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2016


Thumb xl smpl
Skinned multi-person linear model

Black, M.J., Loper, M., Mahmood, N., Pons-Moll, G., Romero, J.

December 2016, Application PCT/EP2016/064610 (misc)

Abstract
The invention comprises a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity- dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. The invention quantitatively evaluates variants of SMPL using linear or dual- quaternion blend skinning and show that both are more accurate than a Blend SCAPE model trained on the same data. In a further embodiment, the invention realistically models dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

ps

Google Patents [BibTex]

2016


Google Patents [BibTex]


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Numerical Investigation of Frictional Forces Between a Finger and a Textured Surface During Active Touch

Khojasteh, B., Janko, M., Visell, Y.

Extended abstract presented in form of an oral presentation at the 3rd International Conference on BioTribology (ICoBT), London, England, September 2016 (misc)

Abstract
The biomechanics of the human finger pad has been investigated in relation to motor behaviour and sensory function in the upper limb. While the frictional properties of the finger pad are important for grip and grasp function, recent attention has also been given to the roles played by friction when perceiving a surface via sliding contact. Indeed, the mechanics of sliding contact greatly affect stimuli felt by the finger scanning a surface. Past research has shed light on neural mechanisms of haptic texture perception, but the relation with time-resolved frictional contact interactions is unknown. Current biotribological models cannot predict time-resolved frictional forces felt by a finger as it slides on a rough surface. This constitutes a missing link in understanding the mechanical basis of texture perception. To ameliorate this, we developed a two-dimensional finite element numerical simulation of a human finger pad in sliding contact with a textured surface. Our model captures bulk mechanical properties, including hyperelasticity, dissipation, and tissue heterogeneity, and contact dynamics. To validate it, we utilized a database of measurements that we previously captured with a variety of human fingers and surfaces. By designing the simulations to match the measurements, we evaluated the ability of the FEM model to predict time-resolved sliding frictional forces. We varied surface texture wavelength, sliding speed, and normal forces in the experiments. An analysis of the results indicated that both time- and frequency-domain features of forces produced during finger-surface sliding interactions were reproduced, including many of the phenomena that we observed in analyses of real measurements, including quasiperiodicity, harmonic distortion and spectral decay in the frequency domain, and their dependence on kinetics and surface properties. The results shed light on frictional signatures of surface texture during active touch, and may inform understanding of the role played by friction in texture discrimination.

hi

[BibTex]

[BibTex]


Thumb xl romo and mini
Behavioral Analysis Automation for Music-Based Robotic Therapy for Children with Autism Spectrum Disorder

Burns, R., Nizambad, S., Park, C. H., Jeon, M., Howard, A.

Workshop paper (5 pages) at the RO-MAN Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics, August 2016 (misc)

Abstract
In this full workshop paper, we discuss the positive impacts of robot, music, and imitation therapies on children with autism. We also discuss the use of Laban Motion Analysis (LMA) to identify emotion through movement and posture cues. We present our preliminary studies of the "Five Senses" game that our two robots, Romo the penguin and Darwin Mini, partake in. Using an LMA-focused approach (enabled by our skeletal tracking Kinect algorithm), we find that our participants show increased frequency of movement and speed when the game has a musical accompaniment. Therefore, participants may have increased engagement with our robots and game if music is present. We also begin exploring motion learning for future works.

hi

link (url) [BibTex]

link (url) [BibTex]


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Design and evaluation of a novel mechanical device to improve hemiparetic gait: a case report

Fjeld, K., Hu, S., Kuchenbecker, K. J., Vasudevan, E. V.

Extended abstract presented at the Biomechanics and Neural Control of Movement Conference (BANCOM), 2016, Poster presentation given by Fjeld (misc)

hi

Project Page [BibTex]

Project Page [BibTex]


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One Sensor, Three Displays: A Comparison of Tactile Rendering from a BioTac Sensor

Brown, J. D., Ibrahim, M., Chase, E. D. Z., Pacchierotti, C., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE Haptics Symposium, Philadelphia, Pennsylvania, USA, April 2016 (misc)

hi

[BibTex]

[BibTex]


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Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)

ei

[BibTex]

[BibTex]


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Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)

ei

pdf [BibTex]

pdf [BibTex]


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Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set

Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.

2016 (misc)

ei

Arxiv [BibTex]

Arxiv [BibTex]


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Designing Human-Robot Exercise Games for Baxter

Fitter, N. T., Hawkes, D. T., Johnson, M. J., Kuchenbecker, K. J.

2016, Late-breaking results report presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (misc)

hi

Project Page [BibTex]

Project Page [BibTex]


Thumb xl sabteaser
Perceiving Systems (2011-2015)
Scientific Advisory Board Report, 2016 (misc)

ps

pdf [BibTex]

pdf [BibTex]


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Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke

Castaneda, S., Katiyar, P., Russo, F., Calaminus, C., Disselhorst, J. A., Ziemann, U., Kohlhofer, U., Quintanilla-Martinez, L., Poli, S., Pichler, B. J.

World Molecular Imaging Conference, 2016 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]


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Multi-view learning on multiparametric PET/MRI quantifies intratumoral heterogeneity and determines therapy efficacy

Katiyar, P., Divine, M. R., Kohlhofer, U., Quintanilla-Martinez, L., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J., Disselhorst, J. A.

World Molecular Imaging Conference, 2016 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]


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Extrapolation and learning equations

Martius, G., Lampert, C. H.

2016, arXiv preprint \url{https://arxiv.org/abs/1610.02995} (misc)

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Project Page [BibTex]

Project Page [BibTex]


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IMU-Mediated Real-Time Human-Baxter Hand-Clapping Interaction

Fitter, N. T., Huang, Y. E., Mayer, J. P., Kuchenbecker, K. J.

2016, Late-breaking results report presented at the {\em IEEE/RSJ International Conference on Intelligent Robots and Systems} (misc)

hi

[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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Haptic Textures for Online Shopping

Culbertson, H., Kuchenbecker, K. J.

Interactive demonstrations in The Retail Collective exhibit, presented at the Dx3 Conference in Toronto, Canada, March 2015 (misc)

hi

[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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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei

[BibTex]

[BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

ei

[BibTex]

[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]

2013


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Jointonation: Robotization of the Human Body by Vibrotactile Feedback

Kurihara, Y., Hachisu, T., Kuchenbecker, K. J., Kajimoto, H.

Emerging Technologies Demonstration with Talk at ACM SIGGRAPH Asia, Hong Kong, November 2013, Hands-on demonstration given by Kurihara, Takei, and Nakai. Best Demonstration Award as voted by the Program Committee (misc)

hi

[BibTex]

2013


[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,651 (misc)

pi

[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent App. 14/016,683 (misc)

pi

[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Kim, S.

sep 2013, US Patent 8,524,092 (misc)

pi

[BibTex]

[BibTex]


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

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

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

ei

Web [BibTex]

Web [BibTex]


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Data-Driven Modeling and Rendering of Isotropic Textures

Culbertson, H., McDonald, C. G., Goodman, B. E., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Daejeon, South Korea, April 2013, Best Demonstration Award (by audience vote) (misc)

hi

[BibTex]

[BibTex]


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Adding Haptics to Robotic Surgery

J. Kuchenbecker, K., Brzezinski, A., D. Gomez, E., Gosselin, M., Hui, J., Koch, E., Koehn, J., McMahan, W., Mahajan, K., Nappo, J., Shah, N.

Learning Center Station at SAGES (Society of American Gastrointestinal and Endoscopic Surgeons) Annual Meeting, Baltimore, Maryland, USA, April 2013 (misc)

hi

[BibTex]

[BibTex]


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Dry adhesives and methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2013, US Patent App. 13/845,702 (misc)

pi

[BibTex]

[BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

ei

PDF [BibTex]

PDF [BibTex]

2004


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Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

ei

PDF [BibTex]


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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract
Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

ei

PDF [BibTex]


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The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative comparative study

Schweikert, G., Luecken, U., Pfeifer, G., Baumeister, W., Plitzko, J.

The thirteenth European Microscopy Congress, August 2004 (talk)

ei

[BibTex]

[BibTex]


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Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data.

ei

PDF [BibTex]


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Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D.

January 2004 (talk)

Abstract
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

ei

PDF [BibTex]


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Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract
A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.

ei

PDF [BibTex]

PDF [BibTex]


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Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B.

Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

Abstract
Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Nanoscale Materials for Energy Storage
{Materials Science \& Engineering B}, 108, pages: 292, Elsevier, 2004 (misc)

mms

[BibTex]

[BibTex]