{"as_of":"2026-08-06T13:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6b450afe168b3c478b2554ca0a1697f16c3c0ea74089b0cc1f3f6cc5fe02c0e4","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T15:46:58.566821Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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":"2026-05-10T15:46:58.566821Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-11T09:51:00.712579Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"cited_work":{"arxiv_id":"2604.10181","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.10181","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","venue":"cs.SD","work_id":"4bc5c6e8-05f4-4829-b61e-12566f19f382","year":2026},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"cited_paper":"/paper/2604.10181","citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:fc9150020308ca6e594d0ea687c4abebb10c62fe2ed6cb83a9c123a1f5ab8792","observation_id":"b537bfb3-a2d6-4cde-a294-3a903e966b27","resolution":{"observed_at":"2026-05-11T09:51:00.715166Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2604.10181/citation-record","integrity":"/paper/2604.10181/integrity","json":"/paper/2604.10181/citation-record.json","paper":"/paper/2604.10181"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"cited_work":{"arxiv_id":"2604.10181","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.10181","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","venue":"cs.SD","work_id":"4bc5c6e8-05f4-4829-b61e-12566f19f382","year":2026},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"cited_paper":"/paper/2604.10181","citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:fc9150020308ca6e594d0ea687c4abebb10c62fe2ed6cb83a9c123a1f5ab8792","observation_id":"b537bfb3-a2d6-4cde-a294-3a903e966b27","resolution":{"observed_at":"2026-05-11T09:51:00.715166Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"This mecha- nism selectively enhances clinically significant segments while suppressing neutral or irrelevant content","venue":null,"work_id":"76792654-ebea-4565-9477-ceddaf62f3e4","year":null},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:6ff16b83372050a02bfc162f60d140eb5afff03d111636baffc457d65e8a7bb4","observation_id":"bad4ceb3-2477-458f-a1c0-4f04e482125c","resolution":{"observed_at":"2026-05-17T18:45:06.915100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Transformer(Qwen)","venue":null,"work_id":"0c53ecd8-14c0-4387-96c6-3427f065b34e","year":2018},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c4d4e14fa2da3d912a277ae6c6c90da079c075cfe2e79865819313b68ea7beb7","observation_id":"9095db27-430f-44bd-8480-cd5177ceb906","resolution":{"observed_at":"2026-05-17T18:45:06.917803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Experiments on PDCD2025 and DAIC- WOZ show that ACMG outperforms baseline models in both accuracy and F1","venue":null,"work_id":"4e1ad5de-f826-4247-8b4f-08c0f509c04f","year":null},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c8772d70c5df08748beb1f0e443781ea60bafeea2675d7f3d64a2778a0bac228","observation_id":"fc322aed-e126-40da-bb97-e3058f3f8cb6","resolution":{"observed_at":"2026-05-17T18:45:06.923765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"They were not used to create any core research con- tent, results, or arguments","venue":null,"work_id":"cec8cabb-5dfb-4d8d-a3c5-830e5572d4f5","year":null},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:0ded548e2f5ea6a0e974ab0a0f9e97909deeb2be2fb79b1adf2bacc5faf1fca3","observation_id":"a5626741-32d6-4d2f-895b-267be4a91e5d","resolution":{"observed_at":"2026-05-17T18:45:06.920693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Burden of de- pressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010","venue":null,"work_id":"f84c6693-4728-4b73-9ec3-772b103d2103","year":2010},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c0a71735dbd33b6d9de99761c629e28793252d30f04eb3e070d7dd768867f30d","observation_id":"ec6c98a1-5129-4799-bbb1-48e8022a98e3","resolution":{"observed_at":"2026-05-17T18:45:06.970510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Research domain crite- ria (rdoc): toward a new classification framework for research on mental disorders","venue":null,"work_id":"6cbb6086-f209-4521-992b-d3e2927ebfbf","year":2010},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c145bd38949cfba11d6aace084af1add78d905a2d22bdfd79f29f408917c3872","observation_id":"fe6ece28-08e9-419c-8c1e-87361661d637","resolution":{"observed_at":"2026-05-17T18:50:05.759143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Detecting depres- sion with audio/text sequence modeling of interviews","venue":null,"work_id":"aa1a4514-6055-469a-8139-1297e7c3698a","year":2018},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:5fc26105c410ac2624788ae93851d442bdced87a278d8de61dd9d17208a5fd68","observation_id":"3e3707b7-4768-4744-8004-3ba5a34ca184","resolution":{"observed_at":"2026-05-17T18:50:05.743466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Ecapa- tdnn based depression detection from clinical speech","venue":null,"work_id":"32d8f585-4ad3-4d41-b2dc-854ee7b42da3","year":2022},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:2b7cd3b40e4e62d5efe5ce09d34e8cd1471946eadf35d987ec24469fe94c9996","observation_id":"2394ec0f-a734-42c4-af63-380f21a0e6c1","resolution":{"observed_at":"2026-05-17T18:45:06.983521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Depression detection in speech using transformer and parallel convolutional neural networks","venue":null,"work_id":"03136258-71be-41ce-abb3-6f510bf57a5d","year":2023},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:eb4d3b73140020cfe66d53ffd1ea9ccb88e07b8294199becdb023e8e7b020b39","observation_id":"3ed62231-ef32-4cc1-bfe4-cc8110880d0a","resolution":{"observed_at":"2026-05-17T18:45:06.941251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Speechformer-ctc: Se- quential modeling of depression detection with speech temporal classification","venue":null,"work_id":"2ef21ba4-a958-4188-b3cb-12b9f1fbe32c","year":2024},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:b1ffd1f1b50a4a2b58563c2ab05be0917c1db96e73f7c38cf973cc42f00e99ad","observation_id":"18b9ddbf-53e3-40a4-a0e4-73e6be08b227","resolution":{"observed_at":"2026-05-17T18:45:06.986443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05862","last_updated":"2019-09-11T08:19:49Z","snapshot_observed_at":"2026-08-06T05:33:37.746688Z","submitted_at":"2019-04-11T17:29:30Z","title":"wav2vec: Unsupervised Pre-training for Speech Recognition","version":4},"cited_work":{"arxiv_id":"1904.05862","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1904.05862","snapshot_observed_at":"2026-07-04T20:30:08.546246Z","title":"wav2vec: Unsupervised Pre-training for Speech Recognition","venue":null,"work_id":"b4e617d6-db5e-4190-aeca-abe7ba1874e8","year":1904},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"cited_paper":"/paper/1904.05862","citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:70881af6e1a82bfcefcd9d32508e73f42d09090550e599d470eafa98fb0f4503","observation_id":"130decd9-62cf-4431-958e-298031d78e5f","resolution":{"observed_at":"2026-05-11T09:51:00.710066Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-07-08T07:34:43.404293Z","title":"Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units","venue":null,"work_id":"5e9a152d-1afa-42b6-b2a2-c022f7fce72a","year":2021},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:d8134c55cb70531d7bc4309aeaf4ca96f2afd719df15a22a7d41d9252b673f6c","observation_id":"a9af8736-5aab-4853-9710-20fb94bbdea6","resolution":{"observed_at":"2026-05-17T18:45:06.973401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-07-09T12:16:14.563919Z","title":"Wavlm: Large-scale self- supervised pre-training for full stack speech processing","venue":null,"work_id":"1d47d864-93dd-41e6-9426-cfc627321f9f","year":2022},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:a9627d961bea4edb143dcff77f047e68e0957f76b4910f607336f3f7becec168","observation_id":"941fe629-fcb2-4703-82f9-b2e9c1975ea2","resolution":{"observed_at":"2026-05-17T18:45:06.976698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Self-supervised represen- tations in speech-based depression detection","venue":null,"work_id":"b5a02879-e79a-45ea-a260-e68072765627","year":2023},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c7cae31198194aa29fed2e71cde9d0b9c0460d53fff6f0887437a71f6efea2a6","observation_id":"72b898e4-98ff-496c-93ed-2e6746167797","resolution":{"observed_at":"2026-05-17T18:45:06.979768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Self-Supervised Embeddings for Detecting Individual Symptoms of Depression","venue":null,"work_id":"bbdffd25-f204-4196-bbf7-eb056226e57a","year":2024},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:25f6e161a52a8aa3e48ae4d04a967d7b087b3c9a5eab83aca442cb91d8c01299","observation_id":"8c2dd05d-5bf4-41ea-8de3-618f8accd03d","resolution":{"observed_at":"2026-05-17T18:50:05.773142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Bert: Pre- training of deep bidirectional transformers for language under- standing","venue":null,"work_id":"4952aee7-8847-4eb0-b26a-e8dce4548ee9","year":2019},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:f12ab5e043c65538d94b67228ed818be6ecd1762c4224b42bdedf9f87c8f9e1c","observation_id":"4fb439be-31ac-486a-a8b8-6e1396658909","resolution":{"observed_at":"2026-05-17T18:45:06.967424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":"1907.11692","doi":"10.1007/s10489-02203627-9","metadata_source":"pith","pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","venue":"cs.CL","work_id":"41fe12c4-e538-4890-a244-480650ed3078","year":2019},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:4646c52c27e2975be427408f1805f8715dc463dde37502d5a444392fa0a6f827","observation_id":"d13bcc55-0973-4e21-a75a-21240c37d271","resolution":{"observed_at":"2026-05-11T09:51:00.695770Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Zero-Shot Speech-Based Depression and Anxiety Assessment with LLMs","venue":null,"work_id":"230ccf54-4138-4242-843a-de90ccc6ca04","year":2025},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:a31758cd3d9a9e3b2879ff3f3ecabe3d75dac49fe2f1f45603cddeab1b0d0ea5","observation_id":"942434f5-9564-43bf-adf6-a44c10d32011","resolution":{"observed_at":"2026-05-17T18:45:06.952545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Glottal source features for auto- matic speech-based depression assessment","venue":null,"work_id":"1f3d8161-94f0-463e-aa7c-3c29c32feea3","year":2017},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:d17d6faa87f043050d82f1912e100644f8e7eb6ac40979c04aa5a46ec6015659","observation_id":"44ed62b3-5a46-49f1-86e6-e9fc9e5d499c","resolution":{"observed_at":"2026-05-17T18:45:06.955486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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 time-frequency channel attention and vectorization network for automatic depression level prediction","venue":null,"work_id":"1f72ce87-2a7a-4d1d-bbae-0a692acd6450","year":2021},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:72f173c61b8bf86e4ac70fc8883b6d88d02a1ce7f33f68345ced5db5ee4b00ee","observation_id":"b327b8bd-e025-4d31-828d-5d6df8bf6e87","resolution":{"observed_at":"2026-05-17T18:45:06.929695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"V ocal acoustic biomarkers of depression severity and treatment response","venue":null,"work_id":"3aeee0a2-4c6b-4061-8439-0f328e504e1b","year":2012},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:d16329093b3eac00ab6e2ca54b770838bfc8b5fa740a077589dccc465e6d44a0","observation_id":"d4468d45-b805-4586-8cd2-43b83f3793c7","resolution":{"observed_at":"2026-05-17T18:45:06.960960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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 meta-analysis of correlations between depression and first person singular pronoun use","venue":null,"work_id":"d478c22d-af44-4430-af74-d4afeea85de7","year":2017},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:d97e408e8960b3a8ccfec90a65f26dd50f1efedbce9c62d42660c8b5da05f533","observation_id":"ba2fbb57-5a66-4396-9896-a3599814fce0","resolution":{"observed_at":"2026-05-17T18:45:06.964139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Depression, negative emotionality, and self-referential language: A multi-lab, multi-measure, and multi-language-task research synthesis","venue":null,"work_id":"6e134785-d076-4310-b078-ea2fd5b45b50","year":2019},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:baf1ebb210e3c37beb0bdcc86f2356f76c9c1d6b40b6161089117656df999c71","observation_id":"38f1ffff-6c0a-42ff-8dae-c914eba532cd","resolution":{"observed_at":"2026-05-17T18:50:05.768363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Investigating acoustic-textual emo- tional inconsistency information for automatic depression detec- tion","venue":null,"work_id":"11a24506-75ec-42af-8c98-6551bc8d664b","year":2026},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:e882fda532dce629fd2dcda1e1f07f4ff917627fc1121f404e28f169086c49e0","observation_id":"93f4b39d-8586-42a8-a6a3-59159f587df6","resolution":{"observed_at":"2026-05-17T18:45:06.926545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Speaker normalization for self-supervised speech emotion recognition","venue":null,"work_id":"5eecc0a4-ec18-4366-9dae-cf3c15cef33f","year":2022},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:f7b9ae6f5d7496e644f7f7e818d71b53d347c8c50242f7a5feea7faa9ff3f8d5","observation_id":"22f3b60e-807e-49cb-9cc2-036c771d47c4","resolution":{"observed_at":"2026-05-17T18:45:06.949661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Speech emotion recognition using self-supervised features","venue":null,"work_id":"7d65589b-d679-408d-885f-e652940706a2","year":2022},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:679d3b682e4ac04c548c588ae56d889f8bc91abd73191a7e2649353ed7b10e7f","observation_id":"3d618064-18f9-4a19-b41e-3df928261dfb","resolution":{"observed_at":"2026-05-17T18:45:06.943881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Comparative analy- ses of bert, roberta, distilbert, and xlnet for text-based emotion recognition","venue":null,"work_id":"97b921d5-7f04-46d6-8e74-f1d9da231015","year":2020},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:c65d402d7a7fdfde753641eadb05c41daf60d4d2ec54ba73b1eaa80e803bfb2e","observation_id":"03bc86c7-7e92-42d6-92df-06ddb87bf798","resolution":{"observed_at":"2026-05-17T18:45:06.946798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.05176","last_updated":"2025-06-11T02:54:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-05T15:49:48Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","version":3},"cited_work":{"arxiv_id":"2506.05176","doi":"10.1016/j.displa.2025.103255","metadata_source":"pith","pith_arxiv_id":"2506.05176","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","venue":"cs.CL","work_id":"bab684a8-d933-426c-a19e-2c855a0d1f59","year":2025},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"cited_paper":"/paper/2506.05176","citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:5e5b1aad7e21b104b8731e2ff73392ec8bf3ca4c950c1a4059fa6003b0836f1b","observation_id":"292569ae-3a3b-4317-8a27-491341f264c0","resolution":{"observed_at":"2026-05-11T09:51:00.700331Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-07-08T21:25:38.681095Z","title":"The distress analysis interview corpus of human and computer interviews","venue":null,"work_id":"f630765e-694b-4cd0-9d8b-9e3246b5e5a7","year":2014},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:a23f63f987a0a5338e7318726266f15a58d7fd83969f34fbe3fa1cfecdf52ab6","observation_id":"619b3639-6816-496d-9ef0-7ebfbbf3de67","resolution":{"observed_at":"2026-05-17T18:50:05.777729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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 self-rating depression scale","venue":null,"work_id":"b8391a4d-1a1a-4168-88e8-a31515143bfb","year":1965},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:a742c69676cfedcb3aed69cc50a8ab57b1852d5b2152af6cf968c7e0953a3bed","observation_id":"922fa4ff-7361-4e86-85d0-a5ebd11629f2","resolution":{"observed_at":"2026-05-17T18:45:06.935502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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 rating instrument for anxiety disorders","venue":null,"work_id":"6d317dc6-e3ec-4fdc-805b-4471a853c8d7","year":1971},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:dd91e638cb951083ed8a1a12948724639932568c67f00d77a99bedf9db05906b","observation_id":"08b64ddd-4ef7-4f1f-a24b-6ad54a92d2d5","resolution":{"observed_at":"2026-05-17T18:45:06.958184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Speech and text foundation models for depression detection: Cross-task and cross-language evaluation","venue":null,"work_id":"3f557b03-4c6b-4981-a359-0400ce9d575d","year":2025},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:42b7a0d72b7679cc9574ea4bde7a164770c50a293c69e8c8b602b5c51e7ddf0a","observation_id":"e680748e-9202-4b10-8bcc-b6d383f8d0fe","resolution":{"observed_at":"2026-05-17T18:45:06.938405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"Unsupervised instance discriminative learning for depression detection from speech sig- nals","venue":null,"work_id":"b923e9cd-03a3-4282-9b8e-ef3d5f2049cb","year":2022},"citing_paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T15:46:58.566821Z"},"links":{"citing_paper":"/paper/2604.10181"},"observation_digest":"sha256:28f1e322d7fd0a8b6cc49f02d66eb145bd79f12c3dc97e9c36bc28d6c8200a0d","observation_id":"e64d1c24-66b8-4977-8f30-ed4e08c60c82","resolution":{"observed_at":"2026-05-17T18:45:06.932606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.10181","last_updated":"2026-04-11T12:19:54Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-07-06T22:58:51.931478Z","submitted_at":"2026-04-11T12:19:54Z","title":"Learning to Attend to Depression-Related Patterns: An Adaptive Cross-Modal Gating Network for Depression Detection"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":3,"verified_fuzzy":30},"total_outbound_references":34},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2604.10181."}