Diffusion and Memory Effect in a Stochastic Processes and the Correspondence to an Information Propagation in a Social System

A generalized Langevin equation is suggested to describe a diffusion particle system with memory. The equation can be transformed into the Fokker-Planck equation by using the Kramers-Moyal expansion. The solution of Fokker-Planck equation can describe not only the diffusion of particles but also that of opinion particles based on the similarities between the two. We find that the memory can restrain some non-equilibrium phenomena of velocity distribution in the system, without memory, induced by correlation between the noise and space[1]. However, the memory can enhance the effective collision among particles as shown by the variation of diffusion coefficients, and changes the diffusion mode between the dissipative and pumping region by comparing with that in the aforementioned system without memory. As the discussions in this physical system is paralleled to a social system, the random diffusion of social ideology, such as the information propagation, can be suppressed by the correlation between the noise and space.

A New Version Non-Zero Diffusion Particle Flow

Molecule stream (PF) is a technique initially proposed for single objective following, and utilized as of late to address the weight decline issue of the consecutive Monte Carlo likelihood speculation thickness (SMC-PHD) channel for various media (AV) multi-speaker following, where the molecule stream is determined by just the estimations close to the molecule, accepting that the objective is identified, as in an ongoing standard dependent on non-zero molecule stream for example the AV-NPF-SMC-PHD channel. This, be that as it may, can be tricky when impediment occurs and the blocked speaker may not be distinguished. To address this issue, we propose another technique where the marks of the particles are evaluated utilizing the probability work, and the molecule stream is determined as far as the chose particles with similar names. Subsequently, the particles related with identified speakers and undetected speakers are recognized dependent on the molecule names. With this novel strategy, named as AV-LPF-SMC-PHD, the speaker states can be evaluated as the weighted mean of the named particles, which is computationally more effective than utilizing a bunching technique as in the AV-NPF-SMC-PHD channel. The proposed calculation is contrasted efficiently and a few standard following techniques utilizing the AV16.3, AVDIAR and CLEAR datasets, and are appeared to offer improved following precision with a low computational expense.