{"as_of":"2026-08-22T12:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eba0eca01fa5a0dc311bb19ae9391622e0a497ceda907c49af8bbc7c51a08a05","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:37:30.109235Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2507.18677/citation-record","integrity":"/paper/2507.18677/integrity","json":"/paper/2507.18677/citation-record.json","paper":"/paper/2507.18677"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-06T14:37:29.695068Z","title":"Layer normalization","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.695068Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:a2c63b884a1649e7328a8df3b02cc8d3635ea3e90131e1d5da0296006be822be","observation_id":"bfc97dbd-ac86-4c71-88f8-65b1b66bedbe","resolution":{"observed_at":"2026-08-06T14:37:29.695068Z","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":"2026-08-06T14:37:31.944875Z","title":"Finite element procedures","venue":null,"work_id":"04453281-04e0-44d0-b9b4-e0becbfb3ec7","year":1996},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.706300Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:53c040ddfa7b1b191fcb6bb1b710abb61430fbe7a487eaaec6accc7c45744ab2","observation_id":"8b95098b-e83d-41ca-8adb-ad179b73fc7f","resolution":{"observed_at":"2026-08-06T14:37:31.957574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.904753Z","title":"Large strain viscoelastic constitutive models","venue":null,"work_id":"1d62277c-d9be-419b-90a7-e0f659a6fb92","year":2001},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.714537Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:10c995f5215570163ed649fbfdb34249f17d0d90c3255baeb412c73532cb4441","observation_id":"69b16aa3-02fe-4bfb-b050-649d15124d33","resolution":{"observed_at":"2026-08-06T14:37:31.922577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14491","last_updated":"2022-01-31T07:20:20Z","snapshot_observed_at":"2026-07-06T11:14:04.454155Z","submitted_at":"2021-05-30T10:17:58Z","title":"How Attentive are Graph Attention Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14491","snapshot_observed_at":"2026-08-06T14:37:29.726441Z","title":"How attentive are graph attention networks? arXiv preprint arXiv:2105.14491","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.726441Z"},"links":{"cited_paper":"/paper/2105.14491","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:dcdfebb7631089a476bee36d7f12005480163128c01c2571c911739a9b953e07","observation_id":"8e765712-7c31-4158-9555-f93bc97aa36c","resolution":{"observed_at":"2026-08-06T14:37:29.726441Z","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":"2026-08-06T14:37:31.858844Z","title":"Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics","venue":null,"work_id":"ebe50df2-f0cb-4e68-aeaa-55cd34a7a09d","year":2020},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.740083Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:a419c2b0f0b46ad14feaf75f355e46a30e3772b104d242b6ffdd24b79c203f6e","observation_id":"1ca579bb-51af-4f2e-996a-abff88f9d6c1","resolution":{"observed_at":"2026-08-06T14:37:31.869644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.810909Z","title":"Emulation of cardiac mechanics using graph neural networks","venue":null,"work_id":"24e1a54b-9bb3-419c-ab50-8fdb78c4a33a","year":2022},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.747278Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:d20feffc676ee07734f3a23b6b153e1b303f44258583788dae087386c06350da","observation_id":"dba78a07-06d8-4c48-bb00-c2649de421dd","resolution":{"observed_at":"2026-08-06T14:37:31.821345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.766436Z","title":"Physics-informed graph neural network emulation of soft-tissue mechanics","venue":null,"work_id":"48c10c8b-9755-4ed0-9496-fc34b4f3c57f","year":2023},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.755616Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:7ca0395ad536da30adaaeb29d40a8e69ec467bd2598d105cfc9e6a410c876384","observation_id":"e146f9bf-c6d1-4247-8e8b-0ddbeb0cf74a","resolution":{"observed_at":"2026-08-06T14:37:31.781616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.729392Z","title":"Model- based assessment of elastic material parameters in rheumatic heart disease patients and healthy subjects","venue":null,"work_id":"63337acc-5773-43c0-96b1-61227ad8d685","year":2023},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.766238Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:a57c8ebdc1298a6d1a4242c0f7cb006d25d54b3f74d79c33c2040719f55f2794","observation_id":"20751b1c-f28b-444f-89e1-dffc52ded535","resolution":{"observed_at":"2026-08-06T14:37:31.738314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.691708Z","title":"Efficientestimationofpersonalizedbiventricular mechanicalfunctionemployinggradient-basedoptimization.Internationaljournalfornumericalmethodsinbiomedicalengineering34,e2982","venue":null,"work_id":"ad0ffc59-ff3b-4a56-a65e-53bb5159741f","year":2018},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.775159Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:1d3a46ac7cfab85e64e94dcfa3ba0fd8e211dd6821c95b36d73f6c9e61dcf62c","observation_id":"e85e8334-38d0-4a7f-996d-167854767c2e","resolution":{"observed_at":"2026-08-06T14:37:31.702694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.651721Z","title":"Modeling pathologies of diastolic and systolic heart failure","venue":null,"work_id":"dbfe8845-0c3e-4164-b363-c8fce668590c","year":2016},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.784248Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:e1b4d7d8864b2b2a6b74d2afdf067bbc1d56a94fe28b7a0d2031645ce5d0235e","observation_id":"0857f4ca-7803-4858-ab37-a48e36c59b5e","resolution":{"observed_at":"2026-08-06T14:37:31.659381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:37:29.791923Z","title":"Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.791923Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:63091222dd435f67f0c2ac3698084cd995a1d6504c87099cb43f5abb99126af1","observation_id":"94becc81-0352-4d7c-b3ef-bdb5321db55f","resolution":{"observed_at":"2026-08-06T14:37:29.791923Z","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":"2026-08-06T14:37:31.601309Z","title":null,"venue":null,"work_id":"b57cc76f-6c09-46a2-99cf-2a3ab68fbca5","year":null},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.800673Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:07e8f565ae9c25153a62d80e0743e0be56bc2c286e2e9a5897416f14113f516e","observation_id":"4f1a62e2-bd0d-41e1-96d3-1b90af2c7e6c","resolution":{"observed_at":"2026-08-06T14:37:31.607734Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.535703Z","title":"Myocardialbiomechanicaleffectsoffetalaorticvalvuloplasty","venue":null,"work_id":"b0473512-998c-46d2-a23a-a0f9ad687f9a","year":2024},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.820447Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:2fa1c11fe57625bebabe49d8c1321ff39ad4ad1c095899d19197a8ec49d8b355","observation_id":"2c78d331-6a8c-42a4-a93e-f55c3fa03937","resolution":{"observed_at":"2026-08-06T14:37:31.546304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.488023Z","title":"Passive material properties of intact ventricular myocardium determined from a cylindrical model","venue":null,"work_id":"c2618a39-4cfa-4967-9dee-719828b04bde","year":1991},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.827274Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:c01a19a21ef86e3f6f64bada8a2a69174ef8c545b67f678d02f897617ec46afe","observation_id":"8ebc5f0e-efbd-4edf-8a85-04714b6f91f3","resolution":{"observed_at":"2026-08-06T14:37:31.497888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.458346Z","title":"Inductive representation learning on large graphs","venue":null,"work_id":"8886accd-ba81-41f9-8f1d-5d5f044abbd8","year":2017},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.836322Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:0004b98a4f315b35ce41038ae60401adefb8d1066d63dc4751b71f9028478194","observation_id":"bd237658-7e03-4a85-a65d-bcba6357269c","resolution":{"observed_at":"2026-08-06T14:37:31.466007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.430784Z","title":"Constitutive modelling of passive myocardium: a structurally based framework for material characterization","venue":null,"work_id":"0d58de72-4314-4192-bff5-bdeb90827a29","year":2009},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.845698Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:1dc8904f4f7d756aa2e38dcf6982f66fe049f0903113342db9e46930f596f88d","observation_id":"864eb57c-8f37-4edf-9eee-dd9f0540a2e2","resolution":{"observed_at":"2026-08-06T14:37:31.437896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-06T14:37:29.853594Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.853594Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:bc2f94f08a653a8d56d960100c37319e951e0ad9d5961e12dc86cb274938af9a","observation_id":"475de5a4-004c-4613-bb74-ae25e5e206da","resolution":{"observed_at":"2026-08-06T14:37:29.853594Z","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":"2026-08-06T14:37:31.403247Z","title":"Patient-specific models of cardiac biomechanics","venue":null,"work_id":"aa8ac340-9655-49f7-9db3-7f9c38f1dfb8","year":2013},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.860299Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:6af9dc432492d9f38b9d08524ced65030843b52897ec6e10d891215b53efa94b","observation_id":"59085a34-6083-48f7-96ca-c1c00b1b4829","resolution":{"observed_at":"2026-08-06T14:37:31.413777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.376391Z","title":"Backpropagation applied to handwritten zip code recognition","venue":null,"work_id":"4566f310-5c2c-4528-9fbc-c47b29b3c7ff","year":1989},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.870975Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:8562209b271aadfcd5def6ae60e3f5e968af50ce2a953d5909908083d8a1d706","observation_id":"ddda3a2b-8d6d-47ac-aba8-7757b7761bf7","resolution":{"observed_at":"2026-08-06T14:37:31.384408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.343919Z","title":"Pytorch-fea: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta","venue":null,"work_id":"40a152d1-4760-4f6b-bd15-de692c4d6bd0","year":2023},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.881707Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:d5906b0e3f5e76706debfb6c5ae91cedcbd2727a4a2abeda7f2877f96823555f","observation_id":"135afcf8-df1c-4c2d-b52d-ff767744ae72","resolution":{"observed_at":"2026-08-06T14:37:31.358082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.297583Z","title":"A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero-pressure geometry of human thoracic aorta","venue":null,"work_id":"22f2a1cf-1457-4ea2-9290-05076bad76df","year":2018},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.891036Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:ae3d6c9a29d36741f650d963095295304a15df3e4545d36abb7af4ea779fea54","observation_id":"8e6f0258-ef04-4f45-9906-f3c67eda570a","resolution":{"observed_at":"2026-08-06T14:37:31.312468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.248043Z","title":"Journal of computational physics 463, 111266","venue":null,"work_id":"ddde1678-1bb8-4fec-8210-b0c82c4bf766","year":2022},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.900401Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:4ce69215a2a0e6e33100d961932f59adf2377dadaf23e918babbe98d91903ba0","observation_id":"ee499ae6-a067-4df3-b6ed-f8381daefdaa","resolution":{"observed_at":"2026-08-06T14:37:31.265179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.214420Z","title":"Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis","venue":null,"work_id":"9921f460-40b8-4d12-8893-3e8ff305ac4d","year":2014},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.909052Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:a46848ad6f9db825a81c2feeccc750cb03fbaaefb72a8fe9767a67077374dddf","observation_id":"b36db499-5c98-4e57-950e-dd6a47778ef7","resolution":{"observed_at":"2026-08-06T14:37:31.222989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20696","last_updated":"2025-06-25T11:37:34Z","snapshot_observed_at":"2026-08-22T06:58:03.398176Z","submitted_at":"2025-06-25T11:37:34Z","title":"IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation","version":1},"cited_work":{"arxiv_id":"2506.20696","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.20696","snapshot_observed_at":"2026-08-06T14:37:30.459755Z","title":"IMC-PINN-FE: A Physics-Informed Neural Network for Patient-Specific Left Ventricular Finite Element Modeling with Image Motion Consistency and Biomechanical Parameter Estimation","venue":"physics.med-ph","work_id":"1ae4876a-5359-4ce5-af6b-b7ab9c56e7ba","year":2025},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.919683Z"},"links":{"cited_paper":"/paper/2506.20696","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:c7ed3f3642a43845e55f6152e9c77ef97266b703abfdaa6c95040f1816315c69","observation_id":"570c5430-8f57-4448-b8e1-9fbd9bea2b6f","resolution":{"observed_at":"2026-08-06T14:37:30.471987Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.180124Z","title":"Effects of using the unloaded configuration in predicting the in vivo diastolic properties of the heart","venue":null,"work_id":"1f871bb0-a06d-40db-9ce3-a18611623e29","year":2016},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.927918Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:389434ad8ef56730a7b6c2cc6b09dd115602fdd775f0718baa749e0e1bc80e45","observation_id":"a024b119-bb45-4aa9-828b-69f9e7a41c17","resolution":{"observed_at":"2026-08-06T14:37:31.187126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.147214Z","title":"Learning mesh-based simulation with graph networks, in: International conference on learning representations","venue":null,"work_id":"783b5610-12d3-4291-b0d4-bda0a61f5987","year":2020},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.937658Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:3e38680f45b8a7ac77b6f7bb2217564da51967ba6d0d421044a8badf28efb619","observation_id":"1e869351-5bdf-45b5-ac49-009d49b82793","resolution":{"observed_at":"2026-08-06T14:37:31.161321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02413","last_updated":"2017-06-07T23:37:44Z","snapshot_observed_at":"2026-08-18T07:23:16.603533Z","submitted_at":"2017-06-07T23:37:44Z","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02413","snapshot_observed_at":"2026-08-06T14:37:29.950051Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.950051Z"},"links":{"cited_paper":"/paper/1706.02413","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:a94f811b3f526f62315adbc18067e3033dc78e3bc30a9e3522d512894b813a42","observation_id":"8ec70d1b-1208-4360-80ef-59931b5a01d3","resolution":{"observed_at":"2026-08-06T14:37:29.950051Z","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":"2026-08-06T14:37:31.095159Z","title":"An Introduction to Nonlinear Finite Element Analysis: with applications to heat transfer, fluid mechanics, and solid mechanics","venue":null,"work_id":"5dbb38f5-5d76-4acc-a5ab-bf53e084bef2","year":2015},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.959177Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:8df103ae366c002ab58160d824e92f4d1b31e3e8a20c2539fecd04d29b535971","observation_id":"d26e12fd-39ff-4b59-b901-12ff808618a2","resolution":{"observed_at":"2026-08-06T14:37:31.106502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.058637Z","title":"Highspatialresolutionmulti-organfiniteelementmodelingofventricular-arterialcoupling","venue":null,"work_id":"6d99e532-0d20-48bb-91e6-cef88b58937c","year":2018},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.972520Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:3d8a08431951460cc9eb202ef953812f8ba815c2dab8a60740489ab6f6b69b88","observation_id":"a41b3223-fd13-49a6-b6b2-bdbf9db31540","resolution":{"observed_at":"2026-08-06T14:37:31.068062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.18968","last_updated":"2025-04-26T16:31:12Z","snapshot_observed_at":"2026-08-20T15:34:17.073558Z","submitted_at":"2025-04-26T16:31:12Z","title":"HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.18968","snapshot_observed_at":"2026-08-06T14:37:29.980080Z","title":"Heartsimsage: Attention-enhanced graph neural networks for accelerating cardiac mechanics modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.980080Z"},"links":{"cited_paper":"/paper/2504.18968","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:174776b90d5206dc9eac8b2f621bd95b1cfbd6ced08e0b28ac3b37318a215592","observation_id":"2e4a21e5-1fc5-4f18-901e-78a67cd0bd3d","resolution":{"observed_at":"2026-08-06T14:37:29.980080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.00461","last_updated":"2023-08-02T08:26:58Z","snapshot_observed_at":"2026-08-16T15:11:39.723960Z","submitted_at":"2023-08-01T11:33:45Z","title":"Non-invasive in silico determination of ventricular wall pre-straining and characteristic cavity pressures","version":2},"cited_work":{"arxiv_id":"2308.00461","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.00461","snapshot_observed_at":"2026-08-06T14:37:30.291904Z","title":"Non-invasive in silico determination of ventricular wall pre-straining and characteristic cavity pressures","venue":"physics.med-ph","work_id":"8e848f8e-42f6-45c9-bc75-6a111a0a9a0a","year":2023},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.990479Z"},"links":{"cited_paper":"/paper/2308.00461","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:1b173186a73b00ecdf05a9eebccee4281206ef2d15d85f6e47fe512ee01455b6","observation_id":"dd5630c5-87f6-4837-895f-8130ec2b95bc","resolution":{"observed_at":"2026-08-06T14:37:30.305219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.983906Z","title":"Dropout: a simple way to prevent neural networks from overfitting","venue":null,"work_id":"e3b3c16a-fffd-4b09-a431-91dbec63e00d","year":2014},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.997747Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:58ed903a3148be106fb11f25f2cbf331d2431694d54c190fee3ab002912997a0","observation_id":"a33eb13e-327c-4a82-aee3-4eeac50198af","resolution":{"observed_at":"2026-08-06T14:37:31.021887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:37:30.005637Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.005637Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:0a586434dee6713788ffa42643d70951a11d29bab31b86b64ad429beb39dd2ca","observation_id":"2b34eee1-8ec0-4d34-9317-ba7ddcaaee93","resolution":{"observed_at":"2026-08-06T14:37:30.005637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-06T14:37:30.026330Z","title":"Graphattentionnetworks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.026330Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:0810caa1706dec45c747968db9ba6706ea4c7dcd8c237f44a0ba65c668ca4fcc","observation_id":"cacd17fd-f736-4790-8e0d-1059c2c167a9","resolution":{"observed_at":"2026-08-06T14:37:30.026330Z","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":"2026-08-06T14:37:30.894685Z","title":"Image-based predictive modeling of heart mechanics","venue":null,"work_id":"0d560d58-5336-4a42-9fc6-c52f590329cf","year":2015},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.036765Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:8883e10b44028d2c4249a8c4b3a3e492f9153fe2f7c08c7455ffbf435b7305c3","observation_id":"dcb46f30-29c5-43cd-9b4c-ea2f3a3bfebe","resolution":{"observed_at":"2026-08-06T14:37:30.910017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.854118Z","title":"Efficientestimationofload-freeleftventriculargeometryandpassivemyocardialpropertiesusingprincipalcomponentanalysis","venue":null,"work_id":"5433faaf-9faf-46b8-9635-43fa1e247d0c","year":2020},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.045493Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:e3e6edf757b44128410e07c2af066f93093223c55d8b5a6fc25b6eadd83497ad","observation_id":"d9cde825-2e03-470e-9988-40d98484eedd","resolution":{"observed_at":"2026-08-06T14:37:30.868872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.800487Z","title":null,"venue":null,"work_id":"5e29780b-885b-49d8-a4bd-dd48bd1d95f7","year":null},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.057781Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:5085523347d29b7c6b1bd8a3750ac39f906e74c47687acf8d006037dd15949f4","observation_id":"a22b46d7-52eb-4b4b-9ed5-a45dd41393b1","resolution":{"observed_at":"2026-08-06T14:37:30.808723Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.712383Z","title":"Non-linear finite element analysis of solids and structures, volume 1: Essentials, ma crisfield, john wiley","venue":null,"work_id":"cef07d65-32d8-4b0d-9a7d-59e4194db5f0","year":1994},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.081506Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:36e377c576b56c213af8902eb5be7bf33b2fa22813e82518b5f5735831d3f497","observation_id":"134a7d3e-fdae-4e8c-a49e-982088d14ada","resolution":{"observed_at":"2026-08-06T14:37:30.726527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.669263Z","title":"Graph transformer networks","venue":null,"work_id":"2000672d-0f29-4a6c-bdf0-c94ffb0338b1","year":2019},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.094404Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:b51d87bd40cf21c1c771c069d6f6cd0de99196724e4c5308fe39084b1326a242","observation_id":"efa1f3f8-1594-43ea-b410-ad558aaf48a2","resolution":{"observed_at":"2026-08-06T14:37:30.682398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.627002Z","title":"Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE international conference on computer vision, pp","venue":null,"work_id":"44ff81c9-bdb8-4b2d-82f2-db84b6985c18","year":2017},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.109235Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:f6adf966376a6f5bb8baf8914616f3b270a394b723ab066744c80a8187dd67f5","observation_id":"c7749e2a-c586-4d33-8752-fb6e7c6174c6","resolution":{"observed_at":"2026-08-06T14:37:30.637546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:31.566573Z","title":"Medical image analysis 17, 525–537","venue":null,"work_id":"e45caeeb-340c-410f-8ad8-0fc8c180695b","year":null},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:29.813493Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:06060468adcddcb7dac796b583925f6b6c641988a6427f283ba6a08b50cb48cb","observation_id":"36e36c95-fa23-45a7-b76c-073925e9f880","resolution":{"observed_at":"2026-08-06T14:37:31.576749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T14:37:30.746358Z","title":"Journal of Cardiovascular Magnetic Resonance 21, 62","venue":null,"work_id":"510ce5c0-5355-4759-b90e-8a8f350da999","year":null},"citing_paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T14:37:30.071452Z"},"links":{"citing_paper":"/paper/2507.18677"},"observation_digest":"sha256:83043bac574633e909c5f97adeb71c2eb5b52bb22c0403d762181b0da317d818","observation_id":"c34ea42e-8a02-4b67-b2f3-34f6a7a978a1","resolution":{"observed_at":"2026-08-06T14:37:30.761089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.18677","last_updated":"2025-07-24T14:31:35Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T19:09:47.117905Z","submitted_at":"2025-07-24T14:31:35Z","title":"HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":2,"verified_fuzzy":30},"total_outbound_references":42},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2507.18677."}