{"as_of":"2026-08-08T08:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25a79fe5a7277122a893413a3378df2f464bab0698f1eaf5a6db16faee38fa16","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:16:02.540786Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.00903/citation-record","integrity":"/paper/2509.00903/integrity","json":"/paper/2509.00903/citation-record.json","paper":"/paper/2509.00903"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.939652Z","title":"Physics and Fractal Structures","venue":null,"work_id":"955b982c-3b96-43c0-a208-f8aa08a84569","year":1996},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.420376Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:18cf6c9ffd71bbb2e8011455ffe13366dc7961ac5cd3e3a8f9612032f242e7fd","observation_id":"5aaa3718-b2d1-46cc-872f-2f6631da66ee","resolution":{"observed_at":"2026-08-05T13:16:02.942374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.932391Z","title":"The Fractal Geometry of Nature , volume 1","venue":null,"work_id":"2069354e-f68a-4246-ba6a-55796b6c4d4d","year":1982},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.423645Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:5e5aa0af9eb631ffbb229782dcd0c9d40854957fdd765bf298a33be6464fa6bf","observation_id":"900d84fc-2d10-4a0e-a594-6f4e139e6ac2","resolution":{"observed_at":"2026-08-05T13:16:02.935024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.924873Z","title":"Fractal-based methods in analysis","venue":null,"work_id":"344b962b-222b-430e-b41b-bc5169de0030","year":2011},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.426790Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:4908cbbafdcfb66ec885431a890b2d7c4dbbb7a6414c2d019974fb82dfa1c808","observation_id":"1626d382-4a25-4527-a246-8e6b258cac31","resolution":{"observed_at":"2026-08-05T13:16:02.927532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.917160Z","title":"Introduction to the Modern Theory of Dynamical Systems , volume 54 of Encyclopedia of Mathematics and its Applications","venue":null,"work_id":"c8048561-acb3-4383-afb3-de7fed00c69b","year":1995},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.429510Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:4424c58df047a8e3a4d6f539a91ee547bae943eca272c131a9471e353e05c161","observation_id":"ae9a2445-ef12-4eed-b109-a8bc5c3c206b","resolution":{"observed_at":"2026-08-05T13:16:02.919848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.909618Z","title":"A first course in chaotic dynamical systems: Theory and experiment","venue":null,"work_id":"fe1ad6c5-80bc-4f67-ab30-bee9f989e852","year":1993},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.432466Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:afe20564d00098f887436c34dd3c10c1fba276dd8486556743899e06c03d3130","observation_id":"434f83fc-7913-447d-96fd-4e18be8ea3ea","resolution":{"observed_at":"2026-08-05T13:16:02.912411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.902155Z","title":"Mandelbrot set and Julia sets of fractional order","venue":null,"work_id":"832b3c6f-1e8a-4316-aaa8-2f7e6f16bdb2","year":2023},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.435431Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:8027aa37bc6891fc2f0c6d03da5b34b4f66c185699a8e094063b715cf4b2cd1b","observation_id":"9ebc8221-9cc4-4a20-8f98-247a55c9b83a","resolution":{"observed_at":"2026-08-05T13:16:02.904800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.894441Z","title":"Zalcman functions and similarity be- tween the Mandelbrot set, Julia sets, and the tricorn","venue":null,"work_id":"36ab767a-e0b6-49e4-b840-04019e7d2986","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.438766Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:eff16bad27d02323cdde68a4ffcd0207e32171711e83888aaf723a695e455b70","observation_id":"08c380de-d345-4224-a530-ec97e80d3e34","resolution":{"observed_at":"2026-08-05T13:16:02.897251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.886683Z","title":"Similarity between the Mandelbrot set and Julia sets","venue":null,"work_id":"f8867aaf-81ab-40d6-bf9a-49234ddf4fc3","year":1990},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.441336Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:1c0ed2de97e026cfa452ff9b0c5d003c23bb1c27f0e564ecaceea67c90654619","observation_id":"cbfd4323-cdf0-4422-802f-a5a3b5b1c459","resolution":{"observed_at":"2026-08-05T13:16:02.889358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.878923Z","title":"Haus- dorff dimension of Julia sets in the logistic family","venue":null,"work_id":"2ed8ccea-4adc-4ed0-a1bc-713ddf18a9c5","year":2023},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.444050Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:698a58263f4afc7d1bada5edcb60811999748f439ebf82465d7c8c062892f306","observation_id":"8045109b-fa73-4cbf-a1e0-e9d0c87fcb52","resolution":{"observed_at":"2026-08-05T13:16:02.881797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.871123Z","title":"On the directional derivative of the Hausdorff dimension of quadratic polynomial Julia sets at-2","venue":null,"work_id":"6d8b35b8-140e-4449-a8be-3218d7402fc3","year":2023},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.446592Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:03570d9d5acb70839358ed04f04aaf060a8be3e45766449c073f45be0b8d7347","observation_id":"8170af59-9c62-451a-825d-d976f7be2bfc","resolution":{"observed_at":"2026-08-05T13:16:02.874102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.862913Z","title":"Automatic prediction of tumour malignancy in breast cancer with fractal di- mension","venue":null,"work_id":"4bcd782d-2acb-4982-86d2-2c784e5fd3d0","year":2016},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.448940Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:0ca50c432b4caf6b3a5978496080ce953afddfe671c82c890a3dda2e1f4eabe0","observation_id":"699b9e37-6bf7-418d-92b4-c24d2b5905f3","resolution":{"observed_at":"2026-08-05T13:16:02.865633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.854658Z","title":"A machine learning approach to auto- matic detection of irregularity in skin lesion border using dermoscopic images","venue":null,"work_id":"e94c009c-be11-48a1-aa49-e8efd1089914","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.451313Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:cacf977c4447b05d97db2b8adcc3fc82f89c27c99c387a29086f119e6a185e42","observation_id":"c2fe9c4f-c6fd-4373-9291-03ca37de459e","resolution":{"observed_at":"2026-08-05T13:16:02.857878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.847009Z","title":"Ma- chine learning and fractal theory models for landslide sus- ceptibility mapping: Case study from the Jinsha River Basin","venue":null,"work_id":"39c8fe3f-bf75-4cd1-a3d9-a49c67b1f095","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.453850Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:f68342c3c575e294bbc246f187b3431989f1b66d26d77bef70d6562048613e84","observation_id":"96d7e224-4766-43f3-af70-bfc790d15281","resolution":{"observed_at":"2026-08-05T13:16:02.849795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.839532Z","title":"Predicting the future of discrete sequences from fractal representations of the past","venue":null,"work_id":"c76e2e6c-bd87-4717-b6dc-11407a39b102","year":2001},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.456216Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:456a96bea48cb30abaac8ebed62c6633e596e536881d03e82afefa4a4d5405a8","observation_id":"0ebcd4fc-9e8c-4603-9797-a9d970f2a195","resolution":{"observed_at":"2026-08-05T13:16:02.842201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.831906Z","title":"Schema genetic algorithm for fractal image compres- sion","venue":null,"work_id":"0914f531-e5c0-477c-a26a-cbd38c7db425","year":2007},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.458539Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:2e880ba1bdf9e80eae0d5f0fa28d8737fa50321bac174da3a2ba8668e07a867b","observation_id":"bedf52d6-a413-444a-bf23-3c8949b10b5c","resolution":{"observed_at":"2026-08-05T13:16:02.834598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.823668Z","title":"Pre-training without natural images","venue":null,"work_id":"3287a68f-2c4e-4f0c-898a-a7f9e9cb4bdf","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.460810Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:82d335390e5bbe85156b50a98d07f9dcd9cf4576db822bafc2ec28a2f95767ce","observation_id":"46ce99c4-d848-4c0a-814f-1d6ea4469567","resolution":{"observed_at":"2026-08-05T13:16:02.826920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.816047Z","title":"Improving fractal pre-training","venue":null,"work_id":"5317c453-a381-4637-bc90-e4340c935664","year":2022},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.463550Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:efad5b34712464063714d856e4613ac1c719e7b0c1d8053860e574b290b52bfd","observation_id":"d9b4fb0e-0ff4-4109-a988-514ab4a4b5d3","resolution":{"observed_at":"2026-08-05T13:16:02.818932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.808422Z","title":"Learning fractals by gradient descent","venue":null,"work_id":"d990b03c-ce6d-4fee-a239-25fcc6303ec4","year":2023},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.465971Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:a7e3aa8090aad50f060d8ce850e76f709e88fc260e0fdf800424ee597b31a699","observation_id":"35af878a-e2e1-41bd-a7c6-1a9138eda838","resolution":{"observed_at":"2026-08-05T13:16:02.811248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.800809Z","title":"Machine learning and fractal geometry","venue":null,"work_id":"1608df1f-36ef-49c6-9b74-cdc75660150f","year":2010},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.468417Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:5d0a3f792cf1d58134885d8628bc31bdd37c6067d22e84d4736f3ad094318434","observation_id":"c52975a6-92c9-4cab-b4e4-36bc1577f32a","resolution":{"observed_at":"2026-08-05T13:16:02.803543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.792692Z","title":"What do deep neural networks under- stand of fractals?, 2017","venue":null,"work_id":"cd40a9fb-4b43-4f00-8b4e-1498be097bd6","year":2017},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.470895Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:3e59242ff8c08290a94738bf352199f1e7a0fe35477642fa96100b0f1308b545","observation_id":"bac45c01-59e4-45e9-967a-b280d8c5506a","resolution":{"observed_at":"2026-08-05T13:16:02.795690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.785009Z","title":"On the quadratic mapping z → z2 − µ for complex µ and z: the fractal structure of its M set, and scaling","venue":null,"work_id":"9062f817-fb81-4440-8305-6dba444bf99a","year":1983},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.473602Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:57387f5f1d0448dd134e0301e5c051353a1a649325ec23548f22cdaf5c858f16","observation_id":"54ad643f-6c2b-464f-ba7b-16e420e1ded2","resolution":{"observed_at":"2026-08-05T13:16:02.787778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.777550Z","title":"Disconnected Julia sets","venue":null,"work_id":"e435504a-71ab-47d1-b267-c58d9ad04448","year":1986},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.476077Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:5a4cac767f6b2588b632415a810dd60aeec7890629b77481627eaac513cb92ea","observation_id":"1b9b36ce-b663-4621-9b2e-ab1b3df87331","resolution":{"observed_at":"2026-08-05T13:16:02.780114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.769671Z","title":"Clinical time series pre - diction: Toward a hierarchical dynamical system frame- work","venue":null,"work_id":"30cf3a55-f36f-4958-99dd-0e4db0752cd9","year":2015},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.478488Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:3a5723a1bdf3868305725c6b98df11533196bc96c5161f1d5f8697cce9e5827b","observation_id":"5b3eed04-aed1-4fca-bf95-31d098518026","resolution":{"observed_at":"2026-08-05T13:16:02.772495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.761977Z","title":"An intelligent syste m for financial time series prediction combining dynamical systems theory, fractal theory, and statistical methods","venue":null,"work_id":"77febb9f-1c9b-4a9a-a293-2cf5d37cff46","year":1995},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.480854Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:546bf90160e0979468231b45a465e278d92ccdb11ca846847325e67b17633497","observation_id":"6cb07779-af03-458b-9db1-e4f3a2bfe3a7","resolution":{"observed_at":"2026-08-05T13:16:02.764793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.754091Z","title":"Classification of chaotic time series with deep learning","venue":null,"work_id":"a83441a7-40da-4ad8-9441-6e8ace9dce98","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.483199Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:69d761a3d22cf192c82eca6740c5eaf56a8b464f5a895d5807fcbd893db02222","observation_id":"43498efc-05fa-446b-8cef-65d7222eb2cb","resolution":{"observed_at":"2026-08-05T13:16:02.757049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.746146Z","title":"Deep learning for time series classification: a review","venue":null,"work_id":"ded1972d-06b4-419d-8040-5b553c9b1c98","year":2019},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.485903Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:839883fcd671a6490771122d920d080d2bdfee3592edeb64a4a2fca67bf99fc2","observation_id":"16a2e115-53f5-4c76-bba4-dd62ca5fd463","resolution":{"observed_at":"2026-08-05T13:16:02.748844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.738382Z","title":"Inception- time: Finding alexnet for time series classification","venue":null,"work_id":"612a3fa1-3c94-4c1b-b28c-ccd1e13c34a7","year":1936},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.488392Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:ce396e066c52677d9ab4cf4862252ede7b3e35f47201c1005ab9aa7a611efcd1","observation_id":"b4621607-9319-4d3c-99b7-fd7a29566063","resolution":{"observed_at":"2026-08-05T13:16:02.741225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.730593Z","title":null,"venue":null,"work_id":"aa93d6de-f8af-4d04-ad3f-c904fd053e84","year":2023},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.490818Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:0098546017a2c92fd98a38f0d082061fa2866d379afb8051b78402197eceb91a","observation_id":"3f1babe9-5c99-481e-885d-997fdde06ce9","resolution":{"observed_at":"2026-08-05T13:16:02.733117Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.722905Z","title":"Solving high-dimensional partial differential equations using dee p learning","venue":null,"work_id":"b2a87b6b-bba2-42e6-a785-682bd0c9d556","year":2018},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.493387Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:7795e4f568f908075f6cd164a8c7e59a6d6833c8d93e2e42e6d0d12dcce75c42","observation_id":"fdcc90d7-5966-49cd-8299-898d5107932e","resolution":{"observed_at":"2026-08-05T13:16:02.725783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.715344Z","title":"Solving differential equations using deep neural networks","venue":null,"work_id":"d15c16a7-4137-4ca1-83ea-be91f3e4418c","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.495857Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:ecc2d5de20c4235558761af35391ca08647a27f9a03ccf45311a6a2278087147","observation_id":"41974756-86d9-4ed0-9f1f-8740c8e46b9a","resolution":{"observed_at":"2026-08-05T13:16:02.718228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.707374Z","title":"A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic par- tial differential equations","venue":null,"work_id":"1227f564-03c1-432c-9b16-5967e5167a23","year":2024},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.498323Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:2ace7f284d0afa69a179ab24a765abdc5b6bff7d94d318f0d15d271840afb3db","observation_id":"7be3adcc-f256-4d86-b2d1-32ea6386a527","resolution":{"observed_at":"2026-08-05T13:16:02.710209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.698212Z","title":"Deep hidden physics models: Deep learn- ing of nonlinear partial differential equations","venue":null,"work_id":"581ba111-1f75-4e71-88aa-47a816c00dd0","year":2018},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.500652Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:e91b37fec94f0469752352e7e86600ea7f3d5f2c9132307ebff05abc037cdb9f","observation_id":"abdf36da-9b38-4524-b1c5-5297ad505451","resolution":{"observed_at":"2026-08-05T13:16:02.701877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.690146Z","title":"Hidden physics models: Machine learning of nonlinear partial dif- ferential equations","venue":null,"work_id":"f699d6b4-41bd-4ebc-9b04-f6bc84fb7e80","year":2018},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.502964Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:2cb16408c466e686ccaad1715442dca21e3f1db16f2adc09e6f5a8850e5adfd9","observation_id":"f6e03cd3-607f-4d1a-bf28-f5d21651742f","resolution":{"observed_at":"2026-08-05T13:16:02.693025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.682396Z","title":"Physics-informed neural networks: A deep learn- ing framework for solving forward and inverse problems involving nonlinear partial differential equations","venue":null,"work_id":"75024433-2814-456c-8f41-a3d4f6c61586","year":2019},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.505455Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:24dc25ccd6d39063a62a3acd48c1dec6440b6aaaa7b458d470f5b59ce4bb7d75","observation_id":"28bec9b7-6869-42aa-8d90-f08a62db9249","resolution":{"observed_at":"2026-08-05T13:16:02.685344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.674608Z","title":"Neural networks for bifurcation and linear stability analysis of steady states in partial differential equations","venue":null,"work_id":"709de8f4-6af5-430b-82b8-384101f70a14","year":2024},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.508037Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:c05434c699a0a54d9f00acea405f2f33fa1ca09876ce8c6d0c9f0018e54ba1fd","observation_id":"20ea30d5-b774-4166-878c-f2f5f1c4349a","resolution":{"observed_at":"2026-08-05T13:16:02.677521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.666291Z","title":"Neural networks for bifurcation and linear sta- bility analysis of steady states in partial differential equ a- tions","venue":null,"work_id":"08c98a82-f354-42ad-86c9-85aa4e3ff8a1","year":2024},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.510443Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:59ba6c7a9d78642f4a0d573458d1613955888db643c7c83c9716aafcd3d1d056","observation_id":"8a4c631b-9bd0-4e36-ac2a-a36574d40bf2","resolution":{"observed_at":"2026-08-05T13:16:02.669408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.658268Z","title":"Neural networks for high-dimensional solutions and snaking bifurcations in nonlinear lattices","venue":null,"work_id":"42d48a83-c9fd-4af4-be79-0bdacbeb5a66","year":2025},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.512993Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:48d10135ce553b1df665461997106a9820499942c16b7bdfeb709ddb2a9850e2","observation_id":"6e590c72-ba94-4a2e-843e-c6c0313bd845","resolution":{"observed_at":"2026-08-05T13:16:02.661075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.650376Z","title":"Un- derstanding and mitigating gradient flow pathologies in physics-informed neural networks","venue":null,"work_id":"6e36c44b-f87f-4b6b-b06a-391a6df1a352","year":2021},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.515430Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:88e2442a34145e75ad059ec501f9d55031457769bebe89b7f7ccc2a68840c0b2","observation_id":"1240cd16-9b0d-4ebc-b907-f749661f9982","resolution":{"observed_at":"2026-08-05T13:16:02.653109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.641611Z","title":"Extended physics-informed neural networks (XPINNs): A general- ized space-time domain decomposition based deep learn- ing framework for nonlinear partial differential equations","venue":null,"work_id":"bd585da1-0c92-45bf-b2f3-e15c8bef64c5","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.517823Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:966cf86f36b7d4339914552134b1b6c67e4cd6707188f82481ff0456668ba73e","observation_id":"e4614f56-e8e9-4c85-8c7f-ecbb0e120ccf","resolution":{"observed_at":"2026-08-05T13:16:02.644522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.633191Z","title":"A composite neural network that learns from multi-fidelity data: Appli- cation to function approximation and inverse PDE prob- lems","venue":null,"work_id":"8b1a7b9c-9fe4-4258-b65d-a9eaec415ca9","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.520435Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:e8057d8c3be8f56b6ccd52dbe9213f26ef6b9da2c7878976cce2852f686e8c9f","observation_id":"bb7ef3f6-fd42-40ea-96dc-d9da16819f12","resolution":{"observed_at":"2026-08-05T13:16:02.636233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.624103Z","title":"Transfer learning on physics-informed neural networks for tracking the hemo- dynamics in the evolving false lumen of dissected aorta","venue":null,"work_id":"44ac5ceb-ae75-4d09-a3dd-1f74f4bfcfb4","year":2024},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.522885Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:4ff9ec7821ef55bd40ae06cd0eb941426f4f36ad261bbe3687dbcf7f6096fc19","observation_id":"6021d8a7-97c3-4f55-95c6-6f9ca4e170a9","resolution":{"observed_at":"2026-08-05T13:16:02.627549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.615652Z","title":"Physics guided neural networks for modelling of non- linear dynamics","venue":null,"work_id":"88059a75-a9a0-4688-aa83-bd761c5df943","year":2022},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.525434Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:656f6f0a863a7693b61878cf62f089293962db267ec36add68acbbd26e1f9c4a","observation_id":"84e58a8e-7e0a-40c2-950d-8268e0beef28","resolution":{"observed_at":"2026-08-05T13:16:02.618441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15771","last_updated":"2025-03-18T18:24:12Z","snapshot_observed_at":"2026-08-02T11:46:28.334353Z","submitted_at":"2024-09-24T05:56:58Z","title":"Zero-shot forecasting of chaotic systems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15771","snapshot_observed_at":"2026-08-05T13:16:02.527907Z","title":"Zero-shot forecast- ing of chaotic systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.527907Z"},"links":{"cited_paper":"/paper/2409.15771","citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:0baf3371ac92b985706401d3011e19c955f399c301f5c83156c75372c8a6013c","observation_id":"2c1dde14-4551-4790-ae0b-2d6dd40e841d","resolution":{"observed_at":"2026-08-05T13:16:02.527907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.607363Z","title":"Discovering governing equations from data by sparse iden- tification of nonlinear dynamical systems","venue":null,"work_id":"0e19f050-05fc-4854-b412-c9c5cdb9252c","year":2016},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.530639Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:a50c9e6948855452de471f685a5ee87f1486d5d95502fa0acbc5d00c4074cf4f","observation_id":"24a10306-2bf5-4178-8899-ab83400d1774","resolution":{"observed_at":"2026-08-05T13:16:02.610273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.598770Z","title":"Data-driven discovery of coordinates and governing equations","venue":null,"work_id":"5fef3737-9343-4117-a33b-4932e2e91134","year":2019},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.533240Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:41b9ad028790a57b5f4a04d5d9e9422f22fe2566da1b6806cbe365c614b904d8","observation_id":"21c81cb4-f032-481a-bf38-fd2eb226711e","resolution":{"observed_at":"2026-08-05T13:16:02.601765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05266","last_updated":"2023-01-29T08:19:43Z","snapshot_observed_at":"2026-08-08T00:01:12.168337Z","submitted_at":"2021-10-11T13:39:41Z","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.05266","snapshot_observed_at":"2026-08-05T13:16:02.535689Z","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.535689Z"},"links":{"cited_paper":"/paper/2110.05266","citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:948b66d4625d07cce7d192eca935381ff4bcc32e33269cb7c900e0935f17d39d","observation_id":"4ae16cca-d8fc-4c37-b1eb-4bcc7dbf3744","resolution":{"observed_at":"2026-08-05T13:16:02.535689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.590820Z","title":"Tjahjono","venue":null,"work_id":"6c5efd89-ff39-4b91-bd55-cad13d00e1cc","year":2025},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.538490Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:93987b513ff8ce68baa6b4081ceef3f0278440e6ffa8e59ec1ffc60e07533351","observation_id":"5ace688c-cddb-473c-9d76-3527c3cf7e4b","resolution":{"observed_at":"2026-08-05T13:16:02.593392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:16:02.580341Z","title":"Discovering physical concepts with neural networks","venue":null,"work_id":"cf10302a-e908-4b44-b246-173cad06e6b0","year":2020},"citing_paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T13:16:02.540786Z"},"links":{"citing_paper":"/paper/2509.00903"},"observation_digest":"sha256:491df6f0b48e04867be5038567a1e2d766b2a56847fa645027cfddf5a478dc62","observation_id":"eea37404-a433-485a-9636-31bb4e9094b8","resolution":{"observed_at":"2026-08-05T13:16:02.584959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.00903","last_updated":"2025-08-31T15:34:02Z","latest_version":1,"primary_category":"nlin.CD","snapshot_observed_at":"2026-08-08T00:01:37.212456Z","submitted_at":"2025-08-31T15:34:02Z","title":"Learning with Mandelbrot and Julia"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":45},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2509.00903."}