{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KD3MXCRPGCTLQBJQDHNBLDVZ2W","short_pith_number":"pith:KD3MXCRP","schema_version":"1.0","canonical_sha256":"50f6cb8a2f30a6b8053019da158eb9d5bd2c1ac175548d15664ecbe9dc8b4aae","source":{"kind":"arxiv","id":"2205.03231","version":1},"attestation_state":"computed","paper":{"title":"Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Jessica J.M.Monaghan, Lina Yao, Molly Lucas, Yun Li, Yu Zhang, Zhe Liu","submitted_at":"2022-05-03T03:17:44Z","abstract_excerpt":"With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders fr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2205.03231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-05-03T03:17:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4ea8aea7dc66c09f971bfe3245705fd3a1809a0198d89ceceb7187f1959bfca6","abstract_canon_sha256":"91454d363b6abfd2f61ba1ad6e3ccbdeac80f8e1a81b2fea88b34a54c64ef748"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:57.344525Z","signature_b64":"aJpa0sfFdXT1yl9b8K0Y0KqSx87VlrA+6mKi1f9qpiGGubEhgpMgFXvxjDs6htdTOnEUaqfTA8jvVuo7jM7gDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50f6cb8a2f30a6b8053019da158eb9d5bd2c1ac175548d15664ecbe9dc8b4aae","last_reissued_at":"2026-07-05T04:20:57.344018Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:57.344018Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Jessica J.M.Monaghan, Lina Yao, Molly Lucas, Yun Li, Yu Zhang, Zhe Liu","submitted_at":"2022-05-03T03:17:44Z","abstract_excerpt":"With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.03231","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.03231/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2205.03231","created_at":"2026-07-05T04:20:57.344085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.03231v1","created_at":"2026-07-05T04:20:57.344085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.03231","created_at":"2026-07-05T04:20:57.344085+00:00"},{"alias_kind":"pith_short_12","alias_value":"KD3MXCRPGCTL","created_at":"2026-07-05T04:20:57.344085+00:00"},{"alias_kind":"pith_short_16","alias_value":"KD3MXCRPGCTLQBJQ","created_at":"2026-07-05T04:20:57.344085+00:00"},{"alias_kind":"pith_short_8","alias_value":"KD3MXCRP","created_at":"2026-07-05T04:20:57.344085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W","json":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W.json","graph_json":"https://pith.science/api/pith-number/KD3MXCRPGCTLQBJQDHNBLDVZ2W/graph.json","events_json":"https://pith.science/api/pith-number/KD3MXCRPGCTLQBJQDHNBLDVZ2W/events.json","paper":"https://pith.science/paper/KD3MXCRP"},"agent_actions":{"view_html":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W","download_json":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W.json","view_paper":"https://pith.science/paper/KD3MXCRP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.03231&json=true","fetch_graph":"https://pith.science/api/pith-number/KD3MXCRPGCTLQBJQDHNBLDVZ2W/graph.json","fetch_events":"https://pith.science/api/pith-number/KD3MXCRPGCTLQBJQDHNBLDVZ2W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W/action/storage_attestation","attest_author":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W/action/author_attestation","sign_citation":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W/action/citation_signature","submit_replication":"https://pith.science/pith/KD3MXCRPGCTLQBJQDHNBLDVZ2W/action/replication_record"}},"created_at":"2026-07-05T04:20:57.344085+00:00","updated_at":"2026-07-05T04:20:57.344085+00:00"}