{
  "annotation_id": "500f19d5-3395-4dff-a30d-b580aff673b0",
  "date_created": "2026-03-02T18:01:07.970000Z",
  "date_modified": "2026-03-02T18:01:07.970000Z",
  "file_hash": "92526053cf9d8f0681189c3abb8324b7e99720fc516767c3c20564355a85746c",
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  "record": {
    "abstract": "Physics concepts have often been borrowed and independently developed by\nother fields of science. In this perspective a significant example is that of\nentropy in Information Theory. The aim of this paper is to provide a short and\npedagogical introduction to the use of data compression techniques for the\nestimate of entropy and other relevant quantities in Information Theory and\nAlgorithmic Information Theory. We consider in particular the LZ77 algorithm as\ncase study and discuss how a zipper can be used for information extraction.",
    "arxiv_id": "physics/0605031",
    "authors": [
      "Andrea Baronchelli",
      "Emanuele Caglioti",
      "Vittorio Loreto"
    ],
    "categories": [
      "physics.ed-ph",
      "physics.comp-ph"
    ],
    "doi": "10.1088/0143-0807/26/5/S08",
    "journal_ref": "Eur. J. Phys. 26 (2005) S69-S77",
    "title": "Measuring complexity with zippers",
    "url": "https://arxiv.org/abs/physics/0605031"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8d0f83a8-7cb9-4dc8-8451-6ae6dbd686db",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}