Header logo is


2020


A gamified app that helps people overcome self-limiting beliefs by promoting metacognition
A gamified app that helps people overcome self-limiting beliefs by promoting metacognition

Amo, V., Lieder, F.

Virtual, SIG 8 Meets SIG 16, September 2020 (conference) Accepted

Abstract
Previous research has shown that approaching learning with a growth mindset is key for maintaining motivation and overcoming setbacks. Mindsets are systems of beliefs that people hold to be true. They influence a person's attitudes, thoughts, and emotions when they learn something new or encounter challenges. In clinical psychology, metareasoning (reflecting on one's mental processes) and meta-awareness (recognizing thoughts as mental events instead of equating them to reality) have proven effective for overcoming maladaptive thinking styles. Hence, they are potentially an effective method for overcoming self-limiting beliefs in other domains as well. However, the potential of integrating assisted metacognition into mindset interventions has not been explored yet. Here, we propose that guiding and training people on how to leverage metareasoning and meta-awareness for overcoming self-limiting beliefs can significantly enhance the effectiveness of mindset interventions. To test this hypothesis, we develop a gamified mobile application that guides and trains people to use metacognitive strategies based on Cognitive Restructuring (CR) and Acceptance Commitment Therapy (ACT) techniques. The application helps users to identify and overcome self-limiting beliefs by working with aversive emotions when they are triggered by fixed mindsets in real-life situations. Our app aims to help people sustain their motivation to learn when they face inner obstacles (e.g. anxiety, frustration, and demotivation). We expect the application to be an effective tool for helping people better understand and develop the metacognitive skills of emotion regulation and self-regulation that are needed to overcome self-limiting beliefs and develop growth mindsets.

re

A gamified app that helps people overcome self-limiting beliefs by promoting metacognition [BibTex]


no image
How to navigate everyday distractions: Leveraging optimal feedback to train attention control

Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F.

Annual Meeting of the Cognitive Science Society, July 2020 (conference) Accepted

Abstract
To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people's productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p < .01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

re sf

How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]


no image
Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment

Kemtur, A., Jain, Y. R., Mehta, A., Callaway, F., Consul, S., Stojcheski, J., Lieder, F.

Virtual, CogSci 2020, July 2020, Anirudha Kemtur and Yash Raj Jain contributed equally to this publication. (conference) Accepted

Abstract
Teaching clever heuristics is a promising approach to improve decision-making. We can leverage machine learning to discover clever strategies automatically. Current methods require an accurate model of the decision problems people face in real life. But most models are misspecified because of limited information and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by model-ing model-misspecification and handling uncertainty about the real-world according to Bayesian inference. We translate our methods into an intelligent tutor that automatically discovers and teaches robust planning strategies. Our robust cognitive tutor significantly improved human decision-making when the model was so biased that conventional cognitive tutors were no longer effective. These findings highlight that our robust strategy discovery methods are a significant step towards leveraging artificial intelligence to improve human decision-making in the real world.

re

Leveraging Machine Learning to Automatically Derive Robust Planning Strategiesfrom Biased Models of the Environment [BibTex]


no image
Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints

Strecke, M., Stückler, J.

In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020 (inproceedings)

ev

preprint project page [BibTex]

preprint project page [BibTex]


Interface-mediated spontaneous symmetry breaking and mutual communication between drops containing chemically active particles
Interface-mediated spontaneous symmetry breaking and mutual communication between drops containing chemically active particles

Singh, D., Domínguez, A., Choudhury, U., Kottapalli, S., Popescu, M., Dietrich, S., Fischer, P.

Nature Communications, 11(2210), May 2020 (article)

Abstract
Symmetry breaking and the emergence of self-organized patterns is the hallmark of com- plexity. Here, we demonstrate that a sessile drop, containing titania powder particles with negligible self-propulsion, exhibits a transition to collective motion leading to self-organized flow patterns. This phenomenology emerges through a novel mechanism involving the interplay between the chemical activity of the photocatalytic particles, which induces Mar- angoni stresses at the liquid–liquid interface, and the geometrical confinement provided by the drop. The response of the interface to the chemical activity of the particles is the source of a significantly amplified hydrodynamic flow within the drop, which moves the particles. Furthermore, in ensembles of such active drops long-ranged ordering of the flow patterns within the drops is observed. We show that the ordering is dictated by a chemical com- munication between drops, i.e., an alignment of the flow patterns is induced by the gradients of the chemicals emanating from the active particles, rather than by hydrodynamic interactions.

pf icm

link (url) DOI [BibTex]


no image
Automatic Discovery of Interpretable Planning Strategies

Skirzyński, J., Becker, F., Lieder, F.

May 2020 (article) Submitted

Abstract
When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decisionmakers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that the provision of decision rules as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Furthermore, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making.

re

Automatic Discovery of Interpretable Planning Strategies The code for our algorithm and the experiments is available [BibTex]


no image
Advancing Rational Analysis to the Algorithmic Level

Lieder, F., Griffiths, T. L.

Behavioral and Brain Sciences, 43, E27, March 2020 (article)

Abstract
The commentaries raised questions about normativity, human rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.

re

Advancing rational analysis to the algorithmic level DOI [BibTex]

Advancing rational analysis to the algorithmic level DOI [BibTex]


no image
Learning to Overexert Cognitive Control in a Stroop Task

Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., Cohen, J.

Febuary 2020, Laura Bustamante and Falk Lieder contributed equally to this publication. (article) In revision

Abstract
How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a given situation. This suggests that people may generalize the value of control learned in one situation to other situations with shared features, even when the demands for cognitive control are different. This makes the intriguing prediction that what a person learned in one setting could, under some circumstances, cause them to misestimate the need for, and potentially over-exert control in another setting, even if this harms their performance. To test this prediction, we had participants perform a novel variant of the Stroop task in which, on each trial, they could choose to either name the color (more control-demanding) or read the word (more automatic). However only one of these tasks was rewarded, it changed from trial to trial, and could be predicted by one or more of the stimulus features (the color and/or the word). Participants first learned colors that predicted the rewarded task. Then they learned words that predicted the rewarded task. In the third part of the experiment, we tested how these learned feature associations transferred to novel stimuli with some overlapping features. The stimulus-task-reward associations were designed so that for certain combinations of stimuli the transfer of learned feature associations would incorrectly predict that more highly rewarded task would be color naming, which would require the exertion of control, even though the actually rewarded task was word reading and therefore did not require the engagement of control. Our results demonstrated that participants over-exerted control for these stimuli, providing support for the feature-based learning mechanism described by the LVOC model.

re

Learning to Overexert Cognitive Control in a Stroop Task DOI [BibTex]

Learning to Overexert Cognitive Control in a Stroop Task DOI [BibTex]


Toward a Formal Theory of Proactivity
Toward a Formal Theory of Proactivity

Lieder, F., Iwama, G.

January 2020 (article) Submitted

Abstract
Beyond merely reacting to their environment and impulses, people have the remarkable capacity to proactively set and pursue their own goals. But the extent to which they leverage this capacity varies widely across people and situations. The goal of this article is to make the mechanisms and variability of proactivity more amenable to rigorous experiments and computational modeling. We proceed in three steps. First, we develop and validate a mathematically precise behavioral measure of proactivity and reactivity that can be applied across a wide range of experimental paradigms. Second, we propose a formal definition of proactivity and reactivity, and develop a computational model of proactivity in the AX Continuous Performance Task (AX-CPT). Third, we develop and test a computational-level theory of meta-control over proactivity in the AX-CPT that identifies three distinct meta-decision-making problems: intention setting, resolving response conflict between intentions and automaticity, and deciding whether to recall context and intentions into working memory. People's response frequencies in the AX-CPT were remarkably well captured by a mixture between the predictions of our models of proactive and reactive control. Empirical data from an experiment varying the incentives and contextual load of an AX-CPT confirmed the predictions of our meta-control model of individual differences in proactivity. Our results suggest that proactivity can be understood in terms of computational models of meta-control. Our model makes additional empirically testable predictions. Future work will extend our models from proactive control in the AX-CPT to proactive goal creation and goal pursuit in the real world.

re

Toward a formal theory of proactivity DOI [BibTex]


no image
Axisymmetric spheroidal squirmers and self-diffusiophoretic particles

Pöhnl, R., Popescu, M. N., Uspal, W. E.

Journal of Physics: Condensed Matter, 32(16), IOP Publishing, Bristol, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Tracer diffusion on a crowded random Manhattan lattice

Mej\’\ia-Monasterio, C., Nechaev, S., Oshanin, G., Vasilyev, O.

New Journal of Physics, 22(3), IOP Publishing, Bristol, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
TUM Flyers: Vision-Based MAV Navigation for Systematic Inspection of Structures

Usenko, V., Stumberg, L. V., Stückler, J., Cremers, D.

In Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users: The Experience of the European Robotics Challenges, 136, pages: 189-209, Springer International Publishing, 2020 (inbook)

ev

[BibTex]

[BibTex]


no image
ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work]

Wirzberger, M., Oreshnikov, I., Passy, J., Lado, A., Shenhav, A., Lieder, F.

66th Spring Conference of the German Ergonomics Society, 2020 (conference)

Abstract
Unser digitales Zeitalter lebt von Informationen und stellt unsere begrenzte Verarbeitungskapazität damit täglich auf die Probe. Gerade in der Wissensarbeit haben ständige Ablenkungen erhebliche Leistungseinbußen zur Folge. Unsere intelligente Anwendung ACTrain setzt genau an dieser Stelle an und verwandelt Computertätigkeiten in eine Trainingshalle für den Geist. Feedback auf Basis maschineller Lernverfahren zeigt anschaulich den Wert auf, sich nicht von einer selbst gewählten Aufgabe ablenken zu lassen. Diese metakognitive Einsicht soll zum Durchhalten motivieren und das zugrunde liegende Fertigkeitsniveau der Aufmerksamkeitskontrolle stärken. In laufenden Feldexperimenten untersuchen wir die Frage, ob das Training mit diesem optimalen Feedback die Aufmerksamkeits- und Selbstkontrollfertigkeiten im Vergleich zu einer Kontrollgruppe ohne Feedback verbessern kann.

re sf

link (url) Project Page [BibTex]


no image
Wetting transitions on soft substrates

Napiorkowski, M., Schimmele, L., Dietrich, S.

{EPL}, 129(1), EDP Science, Les-Ulis, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Blessing and Curse: How a Supercapacitor Large Capacitance Causes its Slow Charging

Lian, C., Janssen, M., Liu, H., van Roij, R.

Physical Review Letters, 124(7), American Physical Society, Woodbury, N.Y., 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Interplay of quenching temperature and drift in Brownian dynamics

Khalilian, H., Nejad, M. R., Moghaddam, A. G., Rohwer, C. M.

EPL, 128(6), EDP Science, Les-Ulis, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Fractal-seaweeds type functionalization of graphene

Amsharov, K., Sharapa, D. I., Vasilyev, O. A., Martin, O., Hauke, F., Görling, A., Soni, H., Hirsch, A.

Carbon, 158, pages: 435-448, Elsevier, Amsterdam, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Planning from Images with Deep Latent Gaussian Process Dynamics

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J.

2nd Annual Conference on Learning for Dynamics and Control (L4DC) , 2020, to appear, arXiv:2005.03770 (conference) Accepted

ev

preprint project page poster [BibTex]

preprint project page poster [BibTex]


no image
Effective pair interaction of patchy particles in critical fluids

Farahmand Bafi, N., Nowakowski, P., Dietrich, S.

The Journal of Chemical Physics, 152(11), American Institute of Physics, Woodbury, N.Y., 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
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]


no image
Cassie-Wenzel transition of a binary liquid mixture on a nanosculptured surface

Singh, S. L., Schimmele, L., Dietrich, S.

Physical Review E, 101(5), American Physical Society, Melville, NY, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Adopting the Boundary Homogenization Approximation from Chemical Kinetics to Motile Chemically Active Particles

Popescu, M. N., Uspal, W. E.

In Chemical Kinetics, pages: 517-540, World Scientific, New Jersey, NJ, 2020 (incollection)

icm

DOI [BibTex]

DOI [BibTex]


no image
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

Wang, R., Yang, N., Stückler, J., Cremers, D.

In Accepted for IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings) Accepted

ev

[BibTex]

[BibTex]


no image
Energy storage in steady states under cyclic local energy input

Zhang, Y., Holyst, R., Maciolek, A.

Physical Review E, 101(1), American Physical Society, Melville, NY, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Numerical simulations of self-diffusiophoretic colloids at fluid interfaces

Peter, T., Malgaretti, P., Rivas, N., Scagliarini, A., Harting, J., Dietrich, S.

Soft Matter, 16(14):3536-3547, Royal Society of Chemistry, Cambridge, UK, 2020 (article)

icm

DOI [BibTex]

DOI [BibTex]

2015


no image
Structures of simple liquids in contact with nanosculptured surfaces

Singh, S. L., Schimmele, L., Dietrich, S.

Physical Review E, 91(3), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

2015


DOI [BibTex]


no image
Line contribution to the critical Casimir force between a homogeneous and a chemically stepped surface

Parisen Toldin, F., Tröndle, M., Dietrich, S.

Journal of Physics: Condensed Matter, 27(21), IOP Publishing, Bristol, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Self-propulsion of a catalytically active particle near a planar wall: from reflection to sliding and hovering

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

Soft Matter, 11(3):434-438, Royal Society of Chemistry, Cambridge, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
A size dependent evaluation of the cytotoxicity and uptake of nanographene oxide

Mendes, R. G., Koch, B., Bachmatiuk, A., Ma, X., Sanchez, S., Damm, C., Schmidt, O. G., Gemming, T., Eckert, J., Rümmeli, M. H.

Journal of Materials Chemistry B, 3(12):2522-2529, Royal Society of Chemistry, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
A bio-catalytically driven Janus mesoporous silica cluster motor with magnetic guidance

Ma, X., Sanchez, S.

Chemical Communications, 51(25):5467-5470, Royal Society of Chemistry, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Sperm Dynamics in Tubular Confinement

Magdanz, V., Koch, B., Sanchez, S., Schmidt, O. G.

Small, 11(7):781-785, Wiley Online Library, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Chromatic patchy particles: Effects of specific interactions on liquid structure

Vasilyev, O., Klumov, B. A., Tkachenko, A. V.

Physical Review E, 92(1), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Monte Carlo study of anisotropic scaling generated by disorder

Vasilyev, O., Berche, B., Dudka, M., Holovatch, Y.

Physical Review E, 92(4), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Fluctuations and diffusion in sheared athermal suspensions of deformable particles

Gross, M., Krüger, T., Varnik, F.

EPL, 108(6), IoPP, Bristol, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Enzyme-Powered Hollow Mesoporous Janus Nanomotors

Ma, Xing, Jannasch, Anita, Albrecht, Urban-Raphael, Hahn, Kersten, Miguel-Lopéz, Albert, Schäfer, Erik, Sanchez, Samuel

Nano Letters, 15(10):7043-7050, American Chemical Society, Washington, DC, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Interaction between colloidal particles on an oil\textendashwater interface in dilute and dense phases

Parolini, L., Cicuta, A. D. P., Law, A. D., Maestro, A., Buzza, M. A.

Journal of Physics: Condensed Matter, 27(19), IOP Publishing, Bristol, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Anomalous Magnetotransport in Disordered Structures: Classical Edge-State Percolation

Schirmacher, Walter, Fuchs, Benedikt, Höfling, Felix, Franosch, Thomas

Physical Review Letters, 115, American Physical Society, Woodbury, N.Y., 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Precise Localization and Control of Catalytic Janus Micromotors using Weak Magnetic Fields

Khalil, I. S., Magdanz, V., Sanchez, S., Schmidt, O. G., Misra, S.

International Journal of Advanced Robotic Systems, 12, InTech, Rijeka, Croatia, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Nano-photocatalysts in microfluidics, energy conversion and environmental applications

Parmar, J., Jang, S., Soler, L., Kim, D., Sánchez, S.

Lab on a Chip, 15(11):2352-2356, Royal Society of Chemistry, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Static dielectric properties of dense ionic fluids

Zarubin, G., Bier, M.

The Journal of Chemical Physics, 142(18), American Institute of Physics, Woodbury, N.Y., 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Chemically Powered Micro-and Nanomotors

Sánchez, S., Soler, L., Katuri, J.

Angewandte Chemie, International Edition, 54(5):1414-1444, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2015 (article)

icm

[BibTex]

[BibTex]


no image
Convergence of large-deviation estimators

Rohwer, C. M., Angeletti, F., Touchette, H.

Physical Review E, 92(5), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Geometrically Tuned Channel Permeability

Malgaretti, P., Pagonabarraga, I., Rubi, J. M.

Macromolecular Symposia, 357(1):178-188, WILEY-VCH Verlag GmbH, Weinheim, Fed. Rep. of Germany, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Critical Casimir forces between planar and crenellated surfaces

Troendle, M., Harnau, L., Dietrich, S.

Journal of Physics: Condensed Matter, 27(21), IOP Publishing, Bristol, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Theory of rheology in confinement

Aerov, A. A., Krüger, M.

Physical Review E, 92(4), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Implications of interface conventions for morphometric thermodynamics

Reindl, A., Bier, M., Dietrich, S.

Physical Review E, 91(2), American Physical Society, Melville, NY, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Linked topological colloids in a nematic host

Martinez, A., Hermosillo, L., Tasinkevych, M., Smalyukh, I. I.

Proceedings of the National Academy of Sciences of the United States of America, 112(15):4546-4551, National Academy of Sciences, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Colloidal spirals in nematic liquid crystals

Senyuk, B., Pandey, M.B., Liu, Q., Tasinkevych, M., Smalyukh, I. I.

Soft Matter, 11, pages: 8758-8767, Royal Society of Chemistry, Cambridge, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Rheotaxis of spherical active particles near a planar wall

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

Soft Matter, 11(33):6613-6632, Royal Society of Chemistry, Cambridge, UK, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]


no image
Applications of three-dimensional (3D) printing for microswimmers and bio-hybrid robotics

Stanton, M. M., Trichet-Paredes, C., Sanchez, S.

Lab on a Chip, 15(7):1634-1637, Royal Society of Chemistry, Cambridge, 2015 (article)

icm

DOI [BibTex]

DOI [BibTex]