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arXiv 2004.03502cond-mat.stat-mechcond-mat.dis-nncond-mat.softnlin.CGphysics.comp-ph
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可计算信息语言中的相关长度

Correlation lengths in the language of computable information

Stefano Martiniani, Yuval Lemberg, Paul M. Chaikin, Dov Levine

摘要:

可计算信息密度(CID),一个有损压缩数据文件的长度与未压缩文件的长度之比,是平衡和非平衡系统中秩序和相关性的度量。在这里,我们表明,相关长度可以通过抽取-稀疏的配置,通过采样数据在增加的时间间隔和重新计算CID。当采样间隔大于系统的相关长度时,数据变得不可压缩。相关长度和它的临界指数因此是可访问的,没有先验知识的序参量,甚至性质的排序。用这种方法测得的关联长度与由两点关联函数$g_{2}(r)$存在时的衰减计算的关联长度吻合得很好。但CID揭示了相关长度和它的缩放,即使当$g_{2}(r)$没有结构,因为我们证明了“掩盖”的数据与鲁丁-夏皮罗序列。

英文摘要:

Computable Information Density (CID), the ratio of the length of a losslessly compressed data file to that of the uncompressed file, is a measure of order and correlation in both equilibrium and nonequilibrium systems. Here we show that correlation lengths can be obtained by decimation - thinning a configuration by sampling data at increasing intervals and recalculating the CID. When the sampling interval is larger than the system's correlation length, the data becomes incompressible. The correlation length and its critical exponents are thus accessible with no a-priori knowledge of an order parameter or even the nature of the ordering. The correlation length measured in this way agrees well with that computed from the decay of two-point correlation functions $g_{2}(r)$ when they exist. But the CID reveals the correlation length and its scaling even when $g_{2}(r)$ has no structure, as we demonstrate by "cloaking" the data with a Rudin-Shapiro sequence.

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