通过聚合物压实提取数据序列中的长程关联
Long-range Correlations in a Data Sequence Extracted Via Polymeric Compaction
AI总结：
提出通过聚合物压实去除数据序列中的淬火随机性，利用链塌缩成球形的偏差揭示长程信息关联，并引入超越算法信息的新系数。
摘要：
提出了一种数值方法，用于从数字数据序列中去除淬火随机性。引入了一条由珠子组成的聚合物链，该链同时具有硬核相互作用以及与数据序列相关联的适当能量。假设淬火随机性会使链塌缩成球形小球。长程信息关联随后会体现在与球形形状的偏差中。在球谐函数展开中得到的系数超越了算法信息或柯尔莫哥洛夫复杂度的常规概念。
英文摘要：
A numerical method is proposed to remove the quenched randomness from a data sequence of numbers. A polymer chain of beads is introduced with both a hard core interaction and an appropriate energy associated with the data sequence. The quenched randomness is hypothesized to collapse the chain to a spherical globule. Long-range informational correlations then show up in deviations from the spherical shape. The resulting coefficients within an expansion in terms of spherical harmonics go beyond the usual concept of algorithmic information or Kolmogorov complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
