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Correlation Tracking: Using simulations to interpolate highly correlated particle tracks

https://doi.org/10.1103/PhysRevE.105.044608

King, Ella M.; Wang, Zizhao; Weitz, David A.; Spaepen, Frans; Brenner, Michael P. (April 2022, Physical Review E)

Despite significant advances in particle imaging technologies over the past two decades, few ad- vances have been made in particle tracking, i.e. linking individual particle positions across time series data. The state-of-the-art tracking algorithm is highly effective for systems in which the particles behave mostly independently. However, these algorithms become inaccurate when particle motion is highly correlated, such as in dense or strongly interacting systems. Accurate particle tracking is essential in the study of the physics of dense colloids, such as the study of dislocation formation, nucleation, and shear transformations. Here, we present a new method for particle tracking that incorporates information about the correlated motion of the particles. We demonstrate significant improvement over the state-of-the-art tracking algorithm in simulated data on highly correlated systems."
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Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]

https://doi.org/10.1109/MRA.2022.3213466

Xiao, Xuesu; Xu, Zifan; Wang, Zizhao; Song, Yunlong; Warnell, Garrett; Stone, Peter; Zhang, Tingnan; Ravi, Shravan; Wang, Gary; Karnan, Haresh; et al (December 2022, IEEE Robotics & Automation Magazine)

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Effects of Vimentin Intermediate Filaments on the Structure and Dynamics of In Vitro Multicomponent Interpenetrating Cytoskeletal Networks

https://doi.org/10.1103/PhysRevLett.127.108101

Shen, Yinan; Wu, Huayin; Lu, Peter J.; Wang, Dianzhuo; Shayegan, Marjan; Li, Hui; Shi, Weichao; Wang, Zizhao; Cai, Li-Heng; Xia, Jing; et al (September 2021, Physical Review Letters)

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