Active matter is based on the concepts of nonequilibrium thermodynamics applied to the most diverse disciplines. Active Brownian particles, unlike their passive counterparts, self-propel and give rise to complex behaviors distinctive of active matter. As the field is relatively recent, active matter still lacks curricular inclusion. Here, we propose macroscopic experiments using Hexbugs, a commercial toy robot, demonstrating effects peculiar of active systems, such as the setting into motion of passive objects via active particles, the sorting of active particles based on their mobility and chirality. Additionally, we provide a demonstration of Casimir-like attraction between planar objects mediated by active particles.
Active matter bridges the fundamental physics of nonequilibrium thermodynamics with applications spanning from biophysics to robotics. Active particles can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We show that Hexbugs perform active and chiral active motion, can set passive objects into motion and rotation. Finally, we show how to sort Hexbug by motility and chirality, and macroscopic demonstration of the Casimir-like activity-induced attraction between planar objects.
Many systems in biology, physics, and finance exhibit anomalous diffusion dynamics where the mean squared displacement grows with an exponent that deviates from one. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks are difficult when only few short trajectories are available, a common scenario in non-equilibrium and living systems. We show that long short-time memory (LSTM) recurrent neural networks excel at characterizing anomalous diffusion from a single short trajectory. The method we developed generalizes to experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers. We discuss the performance of the method in comparison to alternative ones in the context of the Anomalous Diffusion Challenge. In closing, we address the interpretability of the method.
DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of the art deep learning solutions and end-users. It provides tools for designing samples, simulating optical systems, training deep learning networks, and analyzing experimental data. We show the versatility of deep learning by solving a wide field of common problems in microscopy. Our hope is to serve as a platform for researchers to launch their solutions for the benifit of the entire field.
Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement with time has an exponent different from one and can be due to different mechanisms. We show that recurrent neural networks (RNNs) efficiently characterize anomalous diffusion by identifying the mechanism causing it and determining the anomalous exponent from a single short trajectory.
This method outperforms standard techniques and advanced ones when the available data points are limited, as is often the case in experiments. Furthermore, RNNs can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and measuring intermittent systems that switch between different kinds of anomalous diffusion. The method is validated on experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers.
We introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely, recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific force fields and applications.
There is a very limited number of methods to analyze experimental trajectories of systems with feedback and time delay. In most cases, an analytical approach is not even possible. In this study, we show that the feedback parameters and the delay can be accurately characterized using machine learning, namely recurrent neural networks. We demonstrate that our method can dramatically expand the number of time-delayed feedback scenarios that we can characterize. We exemplify our findings on different numerical and experimental scenarios.
From the start of digital video microscopy over 20 years ago, single particle tracking has been dominated by algorithmic approaches. These methods are successful at tracking well-defined particles in good imaging conditions but their performance degrades severely in more challenging conditions. To overcome the limitations of traditional algorithmic approaches, data-driven methods using deep learning have been introduced. They managed to successfully track colloidal particles as well as non-spherical biological objects, even in unsteady imaging conditions.
DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of the art deep learning solutions and end-users. It provides tools for designing samples, simulating optical systems, training deep learning networks, and analyzing experimental data. Moreover, the framework is packaged with an easy-to-use graphical user interface, designed to solve standard microscopy problems with no required programming experience. By specifically designing the framework with modularity and extendability in mind, we allow new methods to easily be implemented and combined with previous applications.
The calibration of physical force fields from particle trajectories is important for experiments in soft matter, biophysics, active matter, and colloidal science. However, it is not always possible to have a standard method to characterize a force field, especially for systems that are out of equilibrium. Here, we introduce a generic toolbox for calibrating any kind of conservative or non-conservative, fixed or time-varying potentials that is powered by recurrent neural networks (RNN). We show that with the help of neural networks, we can outperform standard methods as well as analyze systems that cannot be approached by existing methods. We provide a software package that is available online for free access.
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