18,415 research outputs found
Cycle symmetry, limit theorems, and fluctuation theorems for diffusion processes on the circle
Cyclic structure and dynamics are of great interest in both the fields of
stochastic processes and nonequilibrium statistical physics. In this paper, we
find a new symmetry of the Brownian motion named as the quasi-time-reversal
invariance. It turns out that such an invariance of the Brownian motion is the
key to prove the cycle symmetry for diffusion processes on the circle, which
says that the distributions of the forming times of the forward and backward
cycles, given that the corresponding cycle is formed earlier than the other,
are exactly the same. With the aid of the cycle symmetry, we prove the strong
law of large numbers, functional central limit theorem, and large deviation
principle for the sample circulations and net circulations of diffusion
processes on the circle. The cycle symmetry is further applied to obtain
various types of fluctuation theorems for the sample circulations, net
circulation, and entropy production rate.Comment: 28 page
Reveal flocking of birds flying in fog by machine learning
We study the first-order flocking transition of birds flying in
low-visibility conditions by employing three different representative types of
neural network (NN) based machine learning architectures that are trained via
either an unsupervised learning approach called "learning by confusion" or a
widely used supervised learning approach. We find that after the training via
either the unsupervised learning approach or the supervised learning one, all
of these three different representative types of NNs, namely, the
fully-connected NN, the convolutional NN, and the residual NN, are able to
successfully identify the first-order flocking transition point of this
nonequilibrium many-body system. This indicates that NN based machine learning
can be employed as a promising generic tool to investigate rich physics in
scenarios associated to first-order phase transitions and nonequilibrium
many-body systems.Comment: 7 pages, 3 figure
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