Random Seeds

CLASS FINITE SEED RANDOM SEED DIFFERENCE PATTERN PREDICTABILITY... 

Fig. 8.24 Evolution of a 35 x 35 lattice whose sites are initially randomly seeded with O = 1 with probability p = 1/2. The development proceeds according to T value and OT topology rules defined by code C = (84,36864,2048). The constraints are = 0, A = 10]. The appearance of localized substructures is evidence of a geometrical self-organization.

Build up structures from randomly seeded atoms and randomly selected torsions... 

In Fig. 8 of Ref. [8], different nearly-degenerate textured states evolved under relaxational dynamics are shown. In scores of such relaxations, we have found a uniform state only once, for a particular initial-state random seed. 

Set Bath relaxation time (suggested range step size to the default of 0.1 ps) and assign any number between — 32,768 and 32,768 for Random seed as the starting point for the random number generator used for the simulations. Friction coefficient (any positive value) is needed only for the Langevin dynamics. 

Table 4.1 Summery of all the DFTB trajectories of n, n) SWCNTs of different n and RANDOM SEEDs at different temperatures...

The average values of ti and T2 over the 3 RANDOM SEED trajectories is shown in Fig. 4.3. Minimal time of both-sides self-capping was found for n = 6 at 2500 K and 3000 K, and n = 5 at 3500 K. The value of T2 is roughly the twice of ti but in some case, (6, 6), (8, 8) and (9, 9) at 3500 K for instance, T2 less than the twice of Ti. The reason for such strange step-wise self-capping sequence (first one end caps, then the other), is likely to And in the flying-ice-cube problem of the velocity scaling thermostat [17]. 

Create data template for rm(.Random.seed) set.seed <- 555 nSubj <- 200... 

Replace random seed placeholder in simulation file "ZZZZZ"... 

Starting with a dummy problem, our test statistic for the original data set is calculated and written to a file. This problem includes a simulation record so that the subsequent problem can use a special random seed so that each of the simulated data sets will be different. 

Its only purpose is to allow one to have a sim record in the subsequent problem with a random seed equal to -1. 

In this PROBLEM, data is hrst simulated with a random seed continued from the previous problem and then estimated, with the estimation results saved in a model specihcation output hie as follows ... 

CALL RANDOM (2,R) /RANDOM SEED FROM UNIFORM (0,1)... 

NSGA-II parameters used in this study are maximum number of generations (up to 500), population size (100 chromosomes), probability of crossover (0.85), probability of mutation (0.05), distribution index for the simulated crossover operation (10), distribution index for the simulated mutation operation (20) and random seed (0.6). Except for the first and last parameter listed here, rest of the NSGA-II parameter values are taken from Tarafder et al. (2005). Values for maximum number of generations and random seed are obtained by trial and error. Our preliminary gene manipulation optimization runs show convergence within 500 generations for the random seed of 0.6. 

Fig. 18. Comparison between the experimental and theoretical optical signals from a glassy carbon surface randomly seeded with growing mercury nuclei, electrochemically deposited at an overpotential of 258 mV. The theoretical curve (broken line) is for a nuclear density of 7 x 105 cm 2, with a density spreading coefficient (see ref. 25) of 0.08.

A pseudorandom number generator is a function that takes a short random seed and outputs a longer bit sequence that appears random. To be cryptographically secure, the output of a pseudorandom number generator should be computationally indistinguishable from a random string. In particular, given a short prefix of the sequence, it should be computationally infeasible to predict the rest of the se-... 

Figure 31.6 shows the mean, standard deviation, and maximum for the peak force for n = 10 above, where n = 15 and the random seed is arbitrarily chosen as 7,638, n =20 with seed 418, n = 50 with seed 6,185, n = 100 with seed 331, and n = 200 with seeds 43 and 3,339. Note the range of values at n = 200 when two different seeds were selected. The engineer would then decide if he or she is satisfied with the results. Remember, in an actual computer simulation, the CPU time may be significant, and a sample of even n = 10 may be expensive. 

Table 1 Simulated diflnision coefficients and dielectric crmstant of water confined within a polyelectrolyte mixture that corresponds to an experimental PEM [160]. Three parallel simulations were performed for system (i), which has the slowest dynamics of water, and two for systems (ii) and (iii). In the parallel runs, only the random seeds in building the initial structures were different. Experimental data based on PSS/PDADMA PEMs...

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