{"as_of":"2026-08-20T04:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9fe940813602c7ccea6281a508c8276a678c861ea032d41e98a9f965cdc6386a","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T19:21:25.144911Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2512.02076/citation-record","integrity":"/paper/2512.02076/integrity","json":"/paper/2512.02076/citation-record.json","paper":"/paper/2512.02076"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T19:21:25.052247Z","title":"Geometric means in a novel vector space structure on symmetric positive-definite matrices","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.052247Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:6b2aecf1c918237482ffc8e50d2f5156f2cbc2245f7e797960b0494dcfd17c31","observation_id":"7e9817ad-9ceb-493f-ad01-5a3242d25d0a","resolution":{"observed_at":"2026-08-03T19:21:25.052247Z","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":"2026-08-03T19:21:25.056535Z","title":"Prediction by supervised principal components","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.056535Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:06f71fe1a40bcb790442277436ea3fd6a0bfc76bd1496e9aaac25bdea24a3c31","observation_id":"6aa73ff5-0046-4202-8189-8e6495624109","resolution":{"observed_at":"2026-08-03T19:21:25.056535Z","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":"2026-08-03T19:21:25.059933Z","title":"Multimodal machine learning: A survey and taxonomy","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.059933Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:f76035d06cef3fde8bfd2edd381115f9b70d080f2e1f5baab0ad1673231577ea","observation_id":"df082aa0-f995-489e-b8a3-5b602b3b6303","resolution":{"observed_at":"2026-08-03T19:21:25.059933Z","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":"2026-08-03T19:21:25.063789Z","title":"Geometry of the space of phylogenetic trees","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.063789Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:b689cec632787788e78e017169e746db12ee7e257e75669a1863ccccd51a2466","observation_id":"e78220b4-e8bf-46d2-9cf8-98cb7ab0385f","resolution":{"observed_at":"2026-08-03T19:21:25.063789Z","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":"2026-08-03T19:21:25.067671Z","title":"Randomprojectionindimensionality reduction: Applications to image and text data, in: Proceedings of the 7thACMSIGKDDInternationalConferenceonKnowledgeDiscovery and Data Mining, ACM","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.067671Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:616ade4ebf0ddd71d99a00ce7d9f29101b294ec28bbc7e09cc322df19d0256c0","observation_id":"4fb3bc11-641a-4367-b65c-8bf3ae3c75ed","resolution":{"observed_at":"2026-08-03T19:21:25.067671Z","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":"2026-08-03T19:21:25.070985Z","title":"Tools for fast metric data search in structural methods for image classification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.070985Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:39ba5a341e50acf7a555ef5b16b3b698df51ee675c084fd1517cd547f060c069","observation_id":"c22e1bbc-ab0a-4ae7-af1d-5e93fa5de753","resolution":{"observed_at":"2026-08-03T19:21:25.070985Z","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":"2026-08-03T19:21:25.074938Z","title":"Computational topology for data analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.074938Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:dc2d96d353cfe2fd822b043cd37fdaa703056ad5bd9e06a235451efab6932c85","observation_id":"6a1ef21b-bae3-42ca-bdb9-3193aa0d69ea","resolution":{"observed_at":"2026-08-03T19:21:25.074938Z","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":"2026-08-03T19:21:25.078006Z","title":"Non-euclideanstatistics for covariance matrices, with applications to diffusion tensor imaging","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.078006Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:4f7f40da9afe1223bec3c2edd0d965964f591b0f97b2aa8403fcef863fd89a03","observation_id":"7c868acd-208c-4db8-af06-4f78a4563d9a","resolution":{"observed_at":"2026-08-03T19:21:25.078006Z","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":"2026-08-03T19:21:25.081274Z","title":"Modelingtime-varyingrandomobjects anddynamicnetworks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.081274Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:c5dfeefdac25cfdba50fb009f620f409ed9550abe3eea61fd33f639909bf371e","observation_id":"043c264a-e3fb-4441-aac4-97e85dfe925a","resolution":{"observed_at":"2026-08-03T19:21:25.081274Z","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":"2026-08-03T19:21:25.084421Z","title":"Regression for non-euclidean data using distance matrices","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.084421Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:beab2b712d66638891cca3534b6a4680b5b4940b6cfdec597ef08d7588219fd9","observation_id":"75390166-ec9b-4e25-aac3-2ff8249a0da4","resolution":{"observed_at":"2026-08-03T19:21:25.084421Z","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":"2026-08-03T19:21:25.087611Z","title":"Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.087611Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:74011196cea0192186c735276c89c9b4621f31bdf39662eb2d0057712c85ccf5","observation_id":"9a790cc6-0412-40fc-a1f0-9594c77fc9c7","resolution":{"observed_at":"2026-08-03T19:21:25.087611Z","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":"2026-08-03T19:21:25.090745Z","title":"Robust nonparametric regression with metric-space valued output","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.090745Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:fa99fd2dc1a12c7de222b6d92d4b34584120b0727a0955fdd0272f35f613449c","observation_id":"f8bdd923-85b4-4925-9fa5-744b6e419e26","resolution":{"observed_at":"2026-08-03T19:21:25.090745Z","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":"2026-08-03T19:21:25.094095Z","title":"beta-vae: Learning basic visual concepts with a constrained variational framework, in: International Conference on Learning Representations (ICLR)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.094095Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:87ea2b22ec142653c391421ea5c0df99864ff0bd439b42c8ca0c8d0577e47b9f","observation_id":"0b2e69a9-1c6c-4e5b-ae01-28aaa3f551aa","resolution":{"observed_at":"2026-08-03T19:21:25.094095Z","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":"2026-08-03T19:21:25.097235Z","title":"Areviewonevaluationmetricsfor data classification evaluations","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.097235Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:70ce8ee35e94afb2ccff4e0e90e29afb94a65201f552faf259e23f14b5cb6829","observation_id":"5bbb0515-d771-4991-afb4-5e932ca5aad8","resolution":{"observed_at":"2026-08-03T19:21:25.097235Z","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":"2026-08-03T19:21:25.100643Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.100643Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:8dec7bec857bab4d93c7707703f0921a75c7719f53636894428c98be971cf7dd","observation_id":"b27dcc01-0de1-42be-a8a6-efa493482efc","resolution":{"observed_at":"2026-08-03T19:21:25.100643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04003","last_updated":"2025-06-04T14:31:55Z","snapshot_observed_at":"2026-08-07T10:47:15.125571Z","submitted_at":"2025-06-04T14:31:55Z","title":"Observable Covariance and Principal Observable Analysis for Data on Metric Spaces","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.04003","snapshot_observed_at":"2026-08-03T19:21:25.106917Z","title":"Observable covariance and principal observable analysis for data on metric spaces","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.106917Z"},"links":{"cited_paper":"/paper/2506.04003","citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:a2ccad8f53701bcea880eec532c8bcb9bae7912b28927dcccde333bcfe740c23","observation_id":"15b79ec3-91cb-4cae-9212-a7583d854462","resolution":{"observed_at":"2026-08-03T19:21:25.106917Z","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":"2026-08-03T19:21:25.110362Z","title":"Scaffold: Stochastic controlled averaging for federated learning, in: Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.110362Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:4d5bbb3972438fe0c10dd44fe0a90b11e182f81ecb2a994656e5e07d3f67dc99","observation_id":"408ccde4-087c-4587-8b70-0ee8f9b52d4f","resolution":{"observed_at":"2026-08-03T19:21:25.110362Z","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":"2026-08-03T19:21:25.113493Z","title":"Overcom- ing catastrophic forgetting in neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.113493Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:95b0fb7274fdcda87edabe1c14668e2a0b30726cdf26498c305872714ef7b8fa","observation_id":"0ed5c128-ae65-4da8-8c79-39e364f0054d","resolution":{"observed_at":"2026-08-03T19:21:25.113493Z","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":"2026-08-03T19:21:25.116731Z","title":"Federated optimization in heterogeneous networks, in: Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.116731Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:fd6cb8f638559170206adcfe79419b9459d58f20c8f886b28d87d89427dc598f","observation_id":"22f08ee3-7511-4b10-9f26-9f4ed16670cb","resolution":{"observed_at":"2026-08-03T19:21:25.116731Z","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":"2026-08-03T19:21:25.120019Z","title":"Eclipse: Efficient long-range video retrieval using sight and sound, in: European Conference on Computer Vision, Springer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.120019Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:cfdf22585c5f5602d4b7cf89879367a6cc7f0fa5e7df87bae4ec3c6932ac5311","observation_id":"549ea2ff-c525-4c8f-a0ce-8b286eaee5f6","resolution":{"observed_at":"2026-08-03T19:21:25.120019Z","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":"2026-08-03T19:21:25.122858Z","title":"Object oriented data analysis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.122858Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:6b21ee10ca163b33b20237f8337ccedd22e56b948de689df40a63c67497bb011","observation_id":"c84765c7-e980-48e5-900e-92040f47ad8f","resolution":{"observed_at":"2026-08-03T19:21:25.122858Z","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":"2026-08-03T19:21:25.125873Z","title":"Communication-efficient learning of deep networks from decentralized data, in: Proceedings of the 20th International ConferenceonArtificialIntelligenceandStatistics(AISTATS),PMLR","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.125873Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:b575990e09aae256b94c9c8b49740d79d5f14a7f8666125c4ac43e6816ef35c7","observation_id":"3348e3e9-dab9-41d9-a0d6-d6123b42c7a1","resolution":{"observed_at":"2026-08-03T19:21:25.125873Z","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":"2026-08-03T19:21:25.128661Z","title":"Functionalanalysis:anintroductiontometricspaces, Hilbert spaces, and Banach algebras","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.128661Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:61af2f1c40250f72ed233c8434b774eab8d5e67d13a2227cf3024d7d10c992a7","observation_id":"526d4b15-de5d-4270-9991-b626bc3f4605","resolution":{"observed_at":"2026-08-03T19:21:25.128661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-08-14T18:53:38.574749Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-03T19:21:25.131978Z","title":"Representation learning with contrastive predictive coding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.131978Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:09d5fe9d851e23698f4cac557f111ccde84f35faf075cf33d04ea9198fde0018","observation_id":"5e58fbc7-02f5-4454-9b34-cff5d9e95cf9","resolution":{"observed_at":"2026-08-03T19:21:25.131978Z","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":"2026-08-03T19:21:25.135464Z","title":"Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.135464Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:7caadda548631f0c598dafe02b249b82ff45495688aff6718afa5b55a268c07e","observation_id":"abdb9329-eca6-4206-9748-524a10c85450","resolution":{"observed_at":"2026-08-03T19:21:25.135464Z","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":"2026-08-03T19:21:25.138511Z","title":"Aneffective multimodal image fusion method using mri and pet for alzheimer’s disease diagnosis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.138511Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:473d748b51ceba4a0bd24c8bf90101429aba75f5c4e8dd8e36e18d68ca8ddfbd","observation_id":"53808e1c-c30d-49fe-8608-37b99482ad2d","resolution":{"observed_at":"2026-08-03T19:21:25.138511Z","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":"2026-08-03T19:21:25.141672Z","title":"Remaining useful life prediction of iiot-enabled complex industrial systems with hybrid fusionofmultipleinformationsources.IEEEInternetofThingsJournal 8, 9045–9058","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.141672Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:ee57120d59cdfc4d2c535cbb151ad535a3e5062dda5e55794b2df13c08c5948e","observation_id":"43e6acf3-481b-47bb-b2c5-e71a3315af43","resolution":{"observed_at":"2026-08-03T19:21:25.141672Z","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":"2026-08-03T19:21:25.144911Z","title":"Local polynomial regression for symmetric positive definite matrices","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.144911Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:04be778322b58a850526051c0769a1b5f1e64f0a86a31c48815b3e1cb0518b86","observation_id":"b5d76b52-e22c-4404-861d-2555f2a006da","resolution":{"observed_at":"2026-08-03T19:21:25.144911Z","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":"2026-08-03T19:21:25.103853Z","title":"Foundations and Trends in Machine Learning 14, 1–210","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T19:21:25.103853Z"},"links":{"citing_paper":"/paper/2512.02076"},"observation_digest":"sha256:4ec9a1d0013fe3a6f61fbc8408e433bef51a0a7e5afeb57ceb827e5d340c8abd","observation_id":"147de170-0a1f-479b-a34d-a21e9c1ee0bb","resolution":{"observed_at":"2026-08-03T19:21:25.103853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.02076","last_updated":"2026-07-08T17:04:31Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T12:44:33.700085Z","submitted_at":"2025-11-30T17:13:35Z","title":"FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":29},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2512.02076."}