Long-range Correlations in a Data Sequence Extracted Via Polymeric Compaction
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2019
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| _version_ | 1866916471820517376 |
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| author | Odijk, Theo |
| author_facet | Odijk, Theo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1904_09693 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Long-range Correlations in a Data Sequence Extracted Via Polymeric Compaction Odijk, Theo Soft Condensed Matter Statistical Mechanics 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. |
| title | Long-range Correlations in a Data Sequence Extracted Via Polymeric Compaction |
| topic | Soft Condensed Matter Statistical Mechanics |
| url | https://arxiv.org/abs/1904.09693 |