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2019


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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]

2019


[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

hi

[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) Accepted

hi

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.

hi

Project Page [BibTex]

Project Page [BibTex]


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Perceiving Systems (2016-2018)
Scientific Advisory Board Report, 2019 (misc)

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pdf [BibTex]

pdf [BibTex]


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A special issue on hydrogen-based Energy storage
{International Journal of Hydrogen Energy}, 44, pages: 7737, Elsevier, Amsterdam, 2019 (misc)

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DOI [BibTex]

DOI [BibTex]


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Nanoscale X-ray imaging of spin dynamics in Yttrium iron garnet

Förster, J., Wintz, S., Bailey, J., Finizio, S., Josten, E., Meertens, D., Dubs, C., Bozhko, D. A., Stoll, H., Dieterle, G., Traeger, N., Raabe, J., Slavin, A. N., Weigand, M., Gräfe, J., Schütz, G.

2019 (misc)

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link (url) [BibTex]

link (url) [BibTex]


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Visual-Inertial Mapping with Non-Linear Factor Recovery

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

2019, arXiv:1904.06504 (misc)

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[BibTex]

[BibTex]

2015


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

ei

[BibTex]

2015


[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

ei

[BibTex]

[BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]


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Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

mms

link (url) [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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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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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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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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Learning Motor Primitives for Robotics

Kober, J., Peters, J., Oztop, E.

Advanced Telecommunications Research Center ATR, June 2009 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei

[BibTex]

[BibTex]


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Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

ei

Web [BibTex]

Web [BibTex]