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2020


Learning to Predict Perceptual Distributions of Haptic Adjectives
Learning to Predict Perceptual Distributions of Haptic Adjectives

Richardson, B. A., Kuchenbecker, K. J.

Frontiers in Neurorobotics, 13(116):1-16, Febuary 2020 (article)

Abstract
When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.

hi

DOI [BibTex]

2020


DOI [BibTex]


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Sliding Mode Control with Gaussian Process Regression for Underwater Robots

Lima, G. S., Trimpe, S., Bessa, W. M.

Journal of Intelligent & Robotic Systems, January 2020 (article)

ics

DOI [BibTex]

DOI [BibTex]


Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks

Beuchert, J., Solowjow, F., Raisch, J., Trimpe, S., Seel, T.

IEEE Control Systems Letters, 4(1):103-108, January 2020 (article)

ics

arXiv PDF DOI Project Page [BibTex]

arXiv PDF DOI Project Page [BibTex]


Learning Multi-Human Optical Flow
Learning Multi-Human Optical Flow

Ranjan, A., Hoffmann, D. T., Tzionas, D., Tang, S., Romero, J., Black, M. J.

International Journal of Computer Vision (IJCV), January 2020 (article)

Abstract
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the domain of human motion. Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset. We use a 3D model of the human body and motion capture data to synthesize realistic flow fields in both single-and multi-person images. We then train optical flow networks to estimate human flow fields from pairs of images. We demonstrate that our trained networks are more accurate than a wide range of top methods on held-out test data and that they can generalize well to real image sequences. The code, trained models and the dataset are available for research.

ps

Paper Publisher Version poster link (url) DOI [BibTex]


Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

Baumann, D., Mager, F., Zimmerling, M., Trimpe, S.

IEEE Control Systems Letters, 4(1):127-132, January 2020 (article)

ics

arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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Self-supervised motion deblurring

Liu, P., Janai, J., Pollefeys, M., Sattler, T., Geiger, A.

IEEE Robotics and Automation Letters, 2020 (article)

avg

[BibTex]

[BibTex]


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Effect of the soft layer thickness of magnetization reversal process of exchange-spring nanomagnet patterns

Son, K., Schütz, G., Goering, E.

{Current Applied Physics}, 20(4):477-483, Elsevier B.V., Amsterdam, 2020 (article)

mms

DOI [BibTex]


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Tuning the magnetic properties of permalloy-based magnetoplasmonic crystals for sensor applications

Murzin, D. V., Belyaev, V. K., Groß, F., Gräfe, J., Rivas, M., Rodionova, V. V.

{Japanese Journal of Applied Physics}, 59(SE), IOP Publishing Ltd, Bristol, England, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Element-resolved study of the evolution of magnetic response in FexN compounds

Chen, Y., Gölden, D., Dirba, I., Huang, M., Gutfleisch, O., Nagel, P., Merz, M., Schuppler, S., Schütz, G., Alff, L., Goering, E.

{Journal of Magnetism and Magnetic Materials}, 498, NH, Elsevier, Amsterdam, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms
Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms

Dong, X., Sitti, M.

The International Journal of Robotics Research, 2020 (article)

Abstract
Magnetically actuated mobile microrobots can access distant, enclosed, and small spaces, such as inside microfluidic channels and the human body, making them appealing for minimally invasive tasks. Despite their simplicity when scaling down, creating collective microrobots that can work closely and cooperatively, as well as reconfigure their formations for different tasks, would significantly enhance their capabilities such as manipulation of objects. However, a challenge of realizing such cooperative magnetic microrobots is to program and reconfigure their formations and collective motions with under-actuated control signals. This article presents a method of controlling 2D static and time-varying formations among collective self-repelling ferromagnetic microrobots (100 μm to 350 μm in diameter, up to 260 in number) by spatially and temporally programming an external magnetic potential energy distribution at the air–water interface or on solid surfaces. A general design method is introduced to program external magnetic potential energy using ferromagnets. A predictive model of the collective system is also presented to predict the formation and guide the design procedure. With the proposed method, versatile complex static formations are experimentally demonstrated and the programmability and scaling effects of formations are analyzed. We also demonstrate the collective mobility of these magnetic microrobots by controlling them to exhibit bio-inspired collective behaviors such as aggregation, directional motion with arbitrary swarm headings, and rotational swarming motion. Finally, the functions of the produced microrobotic swarm are demonstrated by controlling them to navigate through cluttered environments and complete reconfigurable cooperative manipulation tasks.

pi

DOI [BibTex]


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The role of temperature and drive current in skyrmion dynamics

Litzius, K., Leliaert, J., Bassirian, P., Rodrigues, D., Kromin, S., Lemesh, I., Zazvorka, J., Lee, K., Mulkers, J., Kerber, N., Heinze, D., Keil, N., Reeve, R. M., Weigand, M., Van Waeyenberge, B., Schütz, G., Everschor-Sitte, K., Beach, G. S. D., Kläui, M.

{Nature Electronics}, 3(1):30-36, Springer Nature, London, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic flux penetration into micron-sized superconductor/ferromagnet bilayers

Simmendinger, J., Weigand, M., Schütz, G., Albrecht, J.

{Superconductor Science and Technology}, 33(2), IOP Pub., Bristol, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Analytical classical density functionals from an equation learning network

Lin, S., Martius, G., Oettel, M.

The Journal of Chemical Physics, 152(2):021102, 2020, arXiv preprint \url{https://arxiv.org/abs/1910.12752} (article)

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Preprint_PDF DOI [BibTex]

Preprint_PDF DOI [BibTex]


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Fabrication and temperature-dependent magnetic properties of large-area L10-FePt/Co exchange-spring magnet nanopatterns

Son, K., Schütz, G.

{Physica E: Low-Dimensional Systems And Nanostructures}, 115, North-Holland, Amsterdam, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]


General Movement Assessment from videos of computed {3D} infant body models is equally effective compared to conventional {RGB} Video rating
General Movement Assessment from videos of computed 3D infant body models is equally effective compared to conventional RGB Video rating

Schroeder, S., Hesse, N., Weinberger, R., Tacke, U., Gerstl, L., Hilgendorff, A., Heinen, F., Arens, M., Bodensteiner, C., Dijkstra, L. J., Pujades, S., Black, M., Hadders-Algra, M.

Early Human Development, 2020 (article)

Abstract
Background: General Movement Assessment (GMA) is a powerful tool to predict Cerebral Palsy (CP). Yet, GMA requires substantial training hampering its implementation in clinical routine. This inspired a world-wide quest for automated GMA. Aim: To test whether a low-cost, marker-less system for three-dimensional motion capture from RGB depth sequences using a whole body infant model may serve as the basis for automated GMA. Study design: Clinical case study at an academic neurodevelopmental outpatient clinic. Subjects: Twenty-nine high-risk infants were recruited and assessed at their clinical follow-up at 2-4 month corrected age (CA). Their neurodevelopmental outcome was assessed regularly up to 12-31 months CA. Outcome measures: GMA according to Hadders-Algra by a masked GMA-expert of conventional and computed 3D body model (“SMIL motion”) videos of the same GMs. Agreement between both GMAs was assessed, and sensitivity and specificity of both methods to predict CP at ≥12 months CA. Results: The agreement of the two GMA ratings was substantial, with κ=0.66 for the classification of definitely abnormal (DA) GMs and an ICC of 0.887 (95% CI 0.762;0.947) for a more detailed GM-scoring. Five children were diagnosed with CP (four bilateral, one unilateral CP). The GMs of the child with unilateral CP were twice rated as mildly abnormal. DA-ratings of both videos predicted bilateral CP well: sensitivity 75% and 100%, specificity 88% and 92% for conventional and SMIL motion videos, respectively. Conclusions: Our computed infant 3D full body model is an attractive starting point for automated GMA in infants at risk of CP.

ps

[BibTex]

[BibTex]


Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage
Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

Haksar, R. N., Trimpe, S., Schwager, M.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

ics

[BibTex]

[BibTex]


Electronics, Software and Analysis of a Bioinspired Sensorized Quadrupedal Robot
Electronics, Software and Analysis of a Bioinspired Sensorized Quadrupedal Robot

Petereit, R.

Technische Universität München, 2020 (mastersthesis)

dlg

[BibTex]


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Visual-Inertial Mapping with Non-Linear Factor Recovery

Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D.

IEEE Robotics and Automation Letters (RA-L), 5, 2020, accepted for presentation at IEEE International Conference on Robotics and Automation (ICRA) 2020, to appear, arXiv:1904.06504 (article)

Abstract
Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.

ev

[BibTex]

[BibTex]


Trunk pitch oscillations for energy trade-offs in bipedal running birds and robots
Trunk pitch oscillations for energy trade-offs in bipedal running birds and robots

Oezge Drama, , Badri-Spröwitz, A.

Bioinspiration & Biomimetics, 2020 (article)

Abstract
Bipedal animals have diverse morphologies and advanced locomotion abilities. Terrestrial birds, in particular, display agile, efficient, and robust running motion, in which they exploit the interplay between the body segment masses and moment of inertias. On the other hand, most legged robots are not able to generate such versatile and energy-efficient motion and often disregard trunk movements as a means to enhance their locomotion capabilities. Recent research investigated how trunk motions affect the gait characteristics of humans, but there is a lack of analysis across different bipedal morphologies. To address this issue, we analyze avian running based on a spring-loaded inverted pendulum model with a pronograde (horizontal) trunk. We use a virtual point based control scheme and modify the alignment of the ground reaction forces to assess how our control strategy influences the trunk pitch oscillations and energetics of the locomotion. We derive three potential key strategies to leverage trunk pitch motions that minimize either the energy fluctuations of the center of mass or the work performed by the hip and leg. We suggest how these strategies could be used in legged robotics.

dlg

link (url) DOI [BibTex]


Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control
Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

am ics

arXiv PDF [BibTex]

arXiv PDF [BibTex]


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Thermal nucleation and high-resolution imaging of submicrometer magnetic bubbles in thin thulium iron garnet films with perpendicular anisotropy

Büttner, F., Mawass, M. A., Bauer, J., Rosenberg, E., Caretta, L., Avci, C. O., Gräfe, J., Finizio, S., Vaz, C. A. F., Novakovic, N., Weigand, M., Litzius, K., Förster, J., Träger, N., Groß, F., Suzuki, D., Huang, M., Bartell, J., Kronast, F., Raabe, J., Schütz, G., Ross, C. A., Beach, G. S. D.

{Physical Review Materials}, 4(1), American Physical Society, College Park, MD, 2020 (article)

mms

DOI [BibTex]

DOI [BibTex]

2012


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Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices

Cherian, A., Sra, S., Banerjee, A., Papanikolopoulos, N.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9):2161-2174, December 2012 (article)

ei

DOI [BibTex]

2012


DOI [BibTex]


Virtual Human Bodies with Clothing and Hair: From Images to Animation
Virtual Human Bodies with Clothing and Hair: From Images to Animation

Guan, P.

Brown University, Department of Computer Science, December 2012 (phdthesis)

ps

pdf [BibTex]

pdf [BibTex]


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Hippocampal-Cortical Interaction during Periods of Subcortical Silence

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Nature, 491, pages: 547-553, November 2012 (article)

Abstract
Hippocampal ripples, episodic high-frequency field-potential oscillations primarily occurring during sleep and calmness, have been described in mice, rats, rabbits, monkeys and humans, and so far they have been associated with retention of previously acquired awake experience. Although hippocampal ripples have been studied in detail using neurophysiological methods, the global effects of ripples on the entire brain remain elusive, primarily owing to a lack of methodologies permitting concurrent hippocampal recordings and whole-brain activity mapping. By combining electrophysiological recordings in hippocampus with ripple-triggered functional magnetic resonance imaging, here we show that most of the cerebral cortex is selectively activated during the ripples, whereas most diencephalic, midbrain and brainstem regions are strongly and consistently inhibited. Analysis of regional temporal response patterns indicates that thalamic activity suppression precedes the hippocampal population burst, which itself is temporally bounded by massive activations of association and primary cortical areas. These findings suggest that during off-line memory consolidation, synergistic thalamocortical activity may be orchestrating a privileged interaction state between hippocampus and cortex by silencing the output of subcortical centres involved in sensory processing or potentially mediating procedural learning. Such a mechanism would cause minimal interference, enabling consolidation of hippocampus-dependent memory.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Scalable graph kernels

Shervashidze, N.

Eberhard Karls Universität Tübingen, Germany, October 2012 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Thermodynamic limits of dynamic cooling

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

Physical Review E, 84(4):16, October 2012 (article)

Abstract
We study dynamic cooling, where an externally driven two-level system is cooled via reservoir, a quantum system with initial canonical equilibrium state. We obtain explicitly the minimal possible temperature Tmin>0 reachable for the two-level system. The minimization goes over all unitary dynamic processes operating on the system and reservoir and over the reservoir energy spectrum. The minimal work needed to reach Tmin grows as 1/Tmin. This work cost can be significantly reduced, though, if one is satisfied by temperatures slightly above Tmin. Our results on Tmin>0 prove unattainability of the absolute zero temperature without ambiguities that surround its derivation from the entropic version of the third law. We also study cooling via a reservoir consisting of N≫1 identical spins. Here we show that Tmin∝1/N and find the maximal cooling compatible with the minimal work determined by the free energy. Finally we discuss cooling by reservoir with an initially microcanonic state and show that although a purely microcanonic state can yield the zero temperature, the unattainability is recovered when taking into account imperfections in preparing the microcanonic state.

ei

Web DOI [BibTex]

Web DOI [BibTex]


Coupled Action Recognition and Pose Estimation from Multiple Views
Coupled Action Recognition and Pose Estimation from Multiple Views

Yao, A., Gall, J., van Gool, L.

International Journal of Computer Vision, 100(1):16-37, October 2012 (article)

ps

publisher's site code pdf Project Page Project Page Project Page [BibTex]

publisher's site code pdf Project Page Project Page Project Page [BibTex]


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GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Kam-Thong, T., Azencott, C., Cayton, L., Pütz, B., Altmann, A., Karbalai, N., Sämann, P., Schölkopf, B., Müller-Myhsok, B., Borgwardt, K.

Human Heredity, 73(4):220-236, September 2012 (article)

Abstract
Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year’s time to complete the same task.

ei

Web [BibTex]

Web [BibTex]


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Fast projection onto mixed-norm balls with applications

Sra, S.

Minining and Knowledge Discovery (DMKD), 25(2):358-377, September 2012 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Bayesian estimation of free energies from equilibrium simulations

Habeck, M.

Physical Review Letters, 109(10):5, September 2012 (article)

Abstract
Free energy calculations are an important tool in statistical physics and biomolecular simulation. This Letter outlines a Bayesian method to estimate free energies from equilibrium Monte Carlo simulations. A Gibbs sampler is developed that allows efficient sampling of free energies and the density of states. The Gibbs sampling output can be used to estimate expected free energy differences and their uncertainties. The probabilistic formulation offers a unifying framework for existing methods such as the weighted histogram analysis method and the multistate Bennett acceptance ratio; both are shown to be approximate versions of the full probabilistic treatment.

ei

Web DOI [BibTex]

Web DOI [BibTex]


{DRAPE: DRessing Any PErson}
DRAPE: DRessing Any PErson

Guan, P., Reiss, L., Hirshberg, D., Weiss, A., Black, M. J.

ACM Trans. on Graphics (Proc. SIGGRAPH), 31(4):35:1-35:10, July 2012 (article)

Abstract
We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.

ps

YouTube pdf talk Project Page Project Page [BibTex]

YouTube pdf talk Project Page Project Page [BibTex]


From Pixels to Layers: Joint Motion Estimation and Segmentation
From Pixels to Layers: Joint Motion Estimation and Segmentation

Sun, D.

Brown University, Department of Computer Science, July 2012 (phdthesis)

ps

pdf [BibTex]

pdf [BibTex]


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PAC-Bayesian Inequalities for Martingales

Seldin, Y., Laviolette, F., Cesa-Bianchi, N., Shawe-Taylor, J., Auer, P.

IEEE Transactions on Information Theory, 58(12):7086-7093, June 2012 (article)

Abstract
We present a set of high-probability inequalities that control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. We also present a comparison inequality that bounds expectation of a convex function of martingale difference type variables by expectation of the same function of independent Bernoulli variables. This inequality is applied to derive a tighter analog of Hoeffding-Azuma inequality.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


Entropy Search for Information-Efficient Global Optimization
Entropy Search for Information-Efficient Global Optimization

Hennig, P., Schuler, C.

Journal of Machine Learning Research, 13, pages: 1809-1837, -, June 2012 (article)

Abstract
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.

ei pn

PDF Web Project Page [BibTex]

PDF Web Project Page [BibTex]


Visual Orientation and Directional Selectivity Through Thalamic Synchrony
Visual Orientation and Directional Selectivity Through Thalamic Synchrony

Stanley, G., Jin, J., Wang, Y., Desbordes, G., Wang, Q., Black, M., Alonso, J.

Journal of Neuroscience, 32(26):9073-9088, June 2012 (article)

Abstract
Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10–20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

ps

preprint publisher's site Project Page [BibTex]

preprint publisher's site Project Page [BibTex]


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A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

Nere, A., Olcese, U., Balduzzi, D., Tononi, G.

PLoS ONE, 7(5):17, May 2012 (article)

Abstract
In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips.

ei

PDF Web DOI [BibTex]


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Online Kernel-based Learning for Task-Space Tracking Robot Control

Nguyen-Tuong, D., Peters, J.

IEEE Transactions on Neural Networks and Learning Systems, 23(9):1417-1425, May 2012 (article)

Abstract
Abstract—Task-space control of redundant robot systems based on analytical models is known to be susceptive to modeling errors. Here, data driven model learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an illposed problem. In particular, the same input data point can yield many different output values, which can form a non-convex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally illposed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local, kernel-based learning approach for online model learning for task-space tracking control. We propose a parametrization for the local model which makes an application in task-space tracking control of redundant robots possible. The model parametrization further allows us to apply the kerneltrick and, therefore, enables a formulation within the kernel learning framework. For evaluations, we show the ability of the method for online model learning for task-space tracking control of redundant robots.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Information-geometric approach to inferring causal directions

Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., Steudel, B., Schölkopf, B.

Artificial Intelligence, 182-183, pages: 1-31, May 2012 (article)

Abstract
While conventional approaches to causal inference are mainly based on conditional (in)dependences, recent methods also account for the shape of (conditional) distributions. The idea is that the causal hypothesis “X causes Y” imposes that the marginal distribution PX and the conditional distribution PY|X represent independent mechanisms of nature. Recently it has been postulated that the shortest description of the joint distribution PX,Y should therefore be given by separate descriptions of PX and PY|X. Since description length in the sense of Kolmogorov complexity is uncomputable, practical implementations rely on other notions of independence. Here we define independence via orthogonality in information space. This way, we can explicitly describe the kind of dependence that occurs between PY and PX|Y making the causal hypothesis “Y causes X” implausible. Remarkably, this asymmetry between cause and effect becomes particularly simple if X and Y are deterministically related. We present an inference method that works in this case. We also discuss some theoretical results for the non-deterministic case although it is not clear how to employ them for a more general inference method.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data

Schönfelder, V., Wichmann, F.

Journal of the Acoustical Society of America, 131(5):3953-3969, May 2012 (article)

Abstract
As a prerequisite to quantitative psychophysical models of sensory processing it is necessary to learn to what extent decisions in behavioral tasks depend on specific stimulus features, the perceptual cues. Based on relative linear combination weights, this study demonstrates how stimulus-response data can be analyzed in this regard relying on an L1-regularized multiple logistic regression, a modern statistical procedure developed in machine learning. This method prevents complex models from over-fitting to noisy data. In addition, it enforces “sparse” solutions, a computational approximation to the postulate that a good model should contain the minimal set of predictors necessary to explain the data. In simulations, behavioral data from a classical auditory tone-in-noise detection task were generated. The proposed method is shown to precisely identify observer cues from a large set of covarying, interdependent stimulus features—a setting where standard correlational and regression methods fail. The proposed method succeeds for a wide range of signal-to-noise ratios and for deterministic as well as probabilistic observers. Furthermore, the detailed decision rules of the simulated observers were reconstructed from the estimated linear model weights allowing predictions of responses on the basis of individual stimuli.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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glm-ie: The Generalised Linear Models Inference and Estimation Toolbox

Nickisch, H.

Journal of Machine Learning Research, 13, pages: 1699-1703, May 2012 (article)

Abstract
The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some The code is fully compatible to both Matlab 7.x and GNU Octave 3.3.x. Abstract Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Feature Selection via Dependence Maximization

Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K.

Journal of Machine Learning Research, 13, pages: 1393-1434, May 2012 (article)

Abstract
We introduce a framework of feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.

ei

PDF [BibTex]

PDF [BibTex]


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High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces

Grosse-Wentrup, M., Schölkopf, B.

Journal of Neural Engineering, 9(4):046001, May 2012 (article)

Abstract
Subjects operating a brain–computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.

ei

Web DOI [BibTex]


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ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections

Karaletsos, T., Stegle, O., Dreyer, C., Winn, J., Borgwardt, K.

Bioinformatics, 28(7):1001-1008, April 2012 (article)

Abstract
Motivation: Accurate large-scale phenotyping has recently gained considerable importance in biology. For example, in genome wide association studies technological advances have rendered genotyping cheap, leaving phenotype acquisition as the major bottleneck. Automatic image analysis is one major strategy to phenotype individuals in large numbers. Current approaches for visual phenotyping focus predominantly on summarizing statistics and geometric measures, such as height and width of an individual, or color histograms and patterns. However, more subtle, but biologically informative phenotypes, such as the local deformation of the shape of an individual with respect to the population mean cannot be automatically extracted and quantified by current techniques. Results: We propose a probabilistic machine learning model that allows for the extraction of deformation phenotypes from biological images, making them available as quantitative traits for downstream analysis. Our approach jointly models a collection of images using a learned common template that is mapped onto each image through a deformable smooth transformation. In a case study we analyze the shape deformations of 388 guppy fish (Poecilia reticulata). We find that the flexible shape phenotypes our model extracts are complementary to basic geometric measures. Moreover, these quantitative traits assort the observations into distinct groups and can be mapped to polymorphic genetic loci of the sample set.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses

Putzeys, T., Bethge, M., Wichmann, F., Wagemans, J., Goris, R.

PLoS Computational Biology, 8(4):1-13, April 2012 (article)

Abstract
Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Patterns of cis regulatory variation in diverse human populations

Stranger, BE., Montgomery, SB., Dimas, AS., Parts, L., Stegle, O., Ingle, CE., Sekowska, M., Smith, GD., Evans, D., Gutierrez-Arcelus, M., others

PLoS genetics, 8(4):e1002639, April 2012 (article)

Abstract
he genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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A Kernel Two-Sample Test

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.

Journal of Machine Learning Research, 13, pages: 723-773, March 2012 (article)

Abstract
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD). We present two distribution-free tests based on large deviation bounds for the MMD, and a third test based on the asymptotic distribution of this statistic. The MMD can be computed in quadratic time, although efficient linear time approximations are available. Our statistic is an instance of an integral probability metric, and various classical metrics on distributions are obtained when alternative function classes are used in place of an RKHS. We apply our two-sample tests to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where they perform strongly. Excellent performance is also obtained when comparing distributions over graphs, for which these are the first such tests.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Technical performance evaluation of a human brain PET/MRI system

Kolb, A., Wehrl, H., Hofmann, M., Judenhofer, M., Eriksson, L., Ladebeck, R., Lichy, M., Byars, L., Michel, C., Schlemmer, H., Schmand, M., Claussen, C., Sossi, V., Pichler, B.

European Radiology, 22(8):1776-1788, March 2012 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]