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2019


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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)

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link (url) DOI [BibTex]

2019


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]


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High-Fidelity Multiphysics Finite Element Modeling of Finger-Surface Interactions with Tactile Feedback

Serhat, G., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (misc)

Abstract
In this study, we develop a high-fidelity finite element (FE) analysis framework that enables multiphysics simulation of the human finger in contact with a surface that is providing tactile feedback. We aim to elucidate a variety of physical interactions that can occur at finger-surface interfaces, including contact, friction, vibration, and electrovibration. We also develop novel FE-based methods that will allow prediction of nonconventional features such as real finger-surface contact area and finger stickiness. We envision using the developed computational tools for efficient design and optimization of haptic devices by replacing expensive and lengthy experimental procedures with high-fidelity simulation.

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[BibTex]

[BibTex]


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Fingertip Friction Enhances Perception of Normal Force Changes

Gueorguiev, D., Lambert, J., Thonnard, J., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (misc)

Abstract
Using a force-controlled robotic platform, we tested the human perception of positive and negative modulations in normal force during passive dynamic touch, which also induced a strong related change in the finger-surface lateral force. In a two-alternative forced-choice task, eleven participants had to detect brief variations in the normal force compared to a constant controlled pre-stimulation force of 1 N and report whether it had increased or decreased. The average 75% just noticeable difference (JND) was found to be around 0.25 N for detecting the peak change and 0.30 N for correctly reporting the increase or the decrease. Interestingly, the friction coefficient of a subject’s fingertip positively correlated with his or her performance at detecting the change and reporting its direction, which suggests that humans may use the lateral force as a sensory cue to perceive variations in the normal force.

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[BibTex]

[BibTex]


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Inflatable Haptic Sensor for the Torso of a Hugging Robot

Block, A. E., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (misc)

Abstract
During hugs, humans naturally provide and intuit subtle non-verbal cues that signify the strength and duration of an exchanged hug. Personal preferences for this close interaction may vary greatly between people; robots do not currently have the abilities to perceive or understand these preferences. This work-in-progress paper discusses designing, building, and testing a novel inflatable torso that can simultaneously soften a robot and act as a tactile sensor to enable more natural and responsive hugging. Using PVC vinyl, a microphone, and a barometric pressure sensor, we created a small test chamber to demonstrate a proof of concept for the full torso. While contacting the chamber in several ways common in hugs (pat, squeeze, scratch, and rub), we recorded data from the two sensors. The preliminary results suggest that the complementary haptic sensing channels allow us to detect coarse and fine contacts typically experienced during hugs, regardless of user hand placement.

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Project Page [BibTex]

Project Page [BibTex]


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Understanding the Pull-off Force of the Human Fingerpad

Nam, S., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (misc)

Abstract
To understand the adhesive force that occurs when a finger pulls off of a smooth surface, we built an apparatus to measure the fingerpad’s moisture, normal force, and real contact area over time during interactions with a glass plate. We recorded a total of 450 trials (45 interactions by each of ten human subjects), capturing a wide range of values across the aforementioned variables. The experimental results showed that the pull-off force increases with larger finger contact area and faster detachment rate. Additionally, moisture generally increases the contact area of the finger, but too much moisture can restrict the increase in the pull-off force.

hi

[BibTex]

[BibTex]


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The Haptician and the Alphamonsters

Forte, M. P., L’Orsa, R., Mohan, M., Nam, S., Kuchenbecker, K. J.

Student Innovation Challenge on Implementing Haptics in Virtual Reality Environment presented at the IEEE World Haptics Conference, Tokyo, Japan, July 2019, Maria Paola Forte, Rachael L'Orsa, Mayumi Mohan, and Saekwang Nam contributed equally to this publication (misc)

Abstract
Dysgraphia is a neurological disorder characterized by writing disabilities that affects between 7% and 15% of children. It presents itself in the form of unfinished letters, letter distortion, inconsistent letter size, letter collision, etc. Traditional therapeutic exercises require continuous assistance from teachers or occupational therapists. Autonomous partial or full haptic guidance can produce positive results, but children often become bored with the repetitive nature of such activities. Conversely, virtual rehabilitation with video games represents a new frontier for occupational therapy due to its highly motivational nature. Virtual reality (VR) adds an element of novelty and entertainment to therapy, thus motivating players to perform exercises more regularly. We propose leveraging the HTC VIVE Pro and the EXOS Wrist DK2 to create an immersive spellcasting “exergame” (exercise game) that helps motivate children with dysgraphia to improve writing fluency.

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Student Innovation Challenge – Virtual Reality [BibTex]

Student Innovation Challenge – Virtual Reality [BibTex]


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Implementation of a 6-DOF Parallel Continuum Manipulator for Delivering Fingertip Tactile Cues

Young, E. M., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 12(3):295-306, June 2019 (article)

Abstract
Existing fingertip haptic devices can deliver different subsets of tactile cues in a compact package, but we have not yet seen a wearable six-degree-of-freedom (6-DOF) display. This paper presents the Fuppeteer (short for Fingertip Puppeteer), a device that is capable of controlling the position and orientation of a flat platform, such that any combination of normal and shear force can be delivered at any location on any human fingertip. We build on our previous work of designing a parallel continuum manipulator for fingertip haptics by presenting a motorized version in which six flexible Nitinol wires are actuated via independent roller mechanisms and proportional-derivative controllers. We evaluate the settling time and end-effector vibrations observed during system responses to step inputs. After creating a six-dimensional lookup table and adjusting simulated inputs using measured Jacobians, we show that the device can make contact with all parts of the fingertip with a mean error of 1.42 mm. Finally, we present results from a human-subject study. A total of 24 users discerned 9 evenly distributed contact locations with an average accuracy of 80.5%. Translational and rotational shear cues were identified reasonably well near the center of the fingertip and more poorly around the edges.

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DOI [BibTex]


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Explorations of Shape-Changing Haptic Interfaces for Blind and Sighted Pedestrian Navigation

Spiers, A., Kuchenbecker, K. J.

pages: 6, Workshop paper (6 pages) presented at the CHI 2019 Workshop on Hacking Blind Navigation, May 2019 (misc) Accepted

Abstract
Since the 1960s, technologists have worked to develop systems that facilitate independent navigation by vision-impaired (VI) pedestrians. These devices vary in terms of conveyed information and feedback modality. Unfortunately, many such prototypes never progress beyond laboratory testing. Conversely, smartphone-based navigation systems for sighted pedestrians have grown in robustness and capabilities, to the point of now being ubiquitous. How can we leverage the success of sighted navigation technology, which is driven by a larger global market, as a way to progress VI navigation systems? We believe one possibility is to make common devices that benefit both VI and sighted individuals, by providing information in a way that does not distract either user from their tasks or environment. To this end we have developed physical interfaces that eschew visual, audio or vibratory feedback, instead relying on the natural human ability to perceive the shape of a handheld object.

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[BibTex]

[BibTex]


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Bimanual Wrist-Squeezing Haptic Feedback Changes Speed-Force Tradeoff in Robotic Surgery Training

Cao, E., Machaca, S., Bernard, T., Wolfinger, B., Patterson, Z., Chi, A., Adrales, G. L., Kuchenbecker, K. J., Brown, J. D.

Extended abstract presented as an ePoster at the Annual Meeting of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Baltimore, USA, April 2019 (misc) Accepted

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[BibTex]

[BibTex]


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Interactive Augmented Reality for Robot-Assisted Surgery

Forte, M. P., Kuchenbecker, K. J.

Extended abstract presented as an Emerging Technology ePoster at the Annual Meeting of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Baltimore, Maryland, USA, April 2019 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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A Design Tool for Therapeutic Social-Physical Human-Robot Interactions

Mohan, M., Kuchenbecker, K. J.

Workshop paper (3 pages) presented at the HRI Pioneers Workshop, Daegu, South Korea, March 2019 (misc) Accepted

Abstract
We live in an aging society; social-physical human-robot interaction has the potential to keep our elderly adults healthy by motivating them to exercise. After summarizing prior work, this paper proposes a tool that can be used to design exercise and therapy interactions to be performed by an upper-body humanoid robot. The interaction design tool comprises a teleoperation system that transmits the operator’s arm motions, head motions and facial expression along with an interface to monitor and assess the motion of the user interacting with the robot. We plan to use this platform to create dynamic and intuitive exercise interactions.

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Project Page [BibTex]

Project Page [BibTex]


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How Does It Feel to Clap Hands with a Robot?

Fitter, N. T., Kuchenbecker, K. J.

International Journal of Social Robotics, 2019 (article) Accepted

Abstract
Future robots may need lighthearted physical interaction capabilities to connect with people in meaningful ways. To begin exploring how users perceive playful human–robot hand-to-hand interaction, we conducted a study with 20 participants. Each user played simple hand-clapping games with the Rethink Robotics Baxter Research Robot during a 1-h-long session involving 24 randomly ordered conditions that varied in facial reactivity, physical reactivity, arm stiffness, and clapping tempo. Survey data and experiment recordings demonstrate that this interaction is viable: all users successfully completed the experiment and mentioned enjoying at least one game without prompting. Hand-clapping tempo was highly salient to users, and human-like robot errors were more widely accepted than mechanical errors. Furthermore, perceptions of Baxter varied in the following statistically significant ways: facial reactivity increased the robot’s perceived pleasantness and energeticness; physical reactivity decreased pleasantness, energeticness, and dominance; higher arm stiffness increased safety and decreased dominance; and faster tempo increased energeticness and increased dominance. These findings can motivate and guide roboticists who want to design social–physical human–robot interactions.

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[BibTex]

[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)

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DOI [BibTex]

DOI [BibTex]


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More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

2019 (misc) Submitted

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arXiv [BibTex]

arXiv [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)

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DOI Project Page Project Page [BibTex]

DOI Project Page Project Page [BibTex]


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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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Toward Expert-Sourcing of a Haptic Device Repository

Seifi, H., Ip, J., Agrawal, A., Kuchenbecker, K. J., MacLean, K. E.

Glasgow, UK, 2019 (misc)

Abstract
Haptipedia is an online taxonomy, database, and visualization that aims to accelerate ideation of new haptic devices and interactions in human-computer interaction, virtual reality, haptics, and robotics. The current version of Haptipedia (105 devices) was created through iterative design, data entry, and evaluation by our team of experts. Next, we aim to greatly increase the number of devices and keep Haptipedia updated by soliciting data entry and verification from haptics experts worldwide.

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link (url) [BibTex]

link (url) [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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Automated Generation of Reactive Programs from Human Demonstration for Orchestration of Robot Behaviors

Berenz, V., Bjelic, A., Mainprice, J.

ArXiv, 2019 (article)

Abstract
Social robots or collaborative robots that have to interact with people in a reactive way are difficult to program. This difficulty stems from the different skills required by the programmer: to provide an engaging user experience the behavior must include a sense of aesthetics while robustly operating in a continuously changing environment. The Playful framework allows composing such dynamic behaviors using a basic set of action and perception primitives. Within this framework, a behavior is encoded as a list of declarative statements corresponding to high-level sensory-motor couplings. To facilitate non-expert users to program such behaviors, we propose a Learning from Demonstration (LfD) technique that maps motion capture of humans directly to a Playful script. The approach proceeds by identifying the sensory-motor couplings that are active at each step using the Viterbi path in a Hidden Markov Model (HMM). Given these activation patterns, binary classifiers called evaluations are trained to associate activations to sensory data. Modularity is increased by clustering the sensory-motor couplings, leading to a hierarchical tree structure. The novelty of the proposed approach is that the learned behavior is encoded not in terms of trajectories in a task space, but as couplings between sensory information and high-level motor actions. This provides advantages in terms of behavioral generalization and reactivity displayed by the robot.

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Support Video link (url) [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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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

PDF DOI [BibTex]

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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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]


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Kernel Methods in Computer Vision

Lampert, CH.

Foundations and Trends in Computer Graphics and Vision, 4(3):193-285, September 2009 (article)

Abstract
Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Fast Kernel-Based Independent Component Analysis

Shen, H., Jegelka, S., Gretton, A.

IEEE Transactions on Signal Processing, 57(9):3498-3511, September 2009 (article)

Abstract
Recent approaches to independent component analysis (ICA) have used kernel independence measures to obtain highly accurate solutions, particularly where classical methods experience difficulty (for instance, sources with near-zero kurtosis). FastKICA (fast HSIC-based kernel ICA) is a new optimization method for one such kernel independence measure, the Hilbert-Schmidt Independence Criterion (HSIC). The high computational efficiency of this approach is achieved by combining geometric optimization techniques, specifically an approximate Newton-like method on the orthogonal group, with accurate estimates of the gradient and Hessian based on an incomplete Cholesky decomposition. In contrast to other efficient kernel-based ICA algorithms, FastKICA is applicable to any twice differentiable kernel function. Experimental results for problems with large numbers of sources and observations indicate that FastKICA provides more accurate solutions at a given cost than gradient descent on HSIC. Comparing with other recently published ICA methods, FastKICA is competitive in terms of accuracy, relatively insensitive to local minima when initialized far from independence, and more robust towards outliers. An analysis of the local convergence properties of FastKICA is provided.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Guest editorial: Special issue on robot learning, Part B

Peters, J., Ng, A.

Autonomous Robots, 27(2):91-92, August 2009 (article)

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Policy Search for Motor Primitives

Peters, J., Kober, J.

KI - Zeitschrift K{\"u}nstliche Intelligenz, 23(3):38-40, August 2009 (article)

Abstract
Many motor skills in humanoid robotics can be learned using parametrized motor primitives from demonstrations. However, most interesting motor learning problems require self-improvement often beyond the reach of current reinforcement learning methods due to the high dimensionality of the state-space. We develop an EM-inspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several well-known parametrized policy search methods and show that it outperforms them. We apply it to motor learning problems and show that it can learn the complex Ball-in-a-Cup task using a real Barrett WAM robot arm.

ei

Web [BibTex]

Web [BibTex]


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A neurophysiologically plausible population code model for human contrast discrimination

Goris, R., Wichmann, F., Henning, G.

Journal of Vision, 9(7):1-22, July 2009 (article)

Abstract
The pedestal effect is the improvement in the detectability of a sinusoidal grating in the presence of another grating of the same orientation, spatial frequency, and phase—usually called the pedestal. Recent evidence has demonstrated that the pedestal effect is differently modified by spectrally flat and notch-filtered noise: The pedestal effect is reduced in flat noise but virtually disappears in the presence of notched noise (G. B. Henning & F. A. Wichmann, 2007). Here we consider a network consisting of units whose contrast response functions resemble those of the cortical cells believed to underlie human pattern vision and demonstrate that, when the outputs of multiple units are combined by simple weighted summation—a heuristic decision rule that resembles optimal information combination and produces a contrast-dependent weighting profile—the network produces contrast-discrimination data consistent with psychophysical observations: The pedestal effect is present without noise, reduced in broadband noise, but almost disappears in notched noise. These findings follow naturally from the normalization model of simple cells in primary visual cortex, followed by response-based pooling, and suggest that in processing even low-contrast sinusoidal gratings, the visual system may combine information across neurons tuned to different spatial frequencies and orientations.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions

Corfield, D., Schölkopf, B., Vapnik, V.

Journal for General Philosophy of Science, 40(1):51-58, July 2009 (article)

Abstract
We compare Karl Popper’s ideas concerning the falsifiability of a theory with similar notions from the part of statistical learning theory known as VC-theory. Popper’s notion of the dimension of a theory is contrasted with the apparently very similar VC-dimension. Having located some divergences, we discuss how best to view Popper’s work from the perspective of statistical learning theory, either as a precursor or as aiming to capture a different learning activity.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Guest editorial: Special issue on robot learning, Part A

Peters, J., Ng, A.

Autonomous Robots, 27(1):1-2, July 2009 (article)

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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A Geometric Approach to Confidence Sets for Ratios: Fieller’s Theorem, Generalizations, and Bootstrap

von Luxburg, U., Franz, V.

Statistica Sinica, 19(3):1095-1117, July 2009 (article)

Abstract
We present a geometric method to determine confidence sets for the ratio E(Y)/E(X) of the means of random variables X and Y. This method reduces the problem of constructing confidence sets for the ratio of two random variables to the problem of constructing confidence sets for the means of one-dimensional random variables. It is valid in a large variety of circumstances. In the case of normally distributed random variables, the so constructed confidence sets coincide with the standard Fieller confidence sets. Generalizations of our construction lead to definitions of exact and conservative confidence sets for very general classes of distributions, provided the joint expectation of (X,Y) exists and the linear combinations of the form aX + bY are well-behaved. Finally, our geometric method allows to derive a very simple bootstrap approach for constructing conservative confidence sets for ratios which perform favorably in certain situations, in particular in the asymmetric heavy-tailed regime.

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Center-surround patterns emerge as optimal predictors for human saccade targets

Kienzle, W., Franz, M., Schölkopf, B., Wichmann, F.

Journal of Vision, 9(5:7):1-15, May 2009 (article)

Abstract
The human visual system is foveated, that is, outside the central visual field resolution and acuity drop rapidly. Nonetheless much of a visual scene is perceived after only a few saccadic eye movements, suggesting an effective strategy for selecting saccade targets. It has been known for some time that local image structure at saccade targets influences the selection process. However, the question of what the most relevant visual features are is still under debate. Here we show that center-surround patterns emerge as the optimal solution for predicting saccade targets from their local image structure. The resulting model, a one-layer feed-forward network, is surprisingly simple compared to previously suggested models which assume much more complex computations such as multi-scale processing and multiple feature channels. Nevertheless, our model is equally predictive. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

ei

PDF DOI [BibTex]


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Influence of Different Assignment Conditions on the Determination of Symmetric Homo-dimeric Structures with ARIA

Bardiaux, B., Bernard, A., Rieping, W., Habeck, M., Malliavin, TE., Nilges, M.

Proteins, 75(3):569-585, May 2009 (article)

Abstract
The ambiguous restraint for iterative assignment (ARIA) approach for NMR structure calculation is evaluated for symmetric homodimeric proteins by assessing the effect of several data analysis and assignment methods on the structure quality. In particular, we study the effects of network anchoring and spin-diffusion correction. The spin-diffusion correction improves the protein structure quality systematically, whereas network anchoring enhances the assignment efficiency by speeding up the convergence and coping with highly ambiguous data. For some homodimeric folds, network anchoring has been proved essential for unraveling both chain and proton assignment ambiguities.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Beamforming in Noninvasive Brain-Computer Interfaces

Grosse-Wentrup, M., Liefhold, C., Gramann, K., Buss, M.

IEEE Transactions on Biomedical Engineering, 56(4):1209-1219, April 2009 (article)

Abstract
Spatial filtering (SF) constitutes an integral part of building EEG-based brain–computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject‘s intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.

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PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Constructing Sparse Kernel Machines Using Attractors

Lee, D., Jung, K., Lee, J.

IEEE Transactions on Neural Networks, 20(4):721-729, April 2009 (article)

Abstract
In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters

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

Theoretical Computer Science, 410(19):1749-1764, April 2009 (article)

Abstract
We study clustering algorithms based on neighborhood graphs on a random sample of data points. The question we ask is how such a graph should be constructed in order to obtain optimal clustering results. Which type of neighborhood graph should one choose, mutual k-nearest-neighbor or symmetric k-nearest-neighbor? What is the optimal parameter k? In our setting, clusters are defined as connected components of the t-level set of the underlying probability distribution. Clusters are said to be identified in the neighborhood graph if connected components in the graph correspond to the true underlying clusters. Using techniques from random geometric graph theory, we prove bounds on the probability that clusters are identified successfully, both in a noise-free and in a noisy setting. Those bounds lead to several conclusions. First, k has to be chosen surprisingly high (rather of the order n than of the order logn) to maximize the probability of cluster identification. Secondly, the major difference between the mutual and the symmetric k-nearest-neighbor graph occurs when one attempts to detect the most significant cluster only.

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