-638, Beilstein-Institut, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Mechanically Switchable Elastomeric Microfibrillar Adhesive Surfaces for Transfer Printing

Sariola, V., Sitti, M.

Advanced Materials Interfaces, 1(4):1300159, 2014 (article)

pi

[BibTex]

[BibTex]


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MultiMo-Bat: A biologically inspired integrated jumping–gliding robot

Woodward, M. A., Sitti, M.

The International Journal of Robotics Research, 33(12):1511-1529, SAGE Publications Sage UK: London, England, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Learning of grasp selection based on shape-templates

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.

Autonomous Robots, 36(1-2):51-65, January 2014 (article)

Abstract
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

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link (url) DOI [BibTex]


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Magnetic field distribution and characteristic fields of the vortex lattice for a clean superconducting niobium sample in an external field applied along a three-fold axis

Yaouanc, A., Maisuradze, A., Nakai, N., Machida, K., Khasanov, R., Amato, A., Biswas, P. K., Baines, C., Herlach, D., Henes, Rolf, Keppler, P., Keller, H.

{Physical Review B}, 89(18), American Physical Society, Woodbury, NY, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Experimental assessment of Physical upper limit for hydrogen storage capacity at 20 K in densified MIL-101 monoliths

Oh, H., Lupu, D., Blanita, G., Hirscher, M.

{RSC Advances}, 4(6):2648-2651, Royal Society of Chemistry, Cambridge, UK, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Strengthening zones in the Co matrix of WC-Co cemented carbides

Konyashin, I., Lachmann, F., Ries, B., Mazilkin, A. A., Straumal, B. B., Kübel, C., Llanes, L., Baretzky, B.

{Scripta Materialia}, 83, pages: 17-20, Pergamon, Tarrytown, NY, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Multilayer Fresnel zone plates for high energy radiation resolve 21 nm features at 1.2 keV

Keskinbora, K., Robisch, A., Mayer, M., Sanli, U., Grévent, C., Wolter, C., Weigand, M., Szeghalmi, A., Knez, M., Salditt, T., Schütz, G.

{Optics Express}, 22(15):18440-18453, Optical Society of America, Washington, DC, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Interplay of linker functionalization and hydrogen adsorption in the metal-organic framework MIL-101

Szilágyi, P. A., Weinrauch, I., Oh, H., Hirscher, M., Juan-Alcaniz, J., Serra-Crespo, P., de Respinis, M., Trzesniewski, B. J., Kapteijn, F., Geerlings, H., Gascon, J., Dam, B., Grzech, A., van de Krol, R.

{The Journal of Physical Chemistry C}, 118(34):19572-19579, American Chemical Society, Washington DC, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Application of magneto-optical Kerr effect to first-order reversal curve measurements

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

{Review of Scientific Instruments}, 85, American Institute of Physics, Woodbury, N.Y. [etc.], 2014 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Efficient focusing of 8 keV X-rays with multilayer Fresnel zone plates fabricated by atomic layer deposition and focused ion beam milling. Erratum

Mayer, M., Keskinbora, K., Grévent, C., Szeghalmi, A., Knez, M., Weigand, M., Snigirev, A., Snigireva, I., Schütz, G.

{Journal of Synchrotron Radiation}, 640, pages: 640-640, Published for the International Union of Crystallography by Munksgaard, Copenhagen, Denmark, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Low-amplitude magnetic vortex core reversal by non-linear interaction between azimuthal spin waves and the vortex gyromode

Sproll, M., Noske, M., Bauer, H., Kammerer, M., Gangwar, A., Dieterle, G., Weigand, M., Stoll, H., Woltersdorf, G., Back, C. H., Schütz, G.

{Applied Physics Letters}, 104(1), American Institute of Physics, Melville, NY, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them

Sun, D., Roth, S., Black, M. J.

International Journal of Computer Vision (IJCV), 106(2):115-137, 2014 (article)

Abstract
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical'' flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

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pdf full text code [BibTex]

pdf full text code [BibTex]


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Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences

Peng, Z, Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)

Abstract
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects’ self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.

ei

DOI [BibTex]

DOI [BibTex]


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Rotating Magnetic Miniature Swimming Robots With Multiple Flexible Flagella

Ye, Z., Régnier, S., Sitti, M.

IEEE Trans. on Robotics, 30(1):3-13, 2014 (article)

pi

[BibTex]

[BibTex]


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Three-Dimensional Programmable Assembly by Untethered Magnetic Robotic Micro-Grippers

Diller, E., Sitti, M.

Advanced Functional Materials, 24, pages: 4397-4404, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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Mechanics of Load–Drag–Unload Contact Cleaning of Gecko-Inspired Fibrillar Adhesives

Abusomwan, U. A., Sitti, M.

Langmuir, 30(40):11913-11918, American Chemical Society, 2014 (article)

pi

Project Page [BibTex]

Project Page [BibTex]


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The local magnetic properties of [MnIII6 CrIII]3+ and [FeIII6 CrIII]3+ single-molecule magnets deposited on surfaces studied by spin-polarized photoemission and XMCD with circularly polarized synchrotron radiation

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

{Journal of Physics: Conference Series}, 488(13), IOP Publishing, Bristol, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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A fluorene based covalent triazine framework with high CO2 and H2 capture and storage capacities

Hug, S., Mesch, M. B., Oh, H., Popp, N., Hirscher, M., Senker, J., Lotsch, B. V.

{Journal of Materials Chemistry A}, 2(16):5928-5936, Royal Society of Chemistry, Cambridge, UK, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Ab-initio calculations and atomistic calculations on the magnetoelectric effects in metallic nanostructures

Fähnle, M., Subkow, S.

{Physica Status Solidi C}, 11(2):185-191, Wiley-VCH, Weinheim, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Role of electron-magnon scatterings in ultrafast demagnetization

Haag, M., Illg, C., Fähnle, M.

{Physical Review B}, 90(1), American Physical Society, Woodbury, NY, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Element specific monolayer depth profiling

Macke, S., Radi, A., Hamann-Borrero, J. E., Verna, A., Bluschke, M., Brück, S., Goering, E., Sutarto, R., He, F., Cristiani, G., Wu, M., Benckiser, E., Habermeier, H., Logvenov, G., Gauquelin, N., Botton, G. A., Kajdos, A. P., Stemmer, S., Sawatzky, G. A., Haverkort, M. W., Keimer, B., Hinkov, V.

{Advanced Materials}, 26(38):6554-6559, Wiley VCH, Weinheim, 2014 (article)

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DOI [BibTex]

DOI [BibTex]


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Local modification of the magnetic vortex-core velocity by gallium implantation

Langner, H. H., Vogel, A., Beyersdorff, B., Weigand, M., Frömter, R., Oepen, H. P., Meier, G.

{Journal of Applied Physcis}, (10), American Institute of Physics, New York, NY, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Influence of magnetic fields on spin-mixing in transition metals

Haag, M., Illg, C., Fähnle, M.

{Physical Review B}, 90(13), American Physical Society, Woodbury, NY, 2014 (article)

mms

DOI [BibTex]

DOI [BibTex]

2009


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Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract
Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

ei

PDF Web Web [BibTex]

2009


PDF Web Web [BibTex]


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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]

PDF Web DOI [BibTex]


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PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract
Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

ei

PDF Web Web [BibTex]

PDF Web Web [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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Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data

Shelton, JA.

Women in Machine Learning Workshop (WiML), December 2009 (talk)

Abstract
Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This manifold learning approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned and such data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, Laplacian regularization improved performance whereas the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights learned by the regression in order to infer brain regions that are important during different types of visual processing.

ei

PDF Web [BibTex]

PDF Web [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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Event-Related Potentials in Brain-Computer Interfacing

Hill, NJ.

Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

Abstract
An introduction to event-related potentials with specific reference to their use in brain-computer interfacing applications and research.

ei

PDF [BibTex]

PDF [BibTex]


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BCI2000 and Python

Hill, NJ.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract
A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 software package.

ei

PDF [BibTex]

PDF [BibTex]


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Implementing a Signal Processing Filter in BCI2000 Using C++

Hill, NJ., Mellinger, J.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract
This tutorial shows how the functionality of the BCI2000 software package can be extended with one‘s own code, using BCI2000‘s C++ API.

ei

PDF [BibTex]

PDF [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.

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PDF Web DOI [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.

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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.

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PDF Web DOI [BibTex]