{"as_of":"2026-08-08T20:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8974cc56d794a244037a660a8940bfc17fbce3e1fa87f4e328a0dc3f52c51e7e","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:48:23.292239Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.09980/citation-record","integrity":"/paper/2507.09980/integrity","json":"/paper/2507.09980/citation-record.json","paper":"/paper/2507.09980"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:39.316123Z","title":"Trusted multi-view classi- fication with dynamic evidential fusion,","venue":null,"work_id":"bc3190b8-231f-4c33-94ff-f5cf64357cbc","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:15.957456Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:280767ae10b3af97919ce64fbbd8599670bc919bcf87e4884a5d2361f7b5cede","observation_id":"1eb91de1-c76e-404e-bb19-dffe2af04597","resolution":{"observed_at":"2026-08-06T17:48:39.446556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.987800Z","title":"Incomplete contrastive multi-view clustering with high-confidence guiding,","venue":null,"work_id":"e1672740-dcf2-4c18-a200-a98c79e35a5b","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:15.987485Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f10aac03d7b6011d806d7769eb4c401caa6eac28b45a0f559c779098f139cf40","observation_id":"b0de53cb-a0da-46d3-b9e7-9eaeb7df7250","resolution":{"observed_at":"2026-08-06T17:48:39.117917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.736456Z","title":"Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification,","venue":null,"work_id":"af405494-5dac-4008-be58-cc7be6eb4a77","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.046174Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:42f9f4e98a031813548ee4035677de055d615cde1a7d9bff8465814387f7fbce","observation_id":"2e8c5423-fff3-488b-a16a-21649c4cb2cf","resolution":{"observed_at":"2026-08-06T17:48:38.836459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.537006Z","title":"Fast continual multi-view clustering with incomplete views,","venue":null,"work_id":"db96338e-5d26-43c4-bf39-fc7e27753548","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.091553Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:44668fa15cb4507663995de9fc9d2baed4b40e9cd1c5a33ab751f6c785967b24","observation_id":"69ae9b1f-a2f6-49ec-9df3-6a1f87c226a9","resolution":{"observed_at":"2026-08-06T17:48:38.629418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.384771Z","title":"Self-supervised information bottleneck for deep multi-view subspace clustering,","venue":null,"work_id":"7d4b9946-3bac-4c8c-8354-9b06d7363980","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.135860Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:dd0ead9d504ed13f99e3ab11f3f64224fcf92f345d79687631748d90dc45f341","observation_id":"702e228b-b63c-476b-8edc-11e541516111","resolution":{"observed_at":"2026-08-06T17:48:38.462387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.242425Z","title":"Posterior network: Uncertainty estimation without ood samples via density-based pseudo- counts,","venue":null,"work_id":"cc71180c-cb0b-4efa-99eb-15c44cf403f4","year":2020},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.175870Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:9e0dda8eb8977dd6ec91967fb18d63ead22423dea8609143c67b083865e43468","observation_id":"a4fba3a7-a976-41ca-a3c8-868bc0d51cf3","resolution":{"observed_at":"2026-08-06T17:48:38.311463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.954038Z","title":"On the Dempster-Shafer framework and new combination rules,","venue":null,"work_id":"044dd5c3-41ab-4241-91ae-e8e221be4667","year":1987},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.300552Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:164c8dbcfed9e03316a9a0187827823785467211dfc30e12ecbb5ac0f2c592e4","observation_id":"f5e3805e-23fd-4063-96ec-b4ea636570cf","resolution":{"observed_at":"2026-08-06T17:48:38.032455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.821448Z","title":"On H ¨older projective divergences,","venue":null,"work_id":"33d339ba-35ab-4046-91c8-a8134b3c1aef","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.416484Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:c36495dcdc34b7e01c2623a99448127e30636297e38335200391e46059a1b66d","observation_id":"177425d6-e59c-47af-b991-c380955f9855","resolution":{"observed_at":"2026-08-06T17:48:37.878036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.656307Z","title":"Bayesian filtering: From Kalman filters to particle filters, and beyond,","venue":null,"work_id":"7b7953d7-701f-4f46-bacf-1b659ea7abe2","year":2003},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.509829Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:e19d70969326dd3cddf257d7bccf4e520da7d20ad8c3194e698033bbb626d14f","observation_id":"78d4eb8e-0754-4d55-ba95-3c04e122fc98","resolution":{"observed_at":"2026-08-06T17:48:37.747012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.513277Z","title":"I-divergence geometry of probability distributions and min- imization problems,","venue":null,"work_id":"d7f190ec-ea8e-485e-86ca-ba14b7eb9d05","year":1975},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.548040Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:d656448e4d9ac99018474e2f6a231dc4171f989ab551ff8e73d3bae4a525b3fb","observation_id":"b85585c1-2287-4cab-a430-b2d46471ae6a","resolution":{"observed_at":"2026-08-06T17:48:37.577287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.357178Z","title":"Self-supervised geometric features discovery via interpretable attention for vehicle re-identification and beyond,","venue":null,"work_id":"510bdd63-0d91-4127-87d6-5fbb11eac5a0","year":2021},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.610211Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f86e901340e524dcd699b29efe022a4f09bbc25b397ee9914f8d681c2a48c68f","observation_id":"94c4f065-cf85-429d-aeba-7ddb0672d6f9","resolution":{"observed_at":"2026-08-06T17:48:37.447905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.195062Z","title":"Exploiting multi- view part-wise correlation via an efficient transformer for vehicle re- identification,","venue":null,"work_id":"2c538b6e-41cb-49ce-868d-87b1faecc8d8","year":2021},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.688765Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:6901927f98f169e38b969b77e2cad6ff9f73f66884156530d6c2f5a5a36d3cf3","observation_id":"b81c6625-b8e6-41e6-b767-4d2a7d7c185c","resolution":{"observed_at":"2026-08-06T17:48:37.277022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:37.046003Z","title":"Synthetic-to-real self-supervised robust depth estimation via learning with motion and structure priors,","venue":null,"work_id":"c33a90f2-1c71-4f20-865e-f5b48ea9f75e","year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.740788Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:6cb654f38d77102bf2770e2756325617874a2502f7fa48e718d428416497f432","observation_id":"392f8f36-a6e1-473e-8023-eba6a060d245","resolution":{"observed_at":"2026-08-06T17:48:37.107585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:36.894876Z","title":"Eventgpt: Event stream understanding with multimodal large language models,","venue":null,"work_id":"95656205-8282-4a09-939c-dcc85e89c1a8","year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.851895Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:fbc88869c5c6b43830ea1f3381a84318837bf9b531a10211c55f19ac4352bd2b","observation_id":"7dcc115b-6c2d-4a1b-a69e-c7c66dd1369c","resolution":{"observed_at":"2026-08-06T17:48:36.971704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.09158","last_updated":"2026-05-09T06:28:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-12T08:33:46Z","title":"FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.09158","snapshot_observed_at":"2026-08-06T17:48:16.921127Z","title":"Favchat: Unlocking fine-grained facial video understanding with multimodal large language models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:16.921127Z"},"links":{"cited_paper":"/paper/2503.09158","citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:3f6d5c86fbfa97ab219270ce1761043557753e74715b1189bd903fbf79feb8a8","observation_id":"5bf708bb-aadd-4262-9815-5ed15dfed5ce","resolution":{"observed_at":"2026-08-06T17:48:16.921127Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:17.025942Z","title":"Unif2ace: Fine-grained face understanding and generation with unified multimodal models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.025942Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:8fb10235af4f4e0a0691ceeb1586a6dded6106e01952cf5be604d927ecda3496","observation_id":"11de69b8-a397-4a97-9446-2494bfdf9ad9","resolution":{"observed_at":"2026-08-06T17:48:17.025942Z","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":"2026-08-06T17:48:36.732512Z","title":"Dr-fer: Discriminative and robust representation learning for facial expression recognition,","venue":null,"work_id":"0ecdf6f3-d9f0-407d-a577-e37a53aa5a6c","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.087293Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:2bb726520bc8ce5bba574ddfdf24cc6c401498f17b9c32fc4759ce4aa206e36d","observation_id":"bfa1b76b-bb84-4bdd-bd00-b062d0a1994e","resolution":{"observed_at":"2026-08-06T17:48:36.811947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:36.597187Z","title":"Stprivacy: Spatio-temporal privacy-preserving action recognition,","venue":null,"work_id":"c600cdde-86a6-4961-b0b7-975278db980a","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.194153Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:e976b919c2a6636855f491799f68929f707367bea5bbe56c78730ac7806f5847","observation_id":"31fe00e8-044c-4a02-8cd8-36243abac0e7","resolution":{"observed_at":"2026-08-06T17:48:36.659610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:36.467986Z","title":"Instant3d: instant text-to-3d generation,","venue":null,"work_id":"1a4758d6-c8c0-4943-a063-a2c077c3c269","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.320065Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:beca3fa116a1583b684f079d7766ea3646596e2f2a670464bddbe47eec7a2b5d","observation_id":"ec8a1808-1a64-443d-88df-854ebc8c9c2e","resolution":{"observed_at":"2026-08-06T17:48:36.517026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09140","last_updated":"2024-10-11T17:55:30Z","snapshot_observed_at":"2026-08-03T12:13:34.901316Z","submitted_at":"2024-10-11T17:55:30Z","title":"RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09140","snapshot_observed_at":"2026-08-06T17:48:17.352432Z","title":"Realera: Semantic-level concept erasure via neighbor-concept mining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.352432Z"},"links":{"cited_paper":"/paper/2410.09140","citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:b9720486167fc6a51231b563e2d3427d00e5df66d649e16956193d3cc04d9980","observation_id":"a82f1206-0aac-4829-b24f-a8048b37cbbe","resolution":{"observed_at":"2026-08-06T17:48:17.352432Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:17.406362Z","title":"Vistorybench: Comprehensive bench- mark suite for story visualization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.406362Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:d7c458893b2e848c2a0ae6535e38f9c27855ba5fae84043342d353e38f1a72af","observation_id":"48a50fe0-88e5-4f90-b289-2b83c8898434","resolution":{"observed_at":"2026-08-06T17:48:17.406362Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:17.512624Z","title":"Reliable conflictive multi-view learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.512624Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f45995e0d655ec7c8981c41a6c78acdef8d0cfb5615675ab640d81a68e156fb4","observation_id":"18bd2dc5-9331-4092-8ea4-09d3b96cd3da","resolution":{"observed_at":"2026-08-06T17:48:17.512624Z","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":"2026-08-06T17:48:36.150058Z","title":"One-Step Multi-View Clustering With Diverse Representation,","venue":null,"work_id":"f5ea68d7-6b03-4ea7-8038-3504fd0b6232","year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.550431Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:e0f28f6e6918009d9c92d1b0593751ca8f19b5e1eff23bf836503b37b610486d","observation_id":"6c5cd254-f2d7-4708-ab65-2f4c7835ee4e","resolution":{"observed_at":"2026-08-06T17:48:36.312663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:35.968669Z","title":"Multi-view multi-label canonical correlation analysis for cross-modal matching and retrieval,","venue":null,"work_id":"8fff87cc-d2f5-418c-9f72-8515d56bd52f","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.662449Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:193be139c298a0232ea47771397aecd4fc99b9051d3ef599b95af699b19b25be","observation_id":"3f64da6e-9406-4dd3-a247-9c426e8bf06f","resolution":{"observed_at":"2026-08-06T17:48:36.062091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:35.627354Z","title":"A multi-view co-training network for semi-supervised medical image-based prognostic prediction,","venue":null,"work_id":"7935813f-00d5-4220-9a10-1c1dac3f7051","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.704237Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:5ae7dd931065a006c249bb184e481d581544259b519c637f912e33a58c7c0a2b","observation_id":"a494d69d-1567-4c89-b8e8-71eea412b7ac","resolution":{"observed_at":"2026-08-06T17:48:35.798001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:35.366000Z","title":"Visual tracking with spatio-temporal Dempster–Shafer information fusion,","venue":null,"work_id":"f4919837-5de7-43bc-8338-ff6fcd8feb1e","year":2013},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.828788Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:81aac38a5b1bc249d30b513bbe23364a08c176b04933aec390ef8e2fe65d0ab6","observation_id":"9befe091-2e97-43de-9ba2-86b96ea1a229","resolution":{"observed_at":"2026-08-06T17:48:35.488793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:35.014771Z","title":"Variational Quantum Linear Solver-based Combination Rules in Dempster–Shafer Theory,","venue":null,"work_id":"341d99a9-4a0a-4bab-a596-f3803f25864f","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.930373Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:d232e1db0daffbfa05b012afded5940c18fe13ebca950d608665d6ee7f4193e0","observation_id":"99aa1b23-92cd-4ada-89f5-a231dc42df4a","resolution":{"observed_at":"2026-08-06T17:48:35.148661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:34.584573Z","title":"Scale-invariant divergences for density functions,","venue":null,"work_id":"1fbf6770-65f0-4163-be6f-d83ce6130d49","year":2014},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:17.963306Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:b65e7bbb49b8decaefa6db5d1d971493d145d7b6927c2ffccc1e9006718fc776","observation_id":"2920c954-7386-43b8-bbe5-7342923499b0","resolution":{"observed_at":"2026-08-06T17:48:34.829110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:34.261289Z","title":"The Cauchy– Schwarz divergence for Poisson point processes,","venue":null,"work_id":"6b9bbf39-5452-477b-b7ae-2b7fa0b71a05","year":2015},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.028282Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:14bf529c3d2645ac0f6c4ca187e037d339da7d176547f104cea9fa5a3ffe4ebf","observation_id":"8ee2219c-875e-4f6a-b956-62a21ad57e35","resolution":{"observed_at":"2026-08-06T17:48:34.412542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:33.859351Z","title":"K-means clustering with H ¨older divergences,","venue":null,"work_id":"3d77b5fd-f617-4f3e-88e5-aad6c4a08ff1","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.144876Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:aafe1d46418d0eeebf6ce2c3e69cf22e1862bcc93ae08e8d23f2952f401d9849","observation_id":"878d72c3-16b0-4a5e-9f28-b3bb030f7bb6","resolution":{"observed_at":"2026-08-06T17:48:34.043752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:33.417301Z","title":"Classification for Polsar image based on h ¨older divergences,","venue":null,"work_id":"235ec31f-fe7a-4ad2-88e1-cf3bea77366a","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.224883Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:58a5a32bb7fcb4f289d7b2dd20a7bb12e8b540939fabbb2473261ec8e32b3481","observation_id":"8abb0ea8-ce51-448e-8674-565b262b1722","resolution":{"observed_at":"2026-08-06T17:48:33.573552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:33.067880Z","title":"Inverse kalman filtering problems for discrete-time systems,","venue":null,"work_id":"1e085d29-42b9-4b0a-915f-4bea4a60e16b","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.322056Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:84a6f72634e2a20c0d24f2298b6061fab70026f592e71d616635f57858d24459","observation_id":"e3ddd8d8-1ade-4bed-8fae-f182c118d66d","resolution":{"observed_at":"2026-08-06T17:48:33.213287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:32.757537Z","title":"Adaptive kalman filtering for histogram-based appearance learn- ing in infrared imagery,","venue":null,"work_id":"0195c85c-c817-45ab-a23e-12cedd0c7661","year":2012},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.449980Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f77901902ce9be89a1eaad70e47ae5629623d3f4e49763326453ca188db2d865","observation_id":"d35c7c3c-c2bf-41ac-bca9-0151eb640f9e","resolution":{"observed_at":"2026-08-06T17:48:32.922181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:32.405687Z","title":"Efficient and fast real-world noisy image denoising by combining pyramid neural network and two-pathway unscented Kalman filter,","venue":null,"work_id":"9233dd43-d037-41c9-b192-531540d41da5","year":2020},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.592686Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:662a097041eb2b5ec62ab6ac3c04ff59c0a2554b068fe227f3fba56a9640459a","observation_id":"8e73952d-5f1d-404c-bb96-6641dfada5e5","resolution":{"observed_at":"2026-08-06T17:48:32.601948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:32.078206Z","title":"Kalman filter for spatial- temporal regularized correlation filters,","venue":null,"work_id":"9c7f4042-218f-4a4b-8550-81daab7398b1","year":2021},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.698928Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:aaa0d37a676a95987fd0f4278193892049ab402994d2e049a678e19be591cff5","observation_id":"8f7f0224-75df-45be-b55c-5874f4d054fd","resolution":{"observed_at":"2026-08-06T17:48:32.200705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:31.861733Z","title":"The statistical analysis of compositional data,","venue":null,"work_id":"b6e11289-b0a4-423f-a4d7-de0049c2855b","year":1982},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.808968Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:4bcb814f94ceeb346d3ebbbb36e27845f33013508a1cf228e80697209afe12dc","observation_id":"30d044ac-b5d9-4572-b9bb-9cbbcb286dc7","resolution":{"observed_at":"2026-08-06T17:48:31.932888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:18.949585Z","title":"Dirichlet and related distribu- tions: Theory, methods and applications,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:18.949585Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:33ae555a76c2fbde00bdce40e7e82882353424f784ffd281cf3e23e0bae6885b","observation_id":"ab456d37-a73a-4b1a-bf04-27541ca859f1","resolution":{"observed_at":"2026-08-06T17:48:18.949585Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:19.092230Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.092230Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:0884f2d041e7e7b750c5d55f3a751e68ebbfafc1a89d9dccba76501cea2493f1","observation_id":"0dad9501-18ba-4fca-9af2-efcebccec63b","resolution":{"observed_at":"2026-08-06T17:48:19.092230Z","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":"2026-08-06T17:48:31.643038Z","title":"Clustering documents with an exponential-family approxima- tion of the Dirichlet compound multinomial distribution,","venue":null,"work_id":"ff124ca3-2e34-434b-b43b-7ae8eecf0f14","year":2006},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.228879Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:5fbec702c0141459f93d88a5f5d2e9323ebf37636c4ed60a7ea84e9bda4de341","observation_id":"5d85d5bf-b1c0-42c8-b4b9-7a609897e6b2","resolution":{"observed_at":"2026-08-06T17:48:31.698124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:38.110025Z","title":"Jsang, Subjective Logic: A formalism for reasoning under uncertainty","venue":null,"work_id":"7a23dc32-db1c-4c87-9ea3-ec6c12492a77","year":2018},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.387587Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:394502e765f9c690e4fb61145f2b97417741d16c4465e6c7c619c93a545c0284","observation_id":"df13ebc0-608b-4d8f-913e-5d03f5f42079","resolution":{"observed_at":"2026-08-06T17:48:38.179106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:31.484620Z","title":"Belief functions and parametric models,","venue":null,"work_id":"a8b8fcf4-23f2-47f4-9b05-e8e5581d0b82","year":1982},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.535676Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:d5a13351c6cfa68cf8050d20376bcb4c1548af2990551143f1ac1cfd049e7816","observation_id":"a47d7371-d631-487b-bf58-dbf11bb1b8f5","resolution":{"observed_at":"2026-08-06T17:48:31.565938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:31.330611Z","title":"A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation,","venue":null,"work_id":"2a228404-aa49-49a2-ba12-23b0f1d7dfa3","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.723009Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:db0ef860f8fabc884783d2698431af2c0421584f2c826ce420f2f84a0e496c00","observation_id":"999ecc0f-605d-46a3-9dff-8a3e9c0eb6c8","resolution":{"observed_at":"2026-08-06T17:48:31.387986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:31.166888Z","title":"Multimodal Dy- namics: Dynamical Fusion for Trustworthy Multimodal Classification,","venue":null,"work_id":"d467d0a8-04f8-40db-8cd6-90d91d54a913","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:19.866823Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f01869dc61316d7405591fd8a8406a08abef6319dc7da4d9a98ed4a0cf875c52","observation_id":"b7e56cb3-ee9c-4afe-8aec-0d346e8f43b7","resolution":{"observed_at":"2026-08-06T17:48:31.235174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:31.031805Z","title":"Cauchy–Schwarz Regularized Autoencoder,","venue":null,"work_id":"424892bc-a8fc-4d80-b2f4-637790347597","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.133468Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:632c11129675817b20af6c236ba79c09f3bd80c717452d2ebcd7d45616d4e806","observation_id":"c041a443-cb90-4bcd-b833-9295bdd492eb","resolution":{"observed_at":"2026-08-06T17:48:31.091810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:30.859495Z","title":"Sun RGB-D: A RGB-D scene understanding benchmark suite,","venue":null,"work_id":"131b715d-6774-46f2-b1a5-a89197f0589e","year":2015},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.263537Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:5d005ad532e0a9acff69e613c8f22afb4481b90f7bb7b4123f5775f8ce4075fb","observation_id":"a4819b35-e03b-49b5-92f3-6d6ead748ebb","resolution":{"observed_at":"2026-08-06T17:48:30.931423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:20.327601Z","title":"Indoor segmentation and support inference from rgbd images,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.327601Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:4487777b3d349d4a73b36da452b166b731a0baeee3b346d6a6b82a712c2a530f","observation_id":"78c30408-3026-4f1e-ab0a-8821504c0d27","resolution":{"observed_at":"2026-08-06T17:48:20.327601Z","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":"2026-08-06T17:48:30.702453Z","title":"Semantic understanding of scenes through the ade20k dataset,","venue":null,"work_id":"b00e4e51-5649-4b28-a7ef-716175c27c31","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.483784Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:19ce0e984fde6b00c3b81fac242eba737a123f71f9d13d42f5e602d3e1f14e6a","observation_id":"7adae435-1993-406f-8c1b-b66aa83ac31a","resolution":{"observed_at":"2026-08-06T17:48:30.772098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:30.528792Z","title":"Scannet: Richly-annotated 3D reconstructions of indoor scenes,","venue":null,"work_id":"13cebf29-6d82-4d7d-8629-dcf6c8456702","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.583665Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:7abe9492d27cfa5e84261141f571b793c416aa9efaee4d2250e4e154ee08c4a4","observation_id":"f879c8ef-fc24-4ecd-8e86-9f912ae6603f","resolution":{"observed_at":"2026-08-06T17:48:30.620474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:30.352805Z","title":"Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification","venue":null,"work_id":"03504316-dd8d-4a59-8743-672cdcca1b8e","year":2016},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.711705Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:fc191a9839f5687c46e3f7fc077d6319afb2dce53aeb321f3356a77fd3ca18ad","observation_id":"5baef114-3212-45c0-9ee4-5448e3f372ec","resolution":{"observed_at":"2026-08-06T17:48:30.427087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:30.213388Z","title":"A fast iterative shrinkage-thresholding algo- rithm for linear inverse problems,","venue":null,"work_id":"54260ab4-6f43-48ec-8299-7360d0e56433","year":2009},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.852842Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:6e5c7ffb75f369f51647c591373ced1ba33cf8e132184cd34713849c5a08ccf3","observation_id":"1129e0f4-96a7-47c0-976c-8d33c662299b","resolution":{"observed_at":"2026-08-06T17:48:30.291741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:30.010447Z","title":"Unsupervised video matting via sparse and low-rank representation,","venue":null,"work_id":"f3bf7b16-83ff-4dd6-ba25-121a4f269f3d","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:20.903389Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:938105930d8a74e43b74409c17ccea9936e3592480ef598324e4d724e1fe0085","observation_id":"618eb559-5fa2-4e5e-8975-90851cbefd68","resolution":{"observed_at":"2026-08-06T17:48:30.088411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:29.446083Z","title":"DasGupta, Probability for statistics and machine learning: fundamentals and advanced topics","venue":null,"work_id":"5265ad58-d08d-4a63-a021-38d324301c8e","year":2011},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.102977Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:9595b0add74931d095786a0290ce3c8031e062aa9f6a28d5eec08df2941f91b4","observation_id":"2bab2c6b-4106-453f-99e3-20c206d27026","resolution":{"observed_at":"2026-08-06T17:48:29.614335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:29.082943Z","title":"Learning deep sparse regu- larizers with applications to multi-view clustering and semi-supervised classification,","venue":null,"work_id":"d6f16354-7509-4702-b7ab-a850bbd01d41","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.203790Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:7afeb6e4743377d5f24aa58433da3f9c64d0e9d081ffed57baf4d0e2771e14b0","observation_id":"22f78378-e82f-4472-b177-8973ff162dba","resolution":{"observed_at":"2026-08-06T17:48:29.287889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:21.338635Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.338635Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:fbeb9e6b44132bc04b4233eb073db4778e83acda8f48cc5bbbe83e12941b500f","observation_id":"95922390-4399-4476-847f-84c983863cfe","resolution":{"observed_at":"2026-08-06T17:48:21.338635Z","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":"2026-08-06T17:48:28.716388Z","title":"ImageNet: A large-scale hierarchical image database,","venue":null,"work_id":"b3015343-baa7-4a86-90c6-002b4625e2a0","year":2009},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.445166Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f829eec0cd12d99e4d07b5d0439e5f6013df932ccff46ebf085fda8c5c272187","observation_id":"5405516f-e57d-43eb-9271-fd5378f828db","resolution":{"observed_at":"2026-08-06T17:48:28.903353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:21.505363Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.505363Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:191bd387514451d241e06fd502560ceb4323a3fd12ab98c29e5d8ee47fe6fe34","observation_id":"b05b5dbb-f917-4712-8719-0aad31acaace","resolution":{"observed_at":"2026-08-06T17:48:21.505363Z","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":"2026-08-06T17:48:28.403183Z","title":"Densely connected convolutional networks,","venue":null,"work_id":"09cae817-eafa-4154-ad6c-fc974a006440","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.550741Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:51d90b332c7ad9724714daa8e2c47d8b39a72183dfc61af6bd1241f1a2b5c254","observation_id":"25ce883f-7135-42d7-adf9-8d810b4c134e","resolution":{"observed_at":"2026-08-06T17:48:28.556382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:28.032791Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,","venue":null,"work_id":"188cf745-d99d-4e8f-8f92-419d18ae79dd","year":2021},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.663697Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:98d87eb49469ed3406c48825bb7fc101d999e098cc2cad59413631399c434fd7","observation_id":"412ac5ea-bed6-444b-9d7e-8985ab6cea11","resolution":{"observed_at":"2026-08-06T17:48:28.184145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:27.734729Z","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models,","venue":null,"work_id":"b1dd8111-6bb0-4fff-b9ed-9e18f2b31d0b","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.734601Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f8152427e011f5ff21062a1c3c2d7b2e3930ecfb0b2199338196ef6650ab4f29","observation_id":"2e66b3bb-a7bf-4883-ac39-94223b87599b","resolution":{"observed_at":"2026-08-06T17:48:27.828260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:27.471046Z","title":"Adam: A Method for Stochastic Optimiza- tion,","venue":null,"work_id":"85994575-2494-4a44-9dd6-98f858f8bb01","year":2015},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.802298Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:2f233ba6ec7e351496d9691537df1a5c89b691bfcc308f006bb43c6e4ecc2fe6","observation_id":"350aa3d7-add4-4e39-8105-5832ed60997f","resolution":{"observed_at":"2026-08-06T17:48:27.573065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:27.208702Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"7bcfc069-7e93-4109-8d30-f786ddbd2983","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:21.902445Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:84cd7976a66bae48a11378c2a68ad8b51a437da054da26f53eab7b2de6fd75b5","observation_id":"fc0e7d99-1bc1-4bf0-9c86-8c8b63ef0b85","resolution":{"observed_at":"2026-08-06T17:48:27.326029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:26.975444Z","title":"Translate-to- recognize networks for RGB-D scene recognition,","venue":null,"work_id":"505ce68e-a39c-42b3-9f9f-f3ff6ea5c429","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.022491Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:244497f242e6b6d299f2be3b9dca7a02148902f1ee549e00692b1c877d71bea8","observation_id":"b13e6cb8-3e65-49cf-9bf3-29c70e90982e","resolution":{"observed_at":"2026-08-06T17:48:27.084090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:26.736414Z","title":"Image rep- resentations with spatial object-to-object relations for RGB-D scene recognition,","venue":null,"work_id":"abc575ae-204b-492f-9ed8-9a03b940b28e","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.078071Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:dfb2ca4ed3415259bd62900d4160d26766c78863de2395dd4140b690b96b2aac","observation_id":"57639c06-a768-4832-a854-87dfd1456dca","resolution":{"observed_at":"2026-08-06T17:48:26.832597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:26.495523Z","title":"Centroid Based Concept Learning for RGB-D Indoor Scene Classification,","venue":null,"work_id":"0a932f49-a63e-444c-9bce-94ad0198a5ad","year":2020},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.145531Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:110d65d000b11f96c26a4d4c98f568204c8a2fc6d4aa69d529ebb4e96692f57f","observation_id":"6bab63d6-5f84-4ef0-9c44-6a769f63fb2a","resolution":{"observed_at":"2026-08-06T17:48:26.559654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:26.367570Z","title":"Cross-modal pyramid translation for RGB-D scene recognition,","venue":null,"work_id":"da945c5e-ab85-4500-afa5-d6dbe74440e5","year":2021},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.211215Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:66c26a77caa095b141ceda5ac9a4f0b4dcae0894a39c180c8d892150a68fdca6","observation_id":"97a294d4-e439-4877-86db-c5cfb3e05046","resolution":{"observed_at":"2026-08-06T17:48:26.448802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:26.231952Z","title":"When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition,","venue":null,"work_id":"ca7e249c-c373-424c-a9da-de5c4f7bc7a7","year":2022},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.318983Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:0a1e63c743970d0233acde42836607ded5f085623f3d28eabbc3f9a46b18eab6","observation_id":"3c8cf367-55ed-49a0-882c-e7052762c174","resolution":{"observed_at":"2026-08-06T17:48:26.292694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:25.968599Z","title":"FGMNet: Feature grouping mechanism network for RGB-D indoor scene semantic seg- mentation,","venue":null,"work_id":"ac0f2354-abd4-40d0-bbe8-f07437928312","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.403214Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:3cfd5aedc65f223448033c6dfd121a9dae6b89ef5c290e9add978d0ba73353cb","observation_id":"8ec85c31-fd83-4c95-98e6-7e5ab68fbbb7","resolution":{"observed_at":"2026-08-06T17:48:26.124100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:25.732487Z","title":"Feature contrast difference and enhanced network for rgb-d indoor scene classification in internet of things,","venue":null,"work_id":"45e32228-f71f-4d5b-b67e-b01e8c2c4e0a","year":2025},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.493960Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f01ca6321bf4abab515392999533f2d85d9c93a9b95f5663a6b2560ad4f58639","observation_id":"2455e7cd-63ed-402c-a568-3b68fc34bbfe","resolution":{"observed_at":"2026-08-06T17:48:25.851072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:25.571105Z","title":"An efficient k-means clustering algorithm: Analysis and implementation,","venue":null,"work_id":"0df6d048-a852-4b60-83e9-d0c71144d6ce","year":2002},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.608343Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:b43697285d75a5e03e1426cd8087b848c5bb22279147d25af49c60de32474493","observation_id":"e3355632-b8bd-4f76-ac8e-67b1b47badc0","resolution":{"observed_at":"2026-08-06T17:48:25.639833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:29.828602Z","title":"Self-weighted multiview clustering with multiple graphs","venue":null,"work_id":"c6c02a20-4509-4d53-871f-edffbf0b81bf","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.686602Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:abb47e647d435109462aa3837fe7837db039ee9f99fb7a597d1cdb3aaec82e5c","observation_id":"cc540381-bfd4-49b2-8292-8355262b8c50","resolution":{"observed_at":"2026-08-06T17:48:29.926185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:25.385256Z","title":"Multi-view clustering and semi-supervised classification with adaptive neighbours,","venue":null,"work_id":"719ab95d-1409-4901-a761-dd5721f484bc","year":2017},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.762791Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:21fbe41967e503ec36372fd191938b19d29d1448eb85437fd6b4f63bbd681561","observation_id":"5c3cf413-555d-46b5-a7bb-673d1980bc62","resolution":{"observed_at":"2026-08-06T17:48:25.486531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:25.161888Z","title":"Multiview consensus graph clustering,","venue":null,"work_id":"fa9c4249-f9d2-4311-9a99-0048657e469b","year":2018},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.831057Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:ec25ce5a843cd5fb7ac26e2d03c1fadf9192df92433fa832ebeff5a371666e85","observation_id":"bcde998e-a90c-4540-9ccf-d5841aab0ffb","resolution":{"observed_at":"2026-08-06T17:48:25.280315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:24.876877Z","title":"Binary multi- view clustering,","venue":null,"work_id":"b8be9871-98cd-428f-bd42-4380c423d528","year":2018},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.899554Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:33c0b555a54753d5593e1f03e2d8c9312099ac9dbce337ed5ed1b3facbfedb7c","observation_id":"307427cf-36d1-44cd-8b01-c37eaf2b4539","resolution":{"observed_at":"2026-08-06T17:48:25.035542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:24.717922Z","title":"Multi-view subspace clustering with intactness-aware similarity,","venue":null,"work_id":"e1d1d430-6fb8-48dd-b2c4-3e0c9785122f","year":2019},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:22.967691Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:8b3896f9cc1e9d9789a1f4d020513cfce700a787f400591cfc03c433b991ac64","observation_id":"df973c27-28a9-4cfa-8a06-316e9d53828d","resolution":{"observed_at":"2026-08-06T17:48:24.785009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:24.570455Z","title":"Multi-view clustering based on view-attention driven,","venue":null,"work_id":"eb1f25b3-245a-4761-ab66-04bdf4f046b1","year":2023},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:23.147676Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:8bbdbbaeab9a13f83c75fc27b8b84ff2c2319b700dc79e31bdacb318b066797d","observation_id":"2e4549f2-ff95-475b-8911-5a32f70a57ed","resolution":{"observed_at":"2026-08-06T17:48:24.673930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15691","last_updated":"2024-01-28T16:30:13Z","snapshot_observed_at":"2026-07-06T17:21:30.030202Z","submitted_at":"2024-01-28T16:30:13Z","title":"One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering","version":1},"cited_work":{"arxiv_id":"2401.15691","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.15691","snapshot_observed_at":"2026-08-06T17:48:23.447733Z","title":"One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering","venue":"cs.LG","work_id":"4fe9904d-1355-422b-8ba3-7c9fcf66c7ad","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:23.233979Z"},"links":{"cited_paper":"/paper/2401.15691","citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:f6aad256130a1b1b4cf26cbcec34bf7ee86f9dd92283b2d81dbd6e474d392275","observation_id":"ee2a36c0-e7dc-4555-9f2b-98d12ef32572","resolution":{"observed_at":"2026-08-06T17:48:23.561827Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:48:23.920212Z","title":"Deep Incomplete Multi-View Learning Network with Insufficient Label Information,","venue":null,"work_id":"716e85c5-b529-4afc-81b8-b3418bd79ecf","year":2024},"citing_paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T17:48:23.292239Z"},"links":{"citing_paper":"/paper/2507.09980"},"observation_digest":"sha256:26fd9e4ec1501635f4d2db8520844c135aa7e47bb6e8682dedd0091f687a7648","observation_id":"ec2cbf74-2d3c-46d9-922a-18bd8322f5b4","resolution":{"observed_at":"2026-08-06T17:48:24.482279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.09980","last_updated":"2025-07-14T06:55:32Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T17:40:05.627053Z","submitted_at":"2025-07-14T06:55:32Z","title":"Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":66},"total_outbound_references":77},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2507.09980."}