AI Article Synopsis
- A model-free approach is introduced for identifying change points in intermittent stochastic processes by analyzing the local convex hull (LCH) of trajectory points.
- The method utilizes geometric properties of the LCH, such as diameter and volume, to distinguish between different phases of motion.
- The technique has been tested on various models, including different types of Brownian motion, and has potential applications in fields like intracellular transport and animal behavior.
Article Abstract
We propose a model-free method to detect change points between distinct phases in a single random trajectory of an intermittent stochastic process. The local convex hull (LCH) is constructed for each trajectory point, while its geometric properties (e.g., the diameter or the volume) are used as discriminators between phases. The efficiency of the LCH method is validated for six models of intermittent motion, including Brownian motion with different diffusivities or drifts, fractional Brownian motion with different Hurst exponents, and surface-mediated diffusion. We discuss potential applications of the method for detection of active and passive phases in the intracellular transport, temporal trapping or binding of diffusing molecules, alternating bulk and surface diffusion, run and tumble (or search) phases in the motion of bacteria and foraging animals, and instantaneous firing rates in neurons.
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| http://dx.doi.org/10.1103/PhysRevE.96.022144 | DOI Listing |
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