{"as_of":"2026-08-18T17:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:da6ffe2cdc4cdb4012803c11aa4ee4e4b62e138bc588398bd47b8c5a29458f3e","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:12:54.755086Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T12:17:54.168604Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T12:20:22.600435Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"cited_work":{"arxiv_id":"2412.16197","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.16197","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Akrami, H","venue":null,"work_id":"c1e64383-8172-419b-993f-318a81199292","year":2024},"citing_paper":{"arxiv_id":"2604.14547","last_updated":"2026-04-16T02:24:24Z","snapshot_observed_at":"2026-07-06T23:02:18.425249Z","submitted_at":"2026-04-16T02:24:24Z","title":"Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-10T12:17:54.168604Z"},"links":{"cited_paper":"/paper/2412.16197","citing_paper":"/paper/2604.14547"},"observation_digest":"sha256:8b6c16045759f0f8e8d6aa94c147f6da1f9422b4afe7b6918e814019c1445d73","observation_id":"ed54fe39-be64-4815-9f8e-0873603771a8","resolution":{"observed_at":"2026-05-10T12:20:22.601781Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.16197/citation-record","integrity":"/paper/2412.16197/integrity","json":"/paper/2412.16197/citation-record.json","paper":"/paper/2412.16197"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.360428Z","title":"Prediction of post traumatic epilepsy using mri-based imaging markers","venue":null,"work_id":"cfd0b37d-c990-4818-9fcc-22432b6b5bc5","year":2024},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.652832Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:842d3be93aa816ff5789c831263358ded7ebd1e48c6495ab1ba0aa1228ec4af8","observation_id":"88dd88fb-09c7-413a-bf6b-740777fc0202","resolution":{"observed_at":"2026-08-11T14:12:55.364224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.05722","last_updated":"2020-03-23T18:36:55Z","snapshot_observed_at":"2026-08-18T16:54:31.051671Z","submitted_at":"2019-11-13T18:53:26Z","title":"Momentum Contrast for Unsupervised Visual Representation Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.05722","snapshot_observed_at":"2026-08-11T14:12:54.666099Z","title":"Zhi-An Huang, Rui Liu, and Kay Chen Tan","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.666099Z"},"links":{"cited_paper":"/paper/1911.05722","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:5ee9af77963d5385f79619b16af02fcae00d8b1fa1188c5d7222956a5eb49bd6","observation_id":"869a5519-c556-486e-bbfb-4492c3eb89ff","resolution":{"observed_at":"2026-08-11T14:12:54.666099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T14:12:54.670494Z","title":"Adam: a method for stochastic optimization (2014).arXiv preprint arXiv:1412.6980, 22,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.670494Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:3137d8a6ed40278e869e046ef6d5ed59d65656404796fce5624b6a748f5e201c","observation_id":"e24f44c9-9c72-4604-81ce-1920d7941b3a","resolution":{"observed_at":"2026-08-11T14:12:54.670494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01418","last_updated":"2020-11-03T01:57:37Z","snapshot_observed_at":"2026-08-16T19:08:33.942078Z","submitted_at":"2020-11-03T01:57:37Z","title":"Meta-learning Transferable Representations with a Single Target Domain","version":1},"cited_work":{"arxiv_id":"2011.01418","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.01418","snapshot_observed_at":"2026-08-11T14:12:55.080371Z","title":"Meta-learning Transferable Representations with a Single Target Domain","venue":"cs.LG","work_id":"76c674de-069f-400c-ab3b-df56271403f5","year":2020},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.687843Z"},"links":{"cited_paper":"/paper/2011.01418","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:cd8026774f4a11a4a713809100067e10fbcd1ca3f7d4e11f6dec6a3e7905170d","observation_id":"633e9017-12a4-4706-a21f-d20368300bf0","resolution":{"observed_at":"2026-08-11T14:12:55.087046Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.322150Z","title":"Brainlm: A foundation model for brain activity recordings.bioRxiv, pp","venue":null,"work_id":"c3b6c923-64d4-4532-9ad9-88774c10748c","year":2023},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.692339Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:a667f49c4f1ceb800f61bad3dd6d586693865a296a1910d39853dea44d9e38e0","observation_id":"9bc51d10-e6d9-4962-a8ae-6f8163d411f9","resolution":{"observed_at":"2026-08-11T14:12:55.326390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.309069Z","title":"Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri.Cerebral cortex, 28(9):3095–3114,","venue":null,"work_id":"82084403-1525-4cf7-a15c-46cad4b3dc63","year":2025},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.696202Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:05d99ca8e0586da49a746fed68c391aaa60347a174f9c851b21871d8cc5c4147","observation_id":"d6f2b317-9c0b-478c-a1f5-8076072f4243","resolution":{"observed_at":"2026-08-11T14:12:55.313823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.292519Z","title":"Contrastive functional connectivity graph learning for population-based fmri classification","venue":null,"work_id":"cee3f9d9-acbf-45e1-b7e7-b0dd462da7af","year":2022},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.705958Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:54bd12e55e6104851f2e98566bdd58e57f5ef2dbd189caaa9230b23d770e183a","observation_id":"0d264d1e-bac9-44e3-923e-4af8ca047c51","resolution":{"observed_at":"2026-08-11T14:12:55.297292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:54.709987Z","title":"doi: 10.1109/tmi.2024.3392988","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.709987Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:d4612bb60d068c39812284cc97342ecf785fec21661800897f3cd8c199f08d2d","observation_id":"d783a6da-6d92-4a9b-9bbf-3c8c9ca84e1d","resolution":{"observed_at":"2026-08-11T14:12:54.709987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.279685Z","title":"When does self-supervision help graph convolutional networks? In international conference on machine learning, pp","venue":null,"work_id":"9002127f-d564-4ae5-98d1-2f4324ce641c","year":2005},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.714297Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:34ebe2660ab769e57f13427a5483d697e0b72b023834e41fa9e82b030bcdb59d","observation_id":"e7d4bd01-9da9-4a4f-9739-1c56e93467c3","resolution":{"observed_at":"2026-08-11T14:12:55.284300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.261245Z","title":"This allows us to assess the robustness and generalization capability of MeTSK under data-scarce conditions—settings that are common in clinical practice","venue":null,"work_id":"72ad9f40-e8c3-4c8b-a6e0-947c8f85ef5d","year":2024},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.726952Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:080972e0bd47101e1d495485a44013012f5792a28d600f136c17b24b9aee14f7","observation_id":"dca7086d-931d-4366-9aa6-93e28693af3f","resolution":{"observed_at":"2026-08-11T14:12:55.267533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.230651Z","title":"(2024) 0.5 F1 Score 40% for testing 42 Harvard-Oxford 69Li et al","venue":null,"work_id":"1238958c-6480-4970-bb8d-477154fa6efe","year":2024},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.735055Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:76c1c21f6f9a487224cd2c2be8256cfe0918efeddfb1ebe13b9bab9bdd6fc356","observation_id":"0ad058ab-8014-4eae-a890-27b1f474c376","resolution":{"observed_at":"2026-08-11T14:12:55.234950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.211982Z","title":"(2021) 0.675 Accuracy 10-fold CV 40 Schaefer 100MeTSK (Ours) 0.6831 AUC 5-fold CV 40 AAL 116 OASIS-3 Han et al","venue":null,"work_id":"f884bf02-10c6-409d-963d-6a5a16870856","year":2021},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.739301Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:768ec85640ac89a41c5d6ad1dedf99e7979e18a0d8e79699dff97b79af6d90e7","observation_id":"e5b059b7-70db-4bf1-b9ab-4f7e85ccacea","resolution":{"observed_at":"2026-08-11T14:12:55.216569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.195668Z","title":null,"venue":null,"work_id":"2d51be6b-467d-4b0f-962b-89bb14d7be57","year":2024},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.743922Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:4dd3775b441f81629edf2d28d92202e2683fe605d67813f2f2260d14244fec7f","observation_id":"898f5df7-b735-4204-a082-0ab4e6e7875a","resolution":{"observed_at":"2026-08-11T14:12:55.200270Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.181804Z","title":"Fluctuations are expected, as the loss is computed on a small (39-subject) randomly sampled meta-validation set","venue":null,"work_id":"902bbbd9-7901-4aa0-b909-45a2c4ad3f66","year":2025},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.750503Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:1419e1aac8adc2110e8bcbe8419fba122941f083bc7d83cbf15a438d85d5d045","observation_id":"7aea5197-b1f6-43ff-a715-144f579e647c","resolution":{"observed_at":"2026-08-11T14:12:55.186530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.168801Z","title":"Notably, the pre-training datasets for these foundation models already included the HCP data, so aligning the training data required only fine- tuning on the ADHD-Peking dataset","venue":null,"work_id":"79fd938b-7870-48c7-b30b-92c12b11ecdc","year":2022},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.755086Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:3dd600ce2dcd26bce13adc4af982af0406fd7545fcb48ba6358de8a1536d818e","observation_id":"eb0c0435-fb87-41e3-b181-753dd694ebb1","resolution":{"observed_at":"2026-08-11T14:12:55.173479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:54.718658Z","title":"2005.1521516","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.718658Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:847a116f72f583c0ed25fb6c379ec6f597a0fbf67ee117715e2c37d17ab690e1","observation_id":"d8d141c8-40a7-47c1-acfc-86576ec15bf3","resolution":{"observed_at":"2026-08-11T14:12:54.718658Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.347889Z","title":"Large scale fine-grained categorization and domain-specific transfer learning,","venue":null,"work_id":"2e08792f-8808-46dd-bb11-4a9de2d947a7","year":2025},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.657753Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:e513be65386b28cc6a45ea91d843d1c1add896a51dbe4824db0ce8b4303fff54","observation_id":"0d1d0e92-05c0-4a7b-a975-a1db35aefdf8","resolution":{"observed_at":"2026-08-11T14:12:55.352727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-11T14:12:54.675092Z","title":"Semi-supervised classification with graph convolutional networks.arXiv preprint arXiv:1609.02907,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.675092Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:dfca8480829fabc4e895961f721f00642d1c544922b3b58a7a77e5291af7a0b7","observation_id":"47af3e69-dcfa-4490-b114-237b7c243cbf","resolution":{"observed_at":"2026-08-11T14:12:54.675092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.10054","last_updated":"2022-02-21T09:03:34Z","snapshot_observed_at":"2026-08-16T20:16:33.870271Z","submitted_at":"2022-02-21T09:03:34Z","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.10054","snapshot_observed_at":"2026-08-11T14:12:54.679454Z","title":"Fine-tuning can distort pretrained features and underperform out-of-distribution.arXiv preprint arXiv:2202.10054,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.679454Z"},"links":{"cited_paper":"/paper/2202.10054","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:a53c1027d2ec24414af7c1de0d2070dbf762cc641e1a23b825fa5b7f8c94c7e6","observation_id":"0dba54fe-0631-47e3-bf53-12fdc8b561ff","resolution":{"observed_at":"2026-08-11T14:12:54.679454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.246094Z","title":"(2022) 0.667 AUC 10-fold CV 42 Brainnetome (Fan et al.,","venue":null,"work_id":"af8cb14a-a749-4194-beed-44bbc37797d2","year":2022},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.730842Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:6b39e119e2d90c92bdf11dd81bfe1ab1e2da5e45e7049e14f81cc4bb578c591b","observation_id":"d5783f16-35d8-4cf6-a85e-5aa64d0bfadc","resolution":{"observed_at":"2026-08-11T14:12:55.251081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07733","last_updated":"2020-09-10T09:46:02Z","snapshot_observed_at":"2026-08-14T05:53:12.800111Z","submitted_at":"2020-06-13T22:35:21Z","title":"Bootstrap your own latent: A new approach to self-supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07733","snapshot_observed_at":"2026-08-11T14:12:54.661495Z","title":"Rao P Gullapalli","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.661495Z"},"links":{"cited_paper":"/paper/2006.07733","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:c59580477df7a9180cf415867e52194a19ed14a4d5d3005f344e6d9ca296bd50","observation_id":"fa28aeaf-6f26-4e90-8ad3-9e83da431dc3","resolution":{"observed_at":"2026-08-11T14:12:54.661495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.371126Z","title":"Prediction of posttrau- matic epilepsy using machine learning","venue":null,"work_id":"32bf1e1c-83b3-4450-b078-af5931a41594","year":2021},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.648029Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:3cc1b363e698745d629ed55e0c2f53c7b70f47c107544dfeb732c49435cd82a0","observation_id":"da815db0-8829-45a7-8bb8-040423c23f9f","resolution":{"observed_at":"2026-08-11T14:12:55.375748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:55.335036Z","title":"Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease.MedRxiv, pp","venue":null,"work_id":"3be86514-2edd-4aff-8e17-38c22565c933","year":2019},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.683816Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:770efb3017b80cd5f0f71e86286b9610db5a40eab922c47791411b0032eb32c6","observation_id":"1c6d004f-2ca0-4deb-b072-61df03ee0d60","resolution":{"observed_at":"2026-08-11T14:12:55.339171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:12:54.722488Z","title":"Xi Sheryl Zhang, Fengyi Tang, Hiroko H Dodge, Jiayu Zhou, and Fei Wang","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.722488Z"},"links":{"citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:266e5a30402ce80026b8ff844e1b7007807928affa09987f956911e27093e3a9","observation_id":"c9426981-3fc0-4916-bf00-4be6e64f403f","resolution":{"observed_at":"2026-08-11T14:12:54.722488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04154","last_updated":"2024-08-08T01:42:31Z","snapshot_observed_at":"2026-08-16T13:27:49.292312Z","submitted_at":"2024-08-08T01:42:31Z","title":"The Data Addition Dilemma","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04154","snapshot_observed_at":"2026-08-11T14:12:54.700280Z","title":"Chunlei Shi, Xianwei Xin, and Jiacai Zhang","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T14:12:54.700280Z"},"links":{"cited_paper":"/paper/2408.04154","citing_paper":"/paper/2412.16197"},"observation_digest":"sha256:3b112014b08788562d0bde990448e62bdf2813e17af37b269c1913ccbdc45e4d","observation_id":"a6b590c5-6e69-4be2-b716-591999b10a46","resolution":{"observed_at":"2026-08-11T14:12:54.700280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.16197","last_updated":"2025-05-28T18:27:12Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-18T14:06:02.657310Z","submitted_at":"2024-12-16T22:07:35Z","title":"Generalizable Representation Learning for fMRI-based Neurological Disorder Identification"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":14},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2412.16197."}