Optimizing parallel particle tracking in Brownian motion using machine learning

In this paper, we present a generic, scalable and adaptive load balancing parallel Lagrangian particle tracking approach in Wiener type processes such as Brownian motion. The approach is particularly suitable in problems involving particles with highly variable computation time, like deposition on boundaries that may include decay, when particle lifetime obeys exponential distribution. At first glance, Lagranginan tracking is highly suitable for a distributed programming model due to the independence of motion of separate particles. However, the commonly employed Decomposition Per Particle (DPP) method, where each process is in charge of a certain number of particles, actually displays poor parallel efficiency due to the high particle lifetime variability when dealing with a wide set of deposition problems that optionally include decay. The proposed method removes DPP defects and brings a novel approach to discrete particle tracking. The algorithm introduces master/slave model dubbed Partial Trajectory Decomposition (PTD), in which a certain number of processes produce partial trajectories and put them into the shared queue, while the remaining processes simulate actual particle motion using previously generated partial trajectories. Our approach also introduces meta-heuristics for determining the optimal values of partial trajectory length, chunk size and the number of processes acting as producers/consumers, for the given total number of participating processes (Optimized Partial Trajectory Decomposition, OPTD). The optimization process employs a surrogate model to estimate the simulation time. The surrogate is based on historical data and uses a coupled machine learning model, consisting of classification and regression phases. OPTD was implemented in C, using standard MPI for message passing and benchmarked on a model of 220 Rn progeny in the diffusion chamber, where particle motion is characterized by an exponential lifetime distribution and Maxwell velocity distribution. The speedup improvement of OPTD is approximatelly 320% over standard DPP, reaching almost ideal speedup on up to 256 CPUs.

Automated home cage training of mice in a hold-still center-out reach task

An obstacle to understanding neural mechanisms of movement is the complex, distributed nature of the mammalian motor system. Here we present a novel behavioral paradigm for high-throughput dissection of neural circuits underlying mouse forelimb control. Custom touch-sensing joysticks were used to quantify mouse forelimb trajectories with micron-millisecond spatiotemporal resolution. Joysticks were integrated into computer-controlled, rack-mountable home cages, enabling batches of mice to be trained in parallel. Closed loop behavioral analysis enabled online control of reward delivery for automated training. We used this system to show that mice can learn, with no human handling, a direction-specific hold-still center-out reach task in which a mouse first held its right forepaw still before reaching out to learned spatial targets. Stabilogram diffusion analysis of submillimeter-scale micromovements produced during the hold demonstrate that an active control process, akin to upright balance, was implemented to maintain forepaw stability. Trajectory decomposition methods, previously used in primates, were used to segment hundreds of thousands of forelimb trajectories into millions of constituent kinematic primitives. This system enables rapid dissection of neural circuits for controlling motion primitives from which forelimb sequences are built. NEW & NOTEWORTHY A novel joystick design resolves mouse forelimb kinematics with micron-millisecond precision. Home cage training is used to train mice in a hold-still center-out reach task. Analytical methods, previously used in primates, are used to decompose mouse forelimb trajectories into kinematic primitives.

Preview-Based Output Tracking of Nonminimum-Phase Linear Systems via Trajectory Decomposition: Nanomanipulation Example

In this article, a trajectory-decomposition-based approach to output tracking with preview for nonminimum-phase systems is proposed. When there exists a finite (in time) preview of the future desired trajectory, precision output tracking of nonminimum-phase systems can be achieved by using the preview-based stable-inversion technique. The preview-based stable-inversion technique has been successfully implemented in various high-speed positioning applications. The performance of this approach, however, can become sensitive to system dynamics uncertainty. Moreover, the computation involved in the implementation of this approach can be demanding. In the proposed approach, such preview-based inversion related challenges are addressed by integrating the notion of signal decomposition and the iterative learning control technique together. Particularly, a library of desired output elements and their corresponding control input elements is constructed, and the ILC techniques such as the recently-developed model-less inversion-based iterative control (MIIC) are used to obtain the control input elements that achieve precision output tracking of the corresponding desired output elements. Then the previewed future desired trajectory is decomposed as a summation of desired output elements, and the control input is synthesized by using the input elements selected for the corresponding output elements with chosen pre-actuation time. Furthermore, the required pre-actuation time is quantified based on the stable-inversion theory. The proposed approach is illustrated through simulation study of a nanomanipulation application using a nonminimum-phase piezo actuator model.