{"as_of":"2026-08-14T15:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:68c5cd81ed51105a33335e1750e9dc0132adeabbd90d037667d7936a2dfb71a3","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T10:44:28.181088Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2502.02912/citation-record","integrity":"/paper/2502.02912/integrity","json":"/paper/2502.02912/citation-record.json","paper":"/paper/2502.02912"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T10:44:29.196151Z","title":"Vulnerability","venue":null,"work_id":"6f26c58f-dff7-44aa-9350-d1c47f5b0030","year":2006},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.636598Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:aca9c40b025ec310ca06e4b565816d81b013a7d8d960eb0d0235092f768e5de4","observation_id":"3172c465-6ea0-43b2-b2d7-9c6e8c966fc9","resolution":{"observed_at":"2026-08-09T10:44:29.200617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.182515Z","title":"Machinelearningandphonedatacanimprovetargetingofhumanitarian aid","venue":null,"work_id":"18216146-140c-44b3-95c7-9fd4d1677cab","year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.642322Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:8826e558b78898d8585f498615e2ddbb2cedc282011382b84fe1eeacc5338efb","observation_id":"e38556a7-2a4e-41f3-8a34-429d17b0536c","resolution":{"observed_at":"2026-08-09T10:44:29.186906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.168488Z","title":"Mapping vulnerability: disasters, development, and people","venue":null,"work_id":"dbcc4d51-5db8-48df-b9fa-6c43691d0328","year":2004},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.647479Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:36554cd6a104043890e7342832eeb5d1d26c7a9b0319132d1e16da81a3c534d6","observation_id":"508833c0-8146-4f30-9405-f5b77f0f83e5","resolution":{"observed_at":"2026-08-09T10:44:29.173076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.153871Z","title":"Community vulnerability and mobility: What matters most in spatio-temporal modeling of the covid-19 pandemic? Social Science & Medicine 287, 114395","venue":null,"work_id":"4447b71d-76bc-43c4-9532-f330f9e06225","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.651825Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:c73a04426565bc8b0d45384c97f71285d10f648ef4bcb3900901b77f1665aaf9","observation_id":"dcc0de6d-da6d-4bbe-a891-e8f581d54a40","resolution":{"observed_at":"2026-08-09T10:44:29.158710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.140244Z","title":"Multiple local 3d cnns for region-based prediction in smart cities","venue":null,"work_id":"bbbd283a-d69a-496d-a4e2-bca08d485d64","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.656685Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:087f4d2551fd8bc9b5e3403f339f0f2462f0f56a144ca3498d82795de222a31a","observation_id":"0e1416cf-01d3-478f-aca2-e2417a01246e","resolution":{"observed_at":"2026-08-09T10:44:29.144761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.126079Z","title":"Microestimates of wealth for all low-and middle-income countries","venue":null,"work_id":"e9b9af0d-1973-449d-a7bc-a1e0979950b7","year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.661625Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:be4deecb287051068125f439ba54d835f3eefd22e8e29ba2bf2c49d03a0ed0f0","observation_id":"93ec6cc9-2af0-4996-9ca1-018a58651e7b","resolution":{"observed_at":"2026-08-09T10:44:29.130595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.111541Z","title":"Social vulnerability to environmental hazards","venue":null,"work_id":"236e0a90-09f0-4ca0-8794-ec95f24481b8","year":2003},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.666731Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:d32066ac948256f9445a05c0b7f92a3166fb49271e7161341fa16db75ef97912","observation_id":"870b579e-212c-4852-b724-8eed70f61626","resolution":{"observed_at":"2026-08-09T10:44:29.116239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.097618Z","title":"Association betweensocialvulnerabilityandacounty’sriskforbecomingacovid-19hotspot—unitedstates,june1–july25,2020","venue":null,"work_id":"d0156c7f-d2b9-42c5-b878-310f2ae5247f","year":2020},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.671282Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:76172cee8f5b9abc15297c74d99c3bdd2e7c988609989bf070be8b0aae612a96","observation_id":"c310f14d-e2e2-40f0-915e-517f9dc9c03d","resolution":{"observed_at":"2026-08-09T10:44:29.102455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.083906Z","title":"Time-seriesrepresentationlearningviatemporalandcontextual contrasting, in: International Joint Conference on Artificial Intelligence","venue":null,"work_id":"85bba45d-933f-42a2-8fa2-aa885a1763fe","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.675807Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:d3e5332e95d8bd5e5e736010cef8acb0be9b5c22481897080647ad128227d8f2","observation_id":"e4e6cfd3-2da3-4993-99d5-f7a4c966ec01","resolution":{"observed_at":"2026-08-09T10:44:29.088516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.068899Z","title":"A social vulnerability index for disaster management","venue":null,"work_id":"2f4455a2-46a6-4a73-b837-de412b58a8c5","year":2011},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.680919Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:2a0a6e070a545272062690bc77dba8b78ef68012db9e45466067bc9825dc4d84","observation_id":"34134355-ab30-4f95-aaa6-a28603071bd1","resolution":{"observed_at":"2026-08-09T10:44:29.073671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.054527Z","title":"Unsupervised scalable representation learning for multivariate time series","venue":null,"work_id":"69977057-2482-4ba0-b817-e65aa5dff282","year":2019},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.685863Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:4171d9fb3b7fd00d85b0fb9401dd5e35cb51d7d103fb894e1e9ad7bdaf611341","observation_id":"5d0c386f-cdd5-4e22-8523-835fdb441067","resolution":{"observed_at":"2026-08-09T10:44:29.059423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.039621Z","title":null,"venue":null,"work_id":"67aef312-fcf6-4e04-985d-8c55ce93dcc8","year":2019},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.690867Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:6d3859435c540275cc8d04463435b8925ffeeed29dae8cee5b543ee1ff08afa6","observation_id":"583c535e-9f76-46a2-9a8c-0072d5bded14","resolution":{"observed_at":"2026-08-09T10:44:29.044506Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.024563Z","title":"Semantictrajectoryrepresentationandretrievalviahierarchicalembedding","venue":null,"work_id":"aa131856-4b1b-44c3-9436-988ee5436c79","year":2020},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.696190Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:c1954bd7429bf6de3dc79e378df1b2e67d8913a494bf0e525ec394f2a570c755","observation_id":"14aba3ac-aa28-4c29-8fd0-58f3a60ec85f","resolution":{"observed_at":"2026-08-09T10:44:29.029218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:29.010017Z","title":null,"venue":null,"work_id":"c16128f1-8880-4c1e-8bf5-447c8f1d675e","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.700784Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:e157d3a1377564758a325dec9f24476a7abdcc0b32c51a5c833f0a146ef708a2","observation_id":"26448b0d-c56f-4d80-9b92-1d64fea1ebff","resolution":{"observed_at":"2026-08-09T10:44:29.014681Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.09021","last_updated":"2022-02-18T04:59:20Z","snapshot_observed_at":"2026-08-13T16:38:44.562359Z","submitted_at":"2022-02-18T04:59:20Z","title":"Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.09021","snapshot_observed_at":"2026-08-09T10:44:27.705584Z","title":"Effective urban region representation learning using heterogeneous urban graph attention network (hugat)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.705584Z"},"links":{"cited_paper":"/paper/2202.09021","citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:f2903a2a6abeab4c0c4a3e0b48c26734e35ca06a767aa7320912fe3e3977953e","observation_id":"983abadb-c68d-42d7-bbda-6d7bdc0a9ff4","resolution":{"observed_at":"2026-08-09T10:44:27.705584Z","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":"2026-08-09T10:44:28.994637Z","title":"Urban region representation learning with openstreetmap building footprints, in: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp","venue":null,"work_id":"ce3d9be0-9da9-4219-85fb-1ae23406c294","year":2023},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.732225Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:73a24d5efb2be28e47d7749adfd671fdc5a06c85422aa6be2822dadeb52ad5c9","observation_id":"869babce-c291-448a-a8f9-6f3c6ef8262c","resolution":{"observed_at":"2026-08-09T10:44:28.999662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.979368Z","title":"Urban region embedding via multi-view contrastive prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp","venue":null,"work_id":"4bf9a548-ddaa-4698-85fa-d2e4ee2f105b","year":2024},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.778937Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:9b5d9fdc0083934f6200922c1cdf4a69bae532133fc29fe9cade127ceacb7ac8","observation_id":"0b022579-312e-45b4-a3ba-4c96ae6186d3","resolution":{"observed_at":"2026-08-09T10:44:28.984672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.966131Z","title":"Revealingspatio-temporalevolutionofurbanvisualenvironmentswithstreetviewimagery","venue":null,"work_id":"eb018ac6-117e-4b5b-ad9f-bb09d1e1af0f","year":2023},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.858609Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:48be365fce27aa26116b2a0ecd9787ef7b9eef04521803ce3f3712fc51d5e9d7","observation_id":"91873562-589f-4c84-97eb-cb52ddc156d3","resolution":{"observed_at":"2026-08-09T10:44:28.970484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.952892Z","title":"Exploringtrajectoryembeddingviaspatial-temporalpropagationfordynamicregion representations","venue":null,"work_id":"f108f841-52d2-4571-8e7e-6779aa7a4bd8","year":2024},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.889409Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:f8146f942cbf50378223cc35a278be9154a136c59fed33cc4591635bd478e399","observation_id":"7672928a-c75e-400f-9a03-a26f8fadced6","resolution":{"observed_at":"2026-08-09T10:44:28.957480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.918133Z","title":"Urban flow pattern mining based on multi-source heterogeneous data fusion and knowledge graph embedding","venue":null,"work_id":"d0122d09-b2be-4fb3-9ddc-422ebc688b2a","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.925317Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:68353a414ed240d6593b11f547f4c450964f109cfc7a4b72b94ae83acb61a3e2","observation_id":"ffa8da89-afc0-43da-89ba-47a0da34a429","resolution":{"observed_at":"2026-08-09T10:44:28.924401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.901997Z","title":"Mhccl: Masked hierarchical cluster-wise contrastive learning for multivariate time series, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp","venue":null,"work_id":"3e8ffe0c-b0e7-440b-a539-6b0ce08b3a54","year":2023},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.931312Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:78b6c7f9e33a4d177e801acebf3c0fb0675923fae3c7617874bba77c31321e02","observation_id":"1f5a069e-4241-4266-9ac4-8e3e75c6a0e8","resolution":{"observed_at":"2026-08-09T10:44:28.907157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.03499","last_updated":"2016-09-19T18:04:35Z","snapshot_observed_at":"2026-08-13T01:24:25.628327Z","submitted_at":"2016-09-12T17:29:40Z","title":"WaveNet: A Generative Model for Raw Audio","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.03499","snapshot_observed_at":"2026-08-09T10:44:27.936799Z","title":"Wavenet: A generative model for raw audio","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.936799Z"},"links":{"cited_paper":"/paper/1609.03499","citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:51d8af1542191403b4dfdccf350f076eb2aeddff7f2c230a1bc8df00831beca3","observation_id":"7fa8aef8-8086-4156-8024-43485f67940b","resolution":{"observed_at":"2026-08-09T10:44:27.936799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-09T10:44:27.941989Z","title":"Representation learning with contrastive predictive coding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.941989Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:b09196f8eb48629175897be97f89e0621e1f34a0bbf2800c263b7234fb7ac8e4","observation_id":"08ec2ec3-ae5a-42f2-91a8-a1a6b2b599fb","resolution":{"observed_at":"2026-08-09T10:44:27.941989Z","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":"2026-08-09T10:44:28.885680Z","title":"Pytorch:An imperative style, high-performance deep learning library","venue":null,"work_id":"8c007cb7-b36c-4218-b475-4c1acfb69a09","year":2019},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:27.974141Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:d16158b7d4fcba834e41c8ed9791f6a9d93feb7d7bb87e08bd31595bc4513306","observation_id":"c84f4c79-9c8c-4511-9e9c-45caf24958d2","resolution":{"observed_at":"2026-08-09T10:44:28.891265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.865845Z","title":"Improveddeepmetriclearningwithmulti-classn-pairlossobjective","venue":null,"work_id":"b1406dc5-030d-4925-a969-b967fd9bfc83","year":2016},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.014758Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:dcd322faa28f7ea62fe4a31f2b00aec6d90a1b9c04e6add68b9330f511effe5d","observation_id":"d72259b0-8deb-40d9-a402-50ec2b835b72","resolution":{"observed_at":"2026-08-09T10:44:28.871380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.852531Z","title":"Most common modes of transportation for commuting in the u.s","venue":null,"work_id":"b9da7ceb-d454-4340-8f37-045165e85c3f","year":2024},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.085068Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:162251fd74a28d95aa5289b1d2277bc7678a49f7c55eaff74ecc3c8c5e88cbb7","observation_id":"ff7bdc7c-2829-4e4f-a0c9-483f6e8df35f","resolution":{"observed_at":"2026-08-09T10:44:28.856675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.838901Z","title":"Unsupervised representation learning for time series with temporal neighborhood coding, in: International Conference on Learning Representations","venue":null,"work_id":"71d53640-e8c8-450a-bdb5-58674281f0ad","year":2021},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.091775Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:906e6a910541af13271d30d2d5eef1f1c15258496e62a44f71a9e8d9578da540","observation_id":"456438aa-abea-4021-b116-dc87f9b23de9","resolution":{"observed_at":"2026-08-09T10:44:28.843594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.824222Z","title":"Graph attention networks","venue":null,"work_id":"7a69e1eb-03a7-4b46-9304-84147fdfb50b","year":2017},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.097218Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:7f4b5f14939cdc40b5fd03e0e1c3df008f342a02afb1343042bac202365db85c","observation_id":"d95f8008-af46-4661-9528-57b6540983d5","resolution":{"observed_at":"2026-08-09T10:44:28.829478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.808014Z","title":"Region representation learning via mobility flow, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp","venue":null,"work_id":"68e69785-4358-4b33-ab74-e5405f229aa1","year":2017},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.102902Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:e95af3de187c3ba981039893bdc6aa1d9bc63696ae11cc9a5cc4142c47ee674f","observation_id":"3d6cc410-4b27-4b99-abcb-da2c1a7a8733","resolution":{"observed_at":"2026-08-09T10:44:28.813983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.791340Z","title":"Mixingupcontrastivelearning:Self-supervisedrepresentationlearning for time series","venue":null,"work_id":"d8943048-bb9c-47c0-8f2b-bfdcdd3c8403","year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.107967Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:b17fb0a018e025ea70962028a6e5d770706bae7a9cef26f3b4a65b3e051a654a","observation_id":"0a582d46-3396-4a2f-b086-c14b78aaf0ce","resolution":{"observed_at":"2026-08-09T10:44:28.797339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.09760","last_updated":"2022-05-09T03:33:52Z","snapshot_observed_at":"2026-08-13T16:54:10.662274Z","submitted_at":"2022-01-24T15:48:50Z","title":"Multi-Graph Fusion Networks for Urban Region Embedding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.09760","snapshot_observed_at":"2026-08-09T10:44:28.113390Z","title":"Multi-graph fusion networks for urban region embedding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.113390Z"},"links":{"cited_paper":"/paper/2201.09760","citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:71713e5772deab672a638ab8404133c5cc30ee4b956f84e841db2b9508db0f96","observation_id":"b515b178-d851-4285-90c5-51a6252b9348","resolution":{"observed_at":"2026-08-09T10:44:28.113390Z","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":"2026-08-09T10:44:28.774699Z","title":"Unsupervised feature learning via non-parametric instance discrimination, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp","venue":null,"work_id":"be87b436-00ef-4d9b-9064-7b3ff2a54f6d","year":2018},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.118776Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:94fb6cd2c8b98b84970db9722902412fa5390ce505d6e40cfdafd010b9beac64","observation_id":"7334f2cc-e7c8-4815-9fd1-572b3de56021","resolution":{"observed_at":"2026-08-09T10:44:28.780734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.681405Z","title":"Assessing dynamics of human vulnerability at community level–using mobility data","venue":null,"work_id":"22384cf9-2ff4-4fdb-a3c0-b78ad8226b53","year":2023},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.123885Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:6764d3002dbea84034acededfb855630d20afe793a9f944562b9f7f32c52766c","observation_id":"2a95a2fe-112b-45d6-a151-2006b2a7d66d","resolution":{"observed_at":"2026-08-09T10:44:28.742249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.575583Z","title":null,"venue":null,"work_id":"6a15edbf-29d1-4b10-9d94-b4d049709f0c","year":2018},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.128607Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:f5b9e39838cf8000a4401421ef114d08fbbe99391ab17d6c5e28d85cc8e398d7","observation_id":"d9e9d35f-de48-4400-8095-38dff8b3d55b","resolution":{"observed_at":"2026-08-09T10:44:28.619802Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.558792Z","title":null,"venue":null,"work_id":"3310cd17-d709-46e8-8040-c1d7b154ef6c","year":2016},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.132894Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:7bf3c18b1b5f793d3f030750a00b3ca9549f638ea6f1129433d66295f7870f50","observation_id":"bc7f0761-8879-44e7-bcd3-b993a59894cc","resolution":{"observed_at":"2026-08-09T10:44:28.563953Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.541898Z","title":"Ts2vec: Towards universal representation of time series, in: AAAI Conference on Artificial Intelligence","venue":null,"work_id":"5f03edab-f33f-4225-96b3-e2a9531a9b9f","year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.137849Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:20e6e7e6adc52f16f19f76c63bfd21c25382ee7829d7e9276f670b8105882182","observation_id":"53be2113-f000-4703-9541-9ad1ca2205b3","resolution":{"observed_at":"2026-08-09T10:44:28.547019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.526970Z","title":"Regionembeddingwithintraandinter-viewcontrastivelearning","venue":null,"work_id":"52a8dd86-f09e-4e94-88e5-4dbbe72e0f59","year":2022},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.143466Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:95fb907cc62abc9b74d15b49664e7b99b8c11549e472e2a62f8efb2ca7e0cf0f","observation_id":"d948feb0-24fd-4a75-a509-aaebe8e583c9","resolution":{"observed_at":"2026-08-09T10:44:28.531678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.510635Z","title":null,"venue":null,"work_id":"d737cb5f-e40b-4575-8093-a69f56e2c985","year":null},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.148154Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:67646035df5b306c81297bb9ad1a167278d7ba6e827804435d6ee79423ed3735","observation_id":"d72966da-4cf8-4b81-8181-a1b933bc4f42","resolution":{"observed_at":"2026-08-09T10:44:28.515854Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.412066Z","title":"Unveiling transit mobility structure towards sustainable cities: An integrated graph embedding approach","venue":null,"work_id":"ae1e7838-7a47-4c9b-bb89-d9cc7c908aab","year":null},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.153072Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:88fe39eb05a7ec3285cce442f630de901ce8780d620e64432339c8d4ecdbc7e3","observation_id":"3d4ae2a1-4f05-4338-bc96-8c0a0ec76325","resolution":{"observed_at":"2026-08-09T10:44:28.455304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-09T10:44:28.298916Z","title":"Learning region similarities via graph-based deep metric learning","venue":null,"work_id":"c2d89e40-76a5-4171-b437-4a34c41dfc4d","year":2023},"citing_paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T10:44:28.181088Z"},"links":{"citing_paper":"/paper/2502.02912"},"observation_digest":"sha256:f207199194eeaa3303897c24bf67f540fbd67a071cfc208321cc740eb70823f3","observation_id":"0c8c6c99-5a50-4e55-b0c6-79ba1253d4b8","resolution":{"observed_at":"2026-08-09T10:44:28.360165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.02912","last_updated":"2025-02-05T06:18:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T07:51:46.314722Z","submitted_at":"2025-02-05T06:18:43Z","title":"MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":40},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2502.02912."}