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Simulacra and Simulation delineates the sign-order into four stages: [8]. The first stage is a faithful image/copy, where people believe, and may even be correct to believe, that a sign is a "reflection of a profound reality" (pg 6), this is a good appearance, in what Baudrillard called "the sacramental order".
Then, in the same way, it picks a triplet that starts with the second and third words in the generated text, and that gives a fourth word. It adds the fourth word, then repeats with the third and fourth words, and so on. This algorithm is called a third-order Markov chain (because it uses sequences of three words). [1]
A simulacrum (pl.: simulacra or simulacrums, from Latin simulacrum, meaning "likeness, semblance") is a representation or imitation of a person or thing. [1] The word was first recorded in the English language in the late 16th century, used to describe a representation, such as a statue or a painting, especially of a god .
The diagram opposite shows a 3rd order solution to G A Sod's shock tube problem (Sod, 1978) using the above high resolution Kurganov and Tadmor Central Scheme (KT) but with parabolic reconstruction and van Albada limiter. This again illustrates the effectiveness of the MUSCL approach to solving the Euler equations.
Bispectra fall in the category of higher-order spectra, or polyspectra and provide supplementary information to the power spectrum. The third order polyspectrum (bispectrum) is the easiest to compute, and hence the most popular. A statistic defined analogously is the bispectral coherency or bicoherence.
For arbitrary stencil points and any derivative of order < up to one less than the number of stencil points, the finite difference coefficients can be obtained by solving the linear equations [6] ( s 1 0 ⋯ s N 0 ⋮ ⋱ ⋮ s 1 N − 1 ⋯ s N N − 1 ) ( a 1 ⋮ a N ) = d !
In simulacra is a Latin phrase meaning "within likenesses." The phrase is used similarly to in vivo or ex vivo to denote the context of an experiment. In this case, the phrase denotes that the experiment is not conducted in the actual subject, but rather a model of such.
For a 3rd-order tensor , where is either or , Tucker Decomposition can be denoted as follows, = () where is the core tensor, a 3rd-order tensor that contains the 1-mode, 2-mode and 3-mode singular values of , which are defined as the Frobenius norm of the 1-mode, 2-mode and 3-mode slices of tensor respectively.