{"as_of":"2026-08-18T06:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:81be7e1c2bd673369f2909848f72473ee616d90052924b81aabe541bc7ca621a","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T06:06:37.192509Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/1909.00166/citation-record","integrity":"/paper/1909.00166/integrity","json":"/paper/1909.00166/citation-record.json","paper":"/paper/1909.00166"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T06:06:37.483348Z","title":null,"venue":null,"work_id":"d081481a-d2b6-4012-927e-f8862b42b916","year":null},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.100603Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:e1ce4100364b07e18b752e2749f33fa4e039428bbe0a0dc73ab812b2ca0e723d","observation_id":"538aba03-3778-4523-b082-634615ff01fb","resolution":{"observed_at":"2026-08-14T06:06:37.487158Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06955","last_updated":"2018-05-29T17:57:31Z","snapshot_observed_at":"2026-08-17T16:20:13.396525Z","submitted_at":"2018-02-20T03:59:39Z","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06955","snapshot_observed_at":"2026-08-14T06:06:37.104798Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.104798Z"},"links":{"cited_paper":"/paper/1802.06955","citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:c615f7f138f29e04fd183386724e322e041c228a699eb8e4d13a1e821f1d9fd2","observation_id":"3c3e83db-e9d2-4012-80f1-e4ff4e009b9d","resolution":{"observed_at":"2026-08-14T06:06:37.104798Z","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":"2026-08-14T06:06:37.473315Z","title":"Azzopardi, N","venue":null,"work_id":"d1091332-24c6-4d5a-882e-9a7b5bf1c830","year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.108994Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:309118c455e22842dfaf996b9c65df373cfb85d136177825fdbebd57d0a95763","observation_id":"00046fd4-6845-4a4c-98e4-c8dc38a7192e","resolution":{"observed_at":"2026-08-14T06:06:37.476845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.463537Z","title":null,"venue":null,"work_id":"4d60a290-f389-4761-89d1-be17262b5468","year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.112764Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:bdebc458dc060b7550121371a20d7049e6dc32657a36edc4dceb7686aa6fcdaf","observation_id":"e00110f3-cac5-4de6-8ef0-2fcc0c9327cc","resolution":{"observed_at":"2026-08-14T06:06:37.466780Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.451746Z","title":null,"venue":null,"work_id":"204dd92f-d06d-4589-a574-93a520b79138","year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.116804Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:2a50074c013d3bf42ca96705f8d1e339fcef92bbb289941a9d8954c4518481ad","observation_id":"f9a68f07-9aa7-414c-be32-2b973930c6b4","resolution":{"observed_at":"2026-08-14T06:06:37.455636Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.441208Z","title":null,"venue":null,"work_id":"bc2cb234-af10-454a-8cb2-f52b56b8c18f","year":2017},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.120297Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:6232ff19bedf1a1534a7867b5235e9422a0e2597d8c4ee6dc473f9eab7500ca5","observation_id":"f5db4f0f-f325-4f07-9c33-11d31214f3a8","resolution":{"observed_at":"2026-08-14T06:06:37.444712Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.431267Z","title":"C ¸ ic ¸ek, A","venue":null,"work_id":"9185648d-9926-4ec0-91b6-2e93a687f42c","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.124035Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:17fa100e91f66f79636a1ef1d1d267447f07f4e448d63c39e57339203941d90b","observation_id":"d96afad7-0901-464b-be67-deb1bceade35","resolution":{"observed_at":"2026-08-14T06:06:37.434664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.421101Z","title":null,"venue":null,"work_id":"d360d854-ba7b-4cab-b54c-45807b1956a2","year":2017},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.127453Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:28208ffbec0f3df7ab58c5a32fd9ef5ac5a04f31e5981bb1bd7d41b86ff91b81","observation_id":"53299bcf-1bc7-4a4b-b46f-6ab5069fb804","resolution":{"observed_at":"2026-08-14T06:06:37.424403Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.02143","last_updated":"2019-11-23T08:36:48Z","snapshot_observed_at":"2026-08-14T19:57:23.255421Z","submitted_at":"2018-01-07T06:06:31Z","title":"Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.02143","snapshot_observed_at":"2026-08-14T06:06:37.130592Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.130592Z"},"links":{"cited_paper":"/paper/1801.02143","citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:ba30cb86c8ddd27d42be83f560eb045d88aaf878037101b063a1d1c05760e55e","observation_id":"2f282bc6-6801-4c30-b940-6a56c73b8d93","resolution":{"observed_at":"2026-08-14T06:06:37.130592Z","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":"2026-08-14T06:06:37.410889Z","title":null,"venue":null,"work_id":"e7cf84f3-a9d0-4ef8-988b-5b92e5e652b0","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.134097Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:0c45376e639de82b5d00d612d5ca56529dfc42306ef43c4849dadad923b34ddf","observation_id":"a594de31-448c-4e75-831e-edbf9567e89c","resolution":{"observed_at":"2026-08-14T06:06:37.414474Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.400277Z","title":"Drozdzal, E","venue":null,"work_id":"25012f64-9be7-4963-861a-25b000437bef","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.137254Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:5bae7bb80c46377153dcb09524e8ad5ca0b33f0745035c0c4f59b2037b62f5dd","observation_id":"4a179e8d-bade-4480-a2d9-25edc3f0657a","resolution":{"observed_at":"2026-08-14T06:06:37.404046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.140380Z","title":"Huang, Z","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.140380Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:ebf408d49fc4e68b903f045f9c4675e91d752dd17e1649cd6af8c0aca417e900","observation_id":"674d5519-56e3-4382-a491-89b7d6dc978a","resolution":{"observed_at":"2026-08-14T06:06:37.140380Z","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":"2026-08-14T06:06:37.384738Z","title":"Ioffe and C","venue":null,"work_id":"8597a834-264f-43df-b1c6-dc3d6dc535e9","year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.143710Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:cc36991aad2cb9290839bddc61dd388144816c8755b3ecddfc75f3edd37b362b","observation_id":"ecdb5597-4438-42bc-8491-1e57877e4dcf","resolution":{"observed_at":"2026-08-14T06:06:37.388034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.374261Z","title":"Kleesiek, G","venue":null,"work_id":"819731bc-b14b-4a61-a12e-035bfddd86ad","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.146929Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:33bc94b30cebe1431ca2119f53d120ceded7a2cef785b2193fddf4e99a3541ac","observation_id":"129b477e-59eb-499b-93c4-514f47f69a2a","resolution":{"observed_at":"2026-08-14T06:06:37.377771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.364039Z","title":null,"venue":null,"work_id":"f9637209-2648-4232-b1be-0f35d4991c3c","year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.150079Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:9f80f489adecda07a11b9c7be7a4be203ad12dc18a4ad735f7606eeba85744ba","observation_id":"f90310ea-8d07-4a55-859c-73ec47af93f8","resolution":{"observed_at":"2026-08-14T06:06:37.367604Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.353563Z","title":"Liskowski and K","venue":null,"work_id":"ab08ffe2-8c43-4ee4-92a9-85103876f690","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.153398Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:e39e7d987fec0ae71ee56581e11b147ab3a464461ecba37c55c8bbf1c0aae3ab","observation_id":"ad247f9a-e71f-4563-b1fc-7eeba788741e","resolution":{"observed_at":"2026-08-14T06:06:37.357234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.156618Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.156618Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:39b49e6d02ea4d0b755f3a9cc0f601162a268a4380b3c71c87de7ff830e879fe","observation_id":"ee35536a-ba30-45c8-9151-dd6831b1f981","resolution":{"observed_at":"2026-08-14T06:06:37.156618Z","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":"2026-08-14T06:06:37.337133Z","title":"Milletari, N","venue":null,"work_id":"79990fee-d8d0-4379-8157-ad9c64e9e70e","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.159787Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:b57f09b210a6601abf2538de23a72083c0bbb351190aade0a3c52dc5c2b8c73e","observation_id":"032ebb29-a2d8-40ac-9bc5-45028a316de7","resolution":{"observed_at":"2026-08-14T06:06:37.340535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03999","last_updated":"2018-05-20T23:33:30Z","snapshot_observed_at":"2026-08-12T19:45:41.630557Z","submitted_at":"2018-04-11T14:13:03Z","title":"Attention U-Net: Learning Where to Look for the Pancreas","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03999","snapshot_observed_at":"2026-08-14T06:06:37.163020Z","title":"Oktay, J","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.163020Z"},"links":{"cited_paper":"/paper/1804.03999","citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:7d21187c9627793884488c875e1c738a13af86eb58e07a4dfcedd5d05caf89bf","observation_id":"82884fc0-8530-474d-b558-41ff3dda898a","resolution":{"observed_at":"2026-08-14T06:06:37.163020Z","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":"2026-08-14T06:06:37.327382Z","title":null,"venue":null,"work_id":"9fd1ee8f-c02a-421b-9587-c46733d99853","year":2014},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.166636Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:099136f281f7a2ad83da25531379717a66f0a9e8c4e3ccfd2e0bae8e7393f907","observation_id":"0122d1fa-53c7-49d2-b0e8-e1a9d06513ff","resolution":{"observed_at":"2026-08-14T06:06:37.331013Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.170108Z","title":"Ronneberger, P","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.170108Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:72ff849a3719780852f7c9195fd903938d4be381e86dc1e6226a8079b37aeb08","observation_id":"56dbc57b-6362-4003-8e68-4525b27a7d14","resolution":{"observed_at":"2026-08-14T06:06:37.170108Z","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":"2026-08-14T06:06:37.311966Z","title":null,"venue":null,"work_id":"db609211-97a4-48e9-b3ce-5f93a34d4719","year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.173355Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:4bbc3772dfd2cbeb595ff5ec70a97ce15877b6c3d414c899e96ee54a65abf27f","observation_id":"1eea018e-c27e-471e-87eb-d3b86a974bdf","resolution":{"observed_at":"2026-08-14T06:06:37.315307Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.301058Z","title":null,"venue":null,"work_id":"da095c27-a717-4745-ab9a-540a9ad6ae76","year":2018},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.176338Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:14e35f55b1ba40022c9f6e3af657c9077db43b366413899a26da99426b4bec8d","observation_id":"fcf98cab-7924-44ef-b61b-71ac5e074b4f","resolution":{"observed_at":"2026-08-14T06:06:37.304910Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.290459Z","title":"Staal, M","venue":null,"work_id":"8ff1d0ac-1a79-417d-be1e-3bce4c4c7fb7","year":2004},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.179611Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:9ecb92a0d55bc28803b29f4776c6a60c586b334e7104e29cc483d56e8b20d45c","observation_id":"b845f623-39f3-48c8-b4ef-d2d74617f65e","resolution":{"observed_at":"2026-08-14T06:06:37.293868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.280253Z","title":"Visin, M","venue":null,"work_id":"bb7bd957-844f-4429-86e0-f56ae8ab849d","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.182798Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:d495fafab0301fcc3aa32b03e12d383942c64ba121640036aed75a3019999b67","observation_id":"c0e01f02-d13e-46a0-82dc-0de463723e3b","resolution":{"observed_at":"2026-08-14T06:06:37.283771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.269728Z","title":"Xingjian, Z","venue":null,"work_id":"2c38ca6c-3b93-4fe5-a798-ca519e8d6b95","year":2015},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.185962Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:af871c367665c351a85baa468b74b56e871d32ef774699c8c9cd08aed7fc6eb4","observation_id":"f90ae42e-3f0a-480b-ab39-88f8d5fa42bf","resolution":{"observed_at":"2026-08-14T06:06:37.273289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.259367Z","title":null,"venue":null,"work_id":"7f359645-0d93-4fdd-9906-7708a9068ee8","year":2016},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.189310Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:1c5e8d1d70808c6176a86847ca0a8a44479bb72dc042b60298096e9771c967e6","observation_id":"c7582fbb-a5a6-4694-95dc-42e5f30703dd","resolution":{"observed_at":"2026-08-14T06:06:37.262929Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-14T06:06:37.246956Z","title":null,"venue":null,"work_id":"ab99ef15-7bcd-421e-ad41-08d4f858ac28","year":2017},"citing_paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:37.192509Z"},"links":{"citing_paper":"/paper/1909.00166"},"observation_digest":"sha256:48e1e301ee47f7cdef1065606771d989819f69d86f21f0daa2674be4c726bce7","observation_id":"ad5d9b8f-eaae-438f-996b-46f753e4ff38","resolution":{"observed_at":"2026-08-14T06:06:37.252121Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1909.00166","last_updated":"2019-08-31T08:29:31Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-17T21:45:33.237994Z","submitted_at":"2019-08-31T08:29:31Z","title":"Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":28},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:1909.00166."}