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2018


Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., Pons-Moll, G.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 37, pages: 185:1-185:15, ACM, November 2018, Two first authors contributed equally (article)

Abstract
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330,000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.

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data code pdf preprint errata video DOI Project Page [BibTex]

2018


data code pdf preprint errata video DOI Project Page [BibTex]


A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm
A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm

Anderson, M., Anderson, S., Berenz, V.

Proceedings of the IEEE, pages: 1,15, October 2018 (article)

Abstract
In this paper, a case-supported principle-based behavior paradigm is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake. We believe that this is the case since we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles that balance these features when they are in conflict. Such principles not only help ensure ethical behavior of complex and dynamic systems but also can serve as a basis for justification of this behavior. The requirements, methods, implementation, and evaluation components of the paradigm are detailed as well as its instantiation in both a simulated and real robot functioning in the domain of eldercare.

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link (url) DOI [BibTex]


Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles
Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Price, E., Lawless, G., Ludwig, R., Martinovic, I., Buelthoff, H. H., Black, M. J., Ahmad, A.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3193-3200, IEEE, October 2018, Also accepted and presented in the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (article)

Abstract
Multi-camera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, online, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs and active selection of most informative regions of image. We demonstrate the efficiency of our approach through simulations with up to 16 robots and real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. ROS-based source code is provided for the benefit of the community.

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Published Version link (url) DOI [BibTex]

Published Version link (url) DOI [BibTex]


First Impressions of Personality Traits From Body Shapes
First Impressions of Personality Traits From Body Shapes

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., O’Toole, A. J.

Psychological Science, 29(12):1969-–1983, October 2018 (article)

Abstract
People infer the personalities of others from their facial appearance. Whether they do so from body shapes is less studied. We explored personality inferences made from body shapes. Participants rated personality traits for male and female bodies generated with a three-dimensional body model. Multivariate spaces created from these ratings indicated that people evaluate bodies on valence and agency in ways that directly contrast positive and negative traits from the Big Five domains. Body-trait stereotypes based on the trait ratings revealed a myriad of diverse body shapes that typify individual traits. Personality-trait profiles were predicted reliably from a subset of the body-shape features used to specify the three-dimensional bodies. Body features related to extraversion and conscientiousness were predicted with the highest consensus, followed by openness traits. This study provides the first comprehensive look at the range, diversity, and reliability of personality inferences that people make from body shapes.

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publisher site pdf DOI [BibTex]

publisher site pdf DOI [BibTex]


Playful: Reactive Programming for Orchestrating Robotic Behavior
Playful: Reactive Programming for Orchestrating Robotic Behavior

Berenz, V., Schaal, S.

IEEE Robotics Automation Magazine, 25(3):49-60, September 2018 (article) In press

Abstract
For many service robots, reactivity to changes in their surroundings is a must. However, developing software suitable for dynamic environments is difficult. Existing robotic middleware allows engineers to design behavior graphs by organizing communication between components. But because these graphs are structurally inflexible, they hardly support the development of complex reactive behavior. To address this limitation, we propose Playful, a software platform that applies reactive programming to the specification of robotic behavior.

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playful website playful_IEEE_RAM link (url) DOI [BibTex]


ClusterNet: Instance Segmentation in RGB-D Images
ClusterNet: Instance Segmentation in RGB-D Images

Shao, L., Tian, Y., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of {\em individual\/} objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.

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link (url) [BibTex]

link (url) [BibTex]


Visual Perception and Evaluation of Photo-Realistic Self-Avatars From {3D} Body Scans in Males and Females
Visual Perception and Evaluation of Photo-Realistic Self-Avatars From 3D Body Scans in Males and Females

Thaler, A., Piryankova, I., Stefanucci, J. K., Pujades, S., de la Rosa, S., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

Frontiers in ICT, 5, pages: 1-14, September 2018 (article)

Abstract
The creation or streaming of photo-realistic self-avatars is important for virtual reality applications that aim for perception and action to replicate real world experience. The appearance and recognition of a digital self-avatar may be especially important for applications related to telepresence, embodied virtual reality, or immersive games. We investigated gender differences in the use of visual cues (shape, texture) of a self-avatar for estimating body weight and evaluating avatar appearance. A full-body scanner was used to capture each participant's body geometry and color information and a set of 3D virtual avatars with realistic weight variations was created based on a statistical body model. Additionally, a second set of avatars was created with an average underlying body shape matched to each participant’s height and weight. In four sets of psychophysical experiments, the influence of visual cues on the accuracy of body weight estimation and the sensitivity to weight changes was assessed by manipulating body shape (own, average) and texture (own photo-realistic, checkerboard). The avatars were presented on a large-screen display, and participants responded to whether the avatar's weight corresponded to their own weight. Participants also adjusted the avatar's weight to their desired weight and evaluated the avatar's appearance with regard to similarity to their own body, uncanniness, and their willingness to accept it as a digital representation of the self. The results of the psychophysical experiments revealed no gender difference in the accuracy of estimating body weight in avatars. However, males accepted a larger weight range of the avatars as corresponding to their own. In terms of the ideal body weight, females but not males desired a thinner body. With regard to the evaluation of avatar appearance, the questionnaire responses suggest that own photo-realistic texture was more important to males for higher similarity ratings, while own body shape seemed to be more important to females. These results argue for gender-specific considerations when creating self-avatars.

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pdf DOI [BibTex]

pdf DOI [BibTex]


Robust Physics-based Motion Retargeting with Realistic Body Shapes
Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

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pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


Real-time Perception meets Reactive Motion Generation
Real-time Perception meets Reactive Motion Generation

(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)

Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)

Abstract
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

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arxiv video video link (url) DOI Project Page [BibTex]


Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted
Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted

Mölbert, S. C., Thaler, A., Mohler, B. J., Streuber, S., Romero, J., Black, M. J., Zipfel, S., Karnath, H., Giel, K. E.

Psychological Medicine, 48(4):642-653, March 2018 (article)

Abstract
Background: Body image disturbance (BID) is a core symptom of anorexia nervosa (AN), but as yet distinctive features of BID are unknown. The present study aimed at disentangling perceptual and attitudinal components of BID in AN. Methods: We investigated n=24 women with AN and n=24 controls. Based on a 3D body scan, we created realistic virtual 3D bodies (avatars) for each participant that were varied through a range of ±20% of the participants' weights. Avatars were presented in a virtual reality mirror scenario. Using different psychophysical tasks, participants identified and adjusted their actual and their desired body weight. To test for general perceptual biases in estimating body weight, a second experiment investigated perception of weight and shape matched avatars with another identity. Results: Women with AN and controls underestimated their weight, with a trend that women with AN underestimated more. The average desired body of controls had normal weight while the average desired weight of women with AN corresponded to extreme AN (DSM-5). Correlation analyses revealed that desired body weight, but not accuracy of weight estimation, was associated with eating disorder symptoms. In the second experiment, both groups estimated accurately while the most attractive body was similar to Experiment 1. Conclusions: Our results contradict the widespread assumption that patients with AN overestimate their body weight due to visual distortions. Rather, they illustrate that BID might be driven by distorted attitudes with regard to the desired body. Clinical interventions should aim at helping patients with AN to change their desired weight.

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doi pdf DOI Project Page [BibTex]


Body size estimation of self and others in females varying in {BMI}
Body size estimation of self and others in females varying in BMI

Thaler, A., Geuss, M. N., Mölbert, S. C., Giel, K. E., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

PLoS ONE, 13(2), Febuary 2018 (article)

Abstract
Previous literature suggests that a disturbed ability to accurately identify own body size may contribute to overweight. Here, we investigated the influence of personal body size, indexed by body mass index (BMI), on body size estimation in a non-clinical population of females varying in BMI. We attempted to disentangle general biases in body size estimates and attitudinal influences by manipulating whether participants believed the body stimuli (personalized avatars with realistic weight variations) represented their own body or that of another person. Our results show that the accuracy of own body size estimation is predicted by personal BMI, such that participants with lower BMI underestimated their body size and participants with higher BMI overestimated their body size. Further, participants with higher BMI were less likely to notice the same percentage of weight gain than participants with lower BMI. Importantly, these results were only apparent when participants were judging a virtual body that was their own identity (Experiment 1), but not when they estimated the size of a body with another identity and the same underlying body shape (Experiment 2a). The different influences of BMI on accuracy of body size estimation and sensitivity to weight change for self and other identity suggests that effects of BMI on visual body size estimation are self-specific and not generalizable to other bodies.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Distributed Event-Based State Estimation for Networked Systems: An LMI Approach

Muehlebach, M., Trimpe, S.

IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

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arXiv (extended version) DOI Project Page [BibTex]

arXiv (extended version) DOI Project Page [BibTex]


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Active microrheology in corrugated channels

Puertas, A. M., Malgaretti, P., Pagonabarraga, I.

The Journal of Chemical Physics, 149(17), American Institute of Physics, Woodbury, N.Y., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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First-passage dynamics of linear stochastic interface models: weak-noise theory and influence of boundary conditions

Gross, M.

Journal of Statistical Mechanics: Theory and Experiment, 2018, Institute of Physics Publishing, Bristol, England, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Cu@TiO2 Janus microswimmers with a versatile motion mechanism

Wang, L. L., Popescu, M. N., Stavale, F., Ali, A., Gemming, T., Simmchen, J.

Soft Matter, 14(34):6969-6973, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Probing interface localization-delocalization transitions by colloids

Kondrat, S., Vasilyev, O., Dietrich, S.

Journal of Physics: Condensed Matter, 30(41), IOP Publishing, Bristol, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Medical imaging for the tracking of micromotors

Vilela, D., Coss\’\io, U., Parmar, J., Mart\’\inez-Villacorta, A. M., Gómez-Vallejo, V., Llop, J., Sánchez, S.

ACS Nano, 12(2):1220-1227, American Chemical Society, Washington, DC, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Noncontinuous Super-Diffusive Dynamics of a Light-Activated Nanobottle Motor

Xuan, M., Mestre, R., Gao, C., Zhou, C., He, Q., Sánchez, S.

Angewandte Chemie International Edition, 57(23):6838-6842, Wiley-VCH, Weinheim, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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On the origin of forward-backward multiplicity correlations in pp collisions at ultrarelativistic energies

Bravina, L., Bleibel, J., Zabrodin, E.

Physics Letters B, 787, pages: 146-152, North-Holland, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Autophoretic motion in three dimensions

Lisicki, M., Reigh, S., Lauga, E.

Soft Matter, 14(17):3304-3314, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Order-disorder transitions in lattice gases with annealed reactive constraints

Dudka, M., Bénichou, O., Oshanin, G.

Journal of Statistical Mechanics: Theory and Experiment, 2018, Institute of Physics Publishing, Bristol, England, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Bacterial Biohybrid Microswimmers

Bastos-Arrieta, J., Revilla-Guarinos, A., Uspal, W., Simmchen, J.

Frontiers in Robotics and AI, 5, Frontiers Media, Lausanne, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Fluctuational electrodynamics for nonlinear materials in and out of thermal equilibrium

Soo, H., Krüger, M.

Physical Review B, 97(4), American Physical Society, Woodbury, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Local pressure for confined systems

Malgaretti, P., Bier, M.

Physical Review E, 97(2), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


Combining learned and analytical models for predicting action effects
Combining learned and analytical models for predicting action effects

Kloss, A., Schaal, S., Bohg, J.

arXiv, 2018 (article) Submitted

Abstract
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction.

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arXiv pdf link (url) [BibTex]


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Charge polarization, local electroneutrality breakdown and eddy formation due to electroosmosis in varying-section channels

Chinappi, M., Malgaretti, P.

Soft Matter, 14(45):9083-9087, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Critical Casimir interactions and percolation: The quantitative description of critical fluctuations

Vasilyev, O.

Physical Review E, 98(6), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Shear-density coupling for a compressible single-component yield-stress fluid

Gross, M., Varnik, F.

Soft Matter, 14(22):4577-4590, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Shape-dependent guidance of active Janus particles by chemically patterned surfaces

Uspal, W. E., Popescu, M. N., Tasinkevych, M., Dietrich, S.

New Journal of Physics, 20, IOP Publishing, Bristol, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Extrapolation to nonequilibrium from coarse-grained response theory

Basu, U., Helden, L., Krüger, M.

Physical Review Letters, 120(18), American Physical Society, Woodbury, N.Y., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Directed Flow of Micromotors through Alignment Interactions with Micropatterned Ratchets

Katuri, J., Caballero, D., Voituriez, R., Samitier, J., Sánchez, S.

ACS Nano, 12(7):7282-7291, American Chemical Society, Washington, DC, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Spontaneous symmetry breaking of charge-regulated surfaces

Majee, A., Bier, M., Podgornik, R.

Soft Matter, 14(6):985-991, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Electrostatic interaction between dissimilar colloids at fluid interfaces

Majee, A., Schmetzer, T., Bier, M.

Physical Review E, 97(4), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Wetting transition of a cylindrical cavity engraved on a hydrophobic surface

Kim, H., Ha, M. Y., Jang, J.

The Journal of Physical Chemistry C, 122(4):2122-2126, American Chemical Society, Washington, D.C., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Curvature corrections to the nonlocal interfacial model for short-ranged forces

Romero-Enrique, J.M., Squarcini, Alessio, Parry, A. O., Goldbart, P. M.

Physical Review E, 97(6), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Effective squirmer models for self-phoretic chemically active spherical colloids

Popescu, M. N., Uspal, W. E., Eskandari, Z., Tasinkevych, M., Dietrich, S.

The European Physical Journal E, 41(12), EDP Sciences; Società Italiana di Fisica; Springer, Les Ulis; Bologna; Heidelberg, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Two time scales for self and collective diffusion near the critical point in a simple patchy model for proteins with floating bonds

Bleibel, J., Habiger, M., Lütje, M., Hirschmann, F., Roosen-Runge, F., Seydel, T., Zhang, F., Schreiber, F., Oettel, M.

Soft Matter, 14(39):8006-8016, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Globulelike Conformation and Enhanced Diffusion of Active Polymers

Bianco, V., Locatelli, E., Malgaretti, P.

Physical Review Letters, 121(21), American Physical Society, Woodbury, N.Y., 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Rheological behavior of colloidal suspension with long-range interactions

Arietaleaniz, S., Malgaretti, P., Pagonabarraga, I., Hidalgo, R. C.

Physical Review E, 98(4), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Solvent coarsening around colloids driven by temperature gradients

Roy, S., Dietrich, S., Maciolek, A.

Physical Review E, 97(4), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Cross-stream migration of active particles

Katuri, J., Uspal, W., Simmchen, J., López, A. M., Sanchez, S.

Science Advances, 4(1), AAAS, Washington, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Transient dynamics of electric double-layer capacitors: Exact expressions within the Debye-Falkenhagen approximation

Janssen, M., Bier, M.

Physical Review E, 97(5), American Physical Society, Melville, NY, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Coalescence preference and droplet size inequality during fluid phase segregation

Roy, S.

EPL, 121(3), EDP Science, Les-Ulis, 2018 (article)

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DOI [BibTex]


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Structure of interfaces at phase coexistence. Theory and numerics

Delfino, G., Selke, W., Squarcini, A.

Journal of Statistical Mechanics: Theory and Experiment, 2018, Institute of Physics Publishing, Bristol, England, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Power spectral density of a single Brownian trajectory: what one can and cannot learn from it

Krapf, D., Marinari, E., Metzler, Ralf, Oshanin, Gleb, Xu, Xinran, Squarcini, A.

New Journal of Physics, 20, IOP Publishing, Bristol, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Diffusiophoretically induced interactions between chemically active and inert particles

Reigh, Shang-Yik, Chuphal, P., Thakur, S., Kapral, R.

Soft Matter, 14(29):6043-6057, Royal Society of Chemistry, Cambridge, UK, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Collective behavior of colloids due to critical Casimir interactions

Maciolek, A., Dietrich, S.

Reviews of Modern Physics, 90(4), American Physical Society, Minneapolis, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


Temporal Human Action Segmentation via Dynamic Clustering
Temporal Human Action Segmentation via Dynamic Clustering

Zhang, Y., Sun, H., Tang, S., Neumann, H.

arXiv preprint arXiv:1803.05790, 2018 (article)

Abstract
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

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link (url) [BibTex]

link (url) [BibTex]


Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 (article)

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Heat radiation and transfer in confinement

Asheichyk, K., Krüger, M.

Physical Review B, 98(19), American Physical Society, Woodbury, NY, 2018 (article)

icm

DOI [BibTex]

DOI [BibTex]