{"as_of":"2026-08-11T14:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0e198609a54fb805d24a50cb205862de2398393069305cb28f7313afc8f1908d","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":"2026-08-03T23:23:15.710196Z","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":"2026-08-11T06:34:44.6726+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":"2026-08-03T23:23:13.363824Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.06163","snapshot_observed_at":"2026-08-03T23:23:13.363824Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.363824Z"},"links":{"cited_paper":"/paper/2511.06163","citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:e41b6001f4651242a56bc86df776339a98b16866b300ceb156cfa53267ea62cf","observation_id":"c81e29cf-b6a8-4824-8054-a7565fef35d8","resolution":{"observed_at":"2026-08-03T23:23:13.363824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2511.06163/citation-record","integrity":"/paper/2511.06163/integrity","json":"/paper/2511.06163/citation-record.json","paper":"/paper/2511.06163"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.322214Z","title":"Early diag- nosis is crucial for improving educational, social, and mental health outcomes [2]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.322214Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:61d2fc6adfc1ad1fff6c845b6c2fa8f5ef78cdc68bd8ea2b246bbe1f132ba3ec","observation_id":"e784a291-f786-4a98-a4e8-d9bb406fbb3c","resolution":{"observed_at":"2026-08-03T23:23:13.322214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.446234Z","title":"Foundation Model Fig","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.446234Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:d1812296e92f1c2bed724832c5a3f51d7abddc3d208cfbcbfcb542fd012c2104","observation_id":"a7a263b9-8768-4edd-bb23-f0693d90bce5","resolution":{"observed_at":"2026-08-03T23:23:13.446234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.534287Z","title":"Emotion and Devel- opment Branch Phenotyping and DTI","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.534287Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:b4fe70fcd77dae73ecc7893570891c7a5c56aef13a7bbceda6f515d474867b35","observation_id":"84928ff3-3739-4d79-ba4e-26b2212e958d","resolution":{"observed_at":"2026-08-03T23:23:13.534287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.612507Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.612507Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:477ad2f8b8fa728308a4777d1aa71aadf8250809fac928deaba64ecb55c581aa","observation_id":"bbfbbf0d-cd08-42e2-b4fe-3acc0b1f3ddb","resolution":{"observed_at":"2026-08-03T23:23:13.612507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.683284Z","title":"This cross-modal adaptation achieved new state-of-the-art results for ADHD diagnosis using only two diffusion MRI-derived feature maps, including FA and MD, as input","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.683284Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:dd649f473e797ea2c008cbfc5f47caf99c8fa2c25d7dd567a715ba4ad5c4b702","observation_id":"824cfe22-b27d-4f6c-9063-5d9cbda2ee9a","resolution":{"observed_at":"2026-08-03T23:23:13.683284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.06163","snapshot_observed_at":"2026-08-03T23:23:13.363824Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.363824Z"},"links":{"cited_paper":"/paper/2511.06163","citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:e41b6001f4651242a56bc86df776339a98b16866b300ceb156cfa53267ea62cf","observation_id":"c81e29cf-b6a8-4824-8054-a7565fef35d8","resolution":{"observed_at":"2026-08-03T23:23:13.363824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.794465Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.794465Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:8c16f2fb04971fe35d267a0d5dea20b88ddeb7ffbd37fba9ce93b168fdacc836","observation_id":"187c2184-5ecd-41ee-bd7e-d494e04ffc90","resolution":{"observed_at":"2026-08-03T23:23:13.794465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.880424Z","title":"Xiaofeng Liu for insightful discus- sions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.880424Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:a369353d71d0ae7915c606beaec668d102b1aa41cbf36200a907bd9b481d6c5c","observation_id":"41e8d04a-7a5e-407e-8916-f18b89983b63","resolution":{"observed_at":"2026-08-03T23:23:13.880424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:13.977499Z","title":"New insights into attention-deficit/hyperactivity disorder using structural neuroimaging,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:13.977499Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:e56d41c15f5946d4429ed5085e03b6b6f4537c24a64d4dc3ed4605adb2995131","observation_id":"7e64b276-33e2-4e2b-adba-3ca635bcea6e","resolution":{"observed_at":"2026-08-03T23:23:13.977499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.049644Z","title":"Why the diagnosis of attention deficit hyperactivity dis- order matters,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.049644Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:1630d4a09b0510c10241aa0fe379fd4525d8abee544cc4fb7fa7ed3a9253dadf","observation_id":"26126819-8553-4907-9901-467844e921ff","resolution":{"observed_at":"2026-08-03T23:23:14.049644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.136537Z","title":"Multimodal mr images-based diagnosis of early adolescent attention- deficit/hyperactivity disorder using multiple kernel learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.136537Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:4a185df248659bdf14226bd996800c8e50e9d30f0863bcdd05605221be573f6b","observation_id":"a2a665e5-8a6e-451b-878a-cebe476cb731","resolution":{"observed_at":"2026-08-03T23:23:14.136537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.220683Z","title":"Population level multimodal neu- roimaging correlates of attention-deficit hyperactivity disorder among children,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.220683Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:27a86e1a6e284095a662237cad2b65820a55f43fb66af9192e131733de75a507","observation_id":"2a36d706-df86-4378-a61b-c6870728fcf3","resolution":{"observed_at":"2026-08-03T23:23:14.220683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.291138Z","title":"Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole- brain white matter microstructure: A longitudinal study,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.291138Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:d5c0d67b5a3cb8946791ea9ea1cdce9e3184dcdd941b3cf71f7df34331844c68","observation_id":"31be100c-945d-4ee9-9790-1dd4a69fe361","resolution":{"observed_at":"2026-08-03T23:23:14.291138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.295467Z","title":"Foundation model for cancer imaging biomarkers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.295467Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:08e606e16baeddd86d3c172618d312d02c53906a041e88a79b7a2d3d9c517441","observation_id":"65b38c89-264c-406d-bb49-4155868e75fd","resolution":{"observed_at":"2026-08-03T23:23:14.295467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.473166Z","title":"Lora: Low-rank adaptation of large lan- guage models.,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.473166Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:1e70916f548e974c42349e594bd130bee8c660eea52152a4ca4c1e8d6c52b715","observation_id":"e984212b-42da-4d62-a411-085b37f8b0e4","resolution":{"observed_at":"2026-08-03T23:23:14.473166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13785","last_updated":"2023-10-17T12:24:24Z","snapshot_observed_at":"2026-08-11T08:24:41.442941Z","submitted_at":"2023-04-26T19:05:34Z","title":"Customized Segment Anything Model for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13785","snapshot_observed_at":"2026-08-03T23:23:14.605621Z","title":"Customized segment anything model for medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.605621Z"},"links":{"cited_paper":"/paper/2304.13785","citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:e301b774a7297b03aa03e9f2c6b4b71f8b3a27620f532b3fd8a5cbbe2ce272e4","observation_id":"2e397c07-53f9-4665-9455-b3ba37b57d0d","resolution":{"observed_at":"2026-08-03T23:23:14.605621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.719933Z","title":"Lora-enhanced rt- detr: First low-rank adaptation based detr for real-time full body anatomical structures identification in muscu- loskeletal ultrasound,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.719933Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:297f332538591534509b216c4ecc28a2ecc5b4ee93cd5d1d14b84bef2799abb2","observation_id":"0382a6bc-9aa3-4ddf-8566-327e4deefee0","resolution":{"observed_at":"2026-08-03T23:23:14.719933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.791917Z","title":"3d u-net: learning dense volumetric segmentation from sparse annotation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.791917Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:4b1950ad4e333372e3db88a14849ebb21a4d21522d636f4009c200b25317a68a","observation_id":"50953679-a45c-4f4d-a5f6-151d82bef171","resolution":{"observed_at":"2026-08-03T23:23:14.791917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:14.927264Z","title":"Models genesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:14.927264Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:466b3e7fb3cd24e83607f04e630c1471bc33c5ab099b8f1d2038e8279541719e","observation_id":"fc4a3275-4b4b-4933-9c58-388f676f5ac1","resolution":{"observed_at":"2026-08-03T23:23:14.927264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:15.100964Z","title":"Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.100964Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:780add05c14bfe30171dac6f863db26ed7d3cba558ab9ae0ada3bae72f49a995","observation_id":"a87a1764-0e0d-4821-8c7c-5f2daa3d5379","resolution":{"observed_at":"2026-08-03T23:23:15.100964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.00625","last_updated":"2019-07-17T10:19:12Z","snapshot_observed_at":"2026-08-06T19:43:14.274077Z","submitted_at":"2019-04-01T08:14:29Z","title":"Med3D: Transfer Learning for 3D Medical Image Analysis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.00625","snapshot_observed_at":"2026-08-03T23:23:15.223243Z","title":"Med3d: Transfer learning for 3d medical image analysis,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.223243Z"},"links":{"cited_paper":"/paper/1904.00625","citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:0f59799fc817d1a22f3479688737eeba97d95f08aa728a7c64bf71e4f8ce513a","observation_id":"cd550e51-e136-4255-ba80-743463722de7","resolution":{"observed_at":"2026-08-03T23:23:15.223243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:15.395030Z","title":"”emotion and development branch phenotyping and dti (2012- 2017)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.395030Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:1c4b4b4f59a06b55d7a420ea063456cd53bf58984520b91057d3efafa57bd450","observation_id":"0ee50d95-ae59-4c8a-b603-b16fbf20ca72","resolution":{"observed_at":"2026-08-03T23:23:15.395030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:15.506457Z","title":"Qsiprep: an integrative plat- form for preprocessing and reconstructing diffusion mri data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.506457Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:2a04e3d84626ecd0cae50fd4c338278a365657d8f1c240c348ab17d1286bbc9f","observation_id":"8302de07-944f-42f1-8bc9-cf4c0c55bfc3","resolution":{"observed_at":"2026-08-03T23:23:15.506457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:15.586787Z","title":"An Inte- grated Approach to Correction for Off-Resonance Ef- fects and Subject Movement in Diffusion MR Imaging,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.586787Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:f4246dfb1758f783107d43b3e087215ef04b16e8504fc46bdb7925c4f7ba87ff","observation_id":"a825bd93-a16c-4dc1-a8c8-90ec9fca3661","resolution":{"observed_at":"2026-08-03T23:23:15.586787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T23:23:15.710196Z","title":"A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration,","venue":null,"work_id":null,"year":2033},"citing_paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T23:23:15.710196Z"},"links":{"citing_paper":"/paper/2511.06163"},"observation_digest":"sha256:cd5988487603b91d493f3d01544e04515ee6d4886808d4c233041597cf55e355","observation_id":"d14301ce-61a0-4a65-90ad-c9866623fdb4","resolution":{"observed_at":"2026-08-03T23:23:15.710196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.06163","last_updated":"2026-01-15T05:18:46Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-09T14:12:30.164232Z","submitted_at":"2025-11-08T23:29:28Z","title":"Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":0,"verified_fuzzy":0},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2511.06163."}