{"as_of":"2026-08-06T03:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:902db86cd9828da2d5f51726c4dfc8f8426d1668454681ccba6c84728ff5e93e","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T08:30:19.662927Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2604.23112/citation-record","integrity":"/paper/2604.23112/integrity","json":"/paper/2604.23112/citation-record.json","paper":"/paper/2604.23112"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2208.09399","last_updated":"2023-05-06T12:43:37Z","snapshot_observed_at":"2026-07-06T13:43:32.141581Z","submitted_at":"2022-08-19T15:29:43Z","title":"Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models","version":3},"cited_work":{"arxiv_id":"2208.09399","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2208.09399","snapshot_observed_at":"2026-07-02T12:46:56.991966Z","title":"Diffusion-based time series imputa- tion and forecasting with structured state space models","venue":null,"work_id":"7a3b9cf6-569d-45bd-a88f-3cf213b8f7cd","year":2022},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2208.09399","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:c6f7ecfae6af318f48012e558b289b85a1896fb411275bf61b7a88e92b0e6089","observation_id":"c4469811-5a6d-4ffd-8571-8809bbd59811","resolution":{"observed_at":"2026-05-11T20:36:09.713419Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.01600","last_updated":"2023-06-21T07:13:14Z","snapshot_observed_at":"2026-08-04T01:27:24.757200Z","submitted_at":"2021-03-02T09:55:05Z","title":"Missing Value Imputation on Multidimensional Time Series","version":3},"cited_work":{"arxiv_id":"2103.01600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.01600","snapshot_observed_at":"2026-07-02T12:46:56.989256Z","title":"Missing value imputation on multidimensional time series","venue":null,"work_id":"510c2ffb-3ec6-4b40-871f-fcc941c8304f","year":2021},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2103.01600","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:024d593cf4185662dfdaedcf7b4337728b17dc9c8d1bf84a9b03ecbf4e4917e4","observation_id":"db5e885e-b2a7-4e31-8eb2-4adf4cd42d59","resolution":{"observed_at":"2026-05-11T20:36:09.661647Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-05T15:23:02.346614Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.04948","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-07-04T20:30:07.231037Z","title":"O., Pfister, T., Zheng, Y., Ye, W., and Liu, Y","venue":null,"work_id":"1fc8e9e1-1245-4fd6-b2c5-c0e1e607560f","year":2023},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:b8cf4427bea8ae925c721a7c9eb536e8e4c4f16df27792089d70d37c093ce9f9","observation_id":"f5906bd9-77a5-4dfb-81a5-6de4cc45eed0","resolution":{"observed_at":"2026-05-11T20:36:09.707554Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02322","last_updated":"2025-02-11T00:53:58Z","snapshot_observed_at":"2026-08-05T20:17:26.313880Z","submitted_at":"2024-09-03T22:31:57Z","title":"TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model","version":2},"cited_work":{"arxiv_id":"2409.02322","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.02322","snapshot_observed_at":"2026-07-04T12:59:52.916170Z","title":"Timedit: General-purpose diffusion transformers for time series foun- dation model","venue":null,"work_id":"1332736b-ef04-4359-914f-b0f2c98be306","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2409.02322","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:1a5b193b1d8ccadf488f27e0297468669ec293b99523b27abd0358a156b43c73","observation_id":"0a4c68a7-1e11-4789-80c3-31eb5c098683","resolution":{"observed_at":"2026-05-11T20:36:09.697941Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mul- timodal federated learning: A survey.Sensors, 23(15):6986","venue":null,"work_id":"8324baed-34f0-43a6-b43a-89840ecb56c3","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:25a7659ff4f3a387e9e999b0bad84e8e67ad13493df68c833abfcef6fe326bc4","observation_id":"b60994cf-f2ed-40dc-963b-b08aebfd2280","resolution":{"observed_at":"2026-05-26T16:57:39.436284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Feddat: An approach for foundation model fine- tuning in multi-modal heterogeneous federated learning","venue":null,"work_id":"5c7c18d7-d547-42e7-8439-03cc5a6f75d7","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:56a4ab43eb292d807a9b5afaff69638744b4c1ff99af0e332247d3231948f307","observation_id":"fc28eb6c-ef1e-49eb-bfb8-6ef1310fbd96","resolution":{"observed_at":"2026-05-26T16:57:39.429758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fedmsplit: Correlation- adaptive federated multi-task learning across multimodal split networks","venue":null,"work_id":"fbc0ab96-897d-4a56-8f15-5c1371cf5ded","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:e372761154ddaa9ec6619cec04615bfcff48d614e7bb43680b97321a79b9eec0","observation_id":"2b971266-bca7-485e-b17b-ee1eb9629f93","resolution":{"observed_at":"2026-05-26T16:57:39.477740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Probabilistic conformal distilla- tion for enhancing missing modality robustness.Advances in Neural Information Processing Systems, 37:36218–36242","venue":null,"work_id":"b57f6474-5922-40dd-9bb7-abe4120a9919","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:4724169bdf1750a4fdd35204e303c94a5dbd84e197fa75aa4a92c9a0d34a551f","observation_id":"31b2deb7-3a8f-4935-8201-da8a03dc1810","resolution":{"observed_at":"2026-05-26T16:57:39.402784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Dam: Towards a foundation model for time se- ries forecasting","venue":null,"work_id":"6fae5f22-c38f-453b-b10e-785ab26cc30e","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:f88f56384a63ad2fca864c028e5457b54bfc20d5bd610e163a010deecd28bf48","observation_id":"5e274c98-9fce-46d2-a621-5067d9b3d993","resolution":{"observed_at":"2026-05-26T16:57:39.448773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A decoder-only foundation model for time-series forecasting","venue":null,"work_id":"d5b87409-9b6b-4c61-9ec0-85fef66300fc","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:22a05abbff2ec37783f7c8ac0f218bd59637d21e6e9d9626ab4072918f161a2d","observation_id":"e87a94a6-5e86-4637-ad43-0dc70f8d0a94","resolution":{"observed_at":"2026-05-26T16:57:39.395983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.17039","last_updated":"2025-06-20T14:48:42Z","snapshot_observed_at":"2026-07-06T21:45:16.289531Z","submitted_at":"2025-06-20T14:48:42Z","title":"LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation","version":1},"cited_work":{"arxiv_id":"2506.17039","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.17039","snapshot_observed_at":"2026-07-02T12:46:56.996675Z","title":"Lscd: Lomb-scargle conditioned diffusion for time series imputa- tion","venue":null,"work_id":"e9317a71-a572-4850-9bd1-c846eec674d0","year":2025},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2506.17039","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:aae0979bd10efa24fe1fe43c09ede7a4b881ef48accdb6174287041a760c13d4","observation_id":"4d67c6e1-a160-45b5-9bb5-41e6f41c0a14","resolution":{"observed_at":"2026-05-11T20:36:09.672455Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Gp-vae: Deep probabilistic time series impu- tation","venue":null,"work_id":"55ff952c-12d4-43f6-9ef0-4f345e2ded73","year":2020},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:b37432bf8e59d8056d870e5708b4490a4f781289395ad041ec05aa279bb1c060","observation_id":"2bff4b16-efa6-4f7e-92ed-a116d2a285a3","resolution":{"observed_at":"2026-05-26T16:57:39.392106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03885","last_updated":"2024-10-10T15:37:45Z","snapshot_observed_at":"2026-08-05T08:29:54.807725Z","submitted_at":"2024-02-06T10:48:46Z","title":"MOMENT: A Family of Open Time-series Foundation Models","version":3},"cited_work":{"arxiv_id":"2402.03885","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03885","snapshot_observed_at":"2026-07-03T17:38:43.284760Z","title":"Moment: A family of open time-series foundation models","venue":null,"work_id":"26ab8b80-a36b-488c-876d-c5b25358a5b7","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2402.03885","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:a7b816dfd612fb2ec6f59797356da548c526145a3bcbb812ea73a3ac0b32ba84","observation_id":"6259f46f-07de-4fbc-848d-99712e3f11ca","resolution":{"observed_at":"2026-05-11T20:36:09.655524Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fusemoe: Mixture-of-experts transformers for flexi- modal fusion.Advances in Neural Information Processing Systems, 37:67850–67900","venue":null,"work_id":"3a6f0dd7-3f0e-472d-8d95-7af0b7ec7edd","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:2c0a2ee0229ef100071a3baa54d5b75998d95bf119566dd6ec87cc7f39ba3ff4","observation_id":"96e315b7-48e4-4cee-8b0d-fc5b69c26fc3","resolution":{"observed_at":"2026-05-26T16:57:39.474244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851","venue":null,"work_id":"06323f29-d4c8-4287-a6d6-57185f68eb04","year":2020},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:94a55355ba5b2af8438641369a8eab2bd12f7c04ecb96f74b27b7e97ad18b038","observation_id":"8d213379-1b92-491d-9227-871abeaf31f0","resolution":{"observed_at":"2026-05-26T16:57:39.470710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Multimodal federated learning: Concept, methods, applications and future directions.Information Fusion, 112:102576","venue":null,"work_id":"3c2a0db7-a358-4ae6-95d5-3c6f472afbf1","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:dca706c53d955eb33750b0b59980dd5f94cf6be4583705060c61cfe63ceb282d","observation_id":"121b45b9-d4b5-49e4-9a94-14130e1b7d16","resolution":{"observed_at":"2026-05-26T16:57:39.439561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"MIMIC-IV","venue":null,"work_id":"33164490-53eb-42cf-9144-077758685890","year":2020},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:708ee9203dd162468b523558c7f56f52b876402ab004da9ece57f4eabd2ac1a1","observation_id":"6d9d4e01-13b8-49c9-9ce1-8e2c812bdfb7","resolution":{"observed_at":"2026-05-26T16:57:39.399217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Sleep-edf database expanded (version 1.0.0)","venue":null,"work_id":"360c4cb9-ad54-4511-9b2b-203177517baf","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:ffc8ce84edc0fe7eb607219f936b97341a9580d54e26585b3debb36d3add823e","observation_id":"e402e8dc-0fba-4b63-bb93-8a664e5c6c54","resolution":{"observed_at":"2026-05-26T16:57:39.442882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Cyin: Cyclic informative latent space for bridging complete and incomplete multimodal learning","venue":null,"work_id":"a0da3c51-6102-4e6c-9de1-21310301531e","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:71c7963288bb89ea97ce0f2ceeed6e529d281976035dc4463e39f09a3a43244f","observation_id":"f8b75ade-93e7-4ebf-b2c6-aeebc3b24962","resolution":{"observed_at":"2026-05-26T16:57:39.388520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02368","last_updated":"2024-10-18T03:19:55Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:55:55Z","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","version":3},"cited_work":{"arxiv_id":"2402.02368","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02368","snapshot_observed_at":"2026-07-04T13:19:50.478446Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":"fb98218c-9996-4866-9703-9494d652ddd5","year":2026},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2402.02368","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:127ae7568ef9e764acd274a7552a5b1211672d2c84f0d1f9dc4f376b75c319ad","observation_id":"2a25ec38-0d88-40e2-ba3d-2009aeaf3e16","resolution":{"observed_at":"2026-05-11T20:36:09.690612Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mul- tivariate time series imputation with generative adversarial networks.Advances in neural information processing systems, 31","venue":null,"work_id":"3c22dfdd-4fcb-437c-9133-76214563000a","year":2018},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:5f54eb8f63becf5ea6c14284cd2c978f7ba2a745376ede3613ce4100ac1a8cfc","observation_id":"9c9270e9-2dbd-427f-bc27-2be0ba5de809","resolution":{"observed_at":"2026-05-26T16:57:39.459020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen, and Phi Le Nguyen","venue":null,"work_id":"6d8ceb2a-1d25-468c-9085-632291b2a883","year":2025},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:5956cdd5aad6f6d1939fd1fcd8f5deefe8ca591003d497488ed24023bc3e056d","observation_id":"5f5a8683-2663-467c-9178-2ce32de11049","resolution":{"observed_at":"2026-05-26T16:57:39.416905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fedmac: Tackling partial-modality missing in federated learn- ing with cross-modal aggregation and contrastive regulariza- tion","venue":null,"work_id":"2efd5e1e-d0d7-487e-943f-62943cd3023a","year":null},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:98c401e3fcbf66d00375f7bc9cda3e04caa01490a0a0c32e2db4638a4c3d5155","observation_id":"1a6b582f-d91a-4d54-96d1-190f54de8538","resolution":{"observed_at":"2026-05-26T16:57:39.455709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fedmm: Feder- ated multi-modal learning with modality heterogeneity in computational pathology","venue":null,"work_id":"78b7eae7-53a0-41e8-8155-4f4fc34419c3","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:596160717d2b35001f9c187388ac8e6ddb3627af394c990d68aaf9d6713b8b94","observation_id":"49945ec3-0558-4050-86ec-d53a590b25f3","resolution":{"observed_at":"2026-05-26T16:57:39.406165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A contrastive learning and graph-based approach for missing modalities in multimodal federated learning","venue":null,"work_id":"6e2e7454-8a87-436c-a004-e28ba2ae3d46","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:90b93dbe56d51c462a9b3f1b2f2e39217cfb4810caa48c9f896078dbde614887","observation_id":"b57fdd43-86e2-4615-b894-354f62c80cb9","resolution":{"observed_at":"2026-05-26T16:57:39.384103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Federated prompt-tuning with heterogeneous and incomplete multimodal client data","venue":null,"work_id":"d0214379-bb34-444b-9b14-b069a1aeca31","year":2025},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:14bbee66a80167fb15c16b47437edb9007d27b11e5c4c8ec0faefe99b97b754a","observation_id":"c1c47557-cf4c-4973-aca5-e64ebcad8a0e","resolution":{"observed_at":"2026-05-26T16:57:39.483549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08278","last_updated":"2024-02-08T05:49:04Z","snapshot_observed_at":"2026-07-06T16:31:52.204065Z","submitted_at":"2023-10-12T12:29:32Z","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2310.08278","doi":"10.48550/arxiv.2310.08278","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.08278","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting","venue":"arXiv (Cornell University)","work_id":"0f9ebc03-9ba2-4330-ac18-0c9fb499c426","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2310.08278","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:e46f2798b71331c2a48f80c555272729fc9cb4640d54ccfedc113251f62459a7","observation_id":"9f45ae73-b525-4a40-986f-628b8e7627ac","resolution":{"observed_at":"2026-05-11T20:36:09.681862Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-07-06T05:27:13.416519Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":"1701.06538","doi":"10.48550/arxiv.1701.06538","metadata_source":"pith","pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","venue":"cs.LG","work_id":"2c6b3f6d-54e4-4df7-baa7-475a490799af","year":2017},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:527bcafc8efb6ccd0c99c7b7e3a540642587050441f86b4eacdf4970a6f2a0dd","observation_id":"a503122e-a97b-4005-aea6-7e33a2346e2e","resolution":{"observed_at":"2026-05-11T20:36:09.643240Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:24.174744+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:24.174744+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Csdi: Conditional score-based diffusion models for probabilistic time series imputation.Advances in neural in- formation processing systems, 34:24804–24816","venue":null,"work_id":"af0b9a97-6acb-4e57-b89d-ede7da2c575e","year":2021},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:f3967a2e977082a5c1f70f590b8afd52c3eea457677faaf86cef151475797dec","observation_id":"5451b363-931e-4ef9-b7ea-f906efd4535f","resolution":{"observed_at":"2026-05-26T16:57:39.409944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"PTB-XL: A large publicly available elec- trocardiography dataset.Scientific Data","venue":null,"work_id":"0b0ca78c-c7b0-444f-8d8a-9eaf3eab1413","year":2020},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:4fb7888277b017ca7df7a3a479a21c1b10cc31e61c8775c9be466c37c242d469","observation_id":"7b25993c-f975-4f60-bc8d-19ad6737fbb9","resolution":{"observed_at":"2026-05-26T16:57:39.432773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04059","last_updated":"2025-05-20T10:57:10Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T15:03:53Z","title":"Deep Learning for Multivariate Time Series Imputation: A Survey","version":3},"cited_work":{"arxiv_id":"2402.04059","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04059","snapshot_observed_at":"2026-07-02T12:46:56.964740Z","title":"Deep learning for multivariate time series imputation: A survey","venue":null,"work_id":"11b026f2-6a6b-465c-9119-fe4672a51f85","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2402.04059","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:b839cb0d2228f393fa36c05d4360a6f8c56f362cbac1be6b0055c5c4ec2598c2","observation_id":"5b77d142-847c-43e3-8d61-3db9c64935d9","resolution":{"observed_at":"2026-05-11T20:36:09.648681Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Distribution- consistent modal recovering for incomplete multimodal learn- ing","venue":null,"work_id":"9cc60ab8-1656-4b86-83af-1e8ada20d59b","year":2023},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:a5e0e16879b817fd9b746e58a9d9938452da732d1017cdd65757e1e8ed1de5ce","observation_id":"ba228601-692d-4fe7-b056-dea9c6f4b696","resolution":{"observed_at":"2026-05-26T16:57:39.480715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02186","last_updated":"2023-04-12T02:34:03Z","snapshot_observed_at":"2026-07-06T13:59:54.436175Z","submitted_at":"2022-10-05T12:19:51Z","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","version":3},"cited_work":{"arxiv_id":"2210.02186","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.02186","snapshot_observed_at":"2026-07-10T10:57:05.501091Z","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","venue":"cs.LG","work_id":"f6f0718a-d2b0-4bd1-969b-37013ffd088b","year":2022},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2210.02186","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:ca3952cba61a03dbef3baf1ad39d5785197cf5a8910f385e6ac6317f3bc484dd","observation_id":"bc5eb00e-b87f-4a7d-8aeb-db9e60241156","resolution":{"observed_at":"2026-05-16T19:09:17.286568Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A unified framework for multi-modal federated learning","venue":null,"work_id":"3eb5f615-f469-4bbd-9305-290efded0fc7","year":2022},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:eb84003a23df43567098c2b10d88993fe3e145947c0f56c7791af99ca0823166","observation_id":"503329cd-1b19-46db-9260-926929442148","resolution":{"observed_at":"2026-05-26T16:57:39.452087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Promptcast: A new prompt- based learning paradigm for time series forecasting.IEEE Transactions on Knowledge and Data Engineering, 36(11): 6851–6864","venue":null,"work_id":"9963feae-5fac-4a61-9294-a514ccf41384","year":2023},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:81d3788699298c74719fdd0591a6324bdd3587e85d48d3de97ed7dd1ed3ce4a1","observation_id":"334d6f94-f81b-438a-ad34-4b9c6896a8ba","resolution":{"observed_at":"2026-05-26T16:57:39.380203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Frequency-aware generative models for multivariate time se- ries imputation.Advances in Neural Information Processing Systems, 37:52595–52623","venue":null,"work_id":"cab3dced-dd9d-41a2-9092-3fcd48d7a84a","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:d00bba1544ad0b069022cb118a5d5a1bc78bb7c9d088985e2739aceede1641f8","observation_id":"87017466-eb5c-458f-986e-8866bff216dc","resolution":{"observed_at":"2026-05-26T16:57:39.419942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Estimating missing data in temporal data streams using multi- directional recurrent neural networks.IEEE Transactions on Biomedical Engineering, 66(5):1477–1490","venue":null,"work_id":"105fb68e-aa4c-4921-bd30-8f78a5b6bc06","year":2018},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:0296347b56626c251c6ff6df28838b982e0e12fed49733dc3bb34a7e66e3fe23","observation_id":"48863e58-078a-44e9-9549-f6b01ff7e541","resolution":{"observed_at":"2026-05-26T16:57:39.423223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.08888","last_updated":"2023-05-06T02:26:32Z","snapshot_observed_at":"2026-07-06T14:52:56.806494Z","submitted_at":"2023-02-17T14:17:44Z","title":"Multimodal Federated Learning via Contrastive Representation Ensemble","version":3},"cited_work":{"arxiv_id":"2302.08888","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.08888","snapshot_observed_at":"2026-07-01T09:45:40.597440Z","title":"Multimodal feder- ated learning via contrastive representation ensemble","venue":null,"work_id":"473acc19-5e65-4d72-88e2-a39cdf85bf0d","year":2023},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"cited_paper":"/paper/2302.08888","citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:b4ac84907f5e4a916b42f6aa6aa4b31f4cd0ef89bb84d8ff7bf6d8366888bbeb","observation_id":"d9307656-0ece-4ab7-acc2-5a8424ae0e5f","resolution":{"observed_at":"2026-05-11T20:36:09.732659Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust multimodal federated learning for incomplete modalities.Computer Communications, 214:234–243","venue":null,"work_id":"4358ea8d-dc18-4ab3-9105-2f3a53379b0f","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:40bc792d1a5075aeb38e2005986f7557be78d86c35d9382a8bfc130f1a700b38","observation_id":"b29f24ac-0102-4869-bcaa-db5ccdd038c8","resolution":{"observed_at":"2026-05-26T16:57:39.486158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Flex-moe: Modeling arbitrary modality combination via the flexible mixture-of-experts.Advances in Neural Information Processing Systems, 37:98782–98805","venue":null,"work_id":"d63efeae-81d4-4a1e-979d-1fbd5f64d1d4","year":2024},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:b676ede34a74cfa6788ef2fd3152680177459dfbcdc792178298c7f0f005bfe9","observation_id":"b2e2a36b-bc88-481f-8293-afbecec6ec49","resolution":{"observed_at":"2026-05-26T16:57:39.463811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"900ffa1e-9ef4-40f1-b207-447abad2668a","year":2001},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:818cbbb0c1a56f52bd1703c23d10fbdf7cc1edb2b5a75b8556008b449e0b3833","observation_id":"a47ebe09-7279-4c9d-a5d7-8e9f4535ec73","resolution":{"observed_at":"2026-05-26T16:57:39.426438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Mul- timodal federated learning on iot data","venue":null,"work_id":"1907c9c1-98f7-4a19-9c4a-1d3ac982f4c2","year":2022},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:71ca0b2ed2babb64352d475d83c699adf6b0c8da89a7a62c2886de7b74fb7469","observation_id":"aa56d4c7-8ee7-4896-8a56-3267867b5dfe","resolution":{"observed_at":"2026-05-26T16:57:39.467092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Missing data imputation via conditional generator and cor- relation learning for multimodal brain tumor segmentation","venue":null,"work_id":"6f05f715-36db-4f0a-9b4b-26e9257e258d","year":2022},"citing_paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-08T08:30:19.662927Z"},"links":{"citing_paper":"/paper/2604.23112"},"observation_digest":"sha256:932cf6022414e5b8564c619535ecf914782421d0445b2f4923f468d9c3867aa5","observation_id":"b98f1dee-6fa9-4b8b-9c6b-218d0d8a7cfb","resolution":{"observed_at":"2026-05-26T16:57:39.413765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.23112","last_updated":"2026-04-25T02:35:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-31T11:45:01.313226Z","submitted_at":"2026-04-25T02:35:08Z","title":"Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":11,"verified_fuzzy":30},"total_outbound_references":43},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2604.23112."}