{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TRBVIZMKWUN3IRGLV2T3ZN3SJV","short_pith_number":"pith:TRBVIZMK","canonical_record":{"source":{"id":"2606.06550","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-04T08:18:38Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"855ad677108acdaf71e734f0f73adf00c5905d9f4f4a2742205aa59dc07f45ab","abstract_canon_sha256":"749dcb5b2ed191384a1ebef9ff91f3931818ab62a2bb08173008598792ecdfa7"},"schema_version":"1.0"},"canonical_sha256":"9c4354658ab51bb444cbaea7bcb7724d514000b385ed993aeeb7b3227deecdeb","source":{"kind":"arxiv","id":"2606.06550","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.06550","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"arxiv_version","alias_value":"2606.06550v1","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06550","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_12","alias_value":"TRBVIZMKWUN3","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_16","alias_value":"TRBVIZMKWUN3IRGL","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_8","alias_value":"TRBVIZMK","created_at":"2026-06-08T00:03:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TRBVIZMKWUN3IRGLV2T3ZN3SJV","target":"record","payload":{"canonical_record":{"source":{"id":"2606.06550","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-04T08:18:38Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"855ad677108acdaf71e734f0f73adf00c5905d9f4f4a2742205aa59dc07f45ab","abstract_canon_sha256":"749dcb5b2ed191384a1ebef9ff91f3931818ab62a2bb08173008598792ecdfa7"},"schema_version":"1.0"},"canonical_sha256":"9c4354658ab51bb444cbaea7bcb7724d514000b385ed993aeeb7b3227deecdeb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T00:03:42.107803Z","signature_b64":"gJrytOAi0b/VcJJSCOVcAi8y+36Nitc/3OxbhtA9iVydQOyP6RuQZTt7JZzSKTNo824sgHGyj7aw1ZW97h+BBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c4354658ab51bb444cbaea7bcb7724d514000b385ed993aeeb7b3227deecdeb","last_reissued_at":"2026-06-08T00:03:42.106975Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T00:03:42.106975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.06550","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-08T00:03:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dCO1cXaImfjygor6/oHMVpwkor7/RBMETiyHocYNfpS8U2aVlZU6zCwxytyZFrkIKrHR38cpKv+N33cXL/BEAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T06:19:33.218453Z"},"content_sha256":"71febd49056a9eb1623a468ec89be832ae38d110f99a8381059c7794bcedd964","schema_version":"1.0","event_id":"sha256:71febd49056a9eb1623a468ec89be832ae38d110f99a8381059c7794bcedd964"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TRBVIZMKWUN3IRGLV2T3ZN3SJV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Ruxiao Qian, Shuanglin Li, Siyang Song","submitted_at":"2026-06-04T08:18:38Z","abstract_excerpt":"Self-supervised learning (SSL) yields powerful, context-rich representations for speech emotion recognition (SER), yet aggregating these representations into holistic descriptors remains a bottleneck. Conventional first-order aggregation implicitly assumes feature independence, which overlooks the latent Riemannian geometry and discards higher-order relationships essential to the representational power of the backbone. To address this problem, this paper proposes a novel Second-Order Correlation (SOC) layer. Instead of treating features in isolation, SOC models feature correlations as covarian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06550","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/2606.06550/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-08T00:03:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lU7rF83y0WlcuEduVYKpusWJ8e4MIc8dmZUR4Ui09XfYA+F5XjONeWe1TmhIblFJYKIJvcr1qV+yEA1mJUnNBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T06:19:33.218822Z"},"content_sha256":"6b329ed7e040255137ed401d555a9d628a2d43693489eb60e13f12de9519c455","schema_version":"1.0","event_id":"sha256:6b329ed7e040255137ed401d555a9d628a2d43693489eb60e13f12de9519c455"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/bundle.json","state_url":"https://pith.science/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-23T06:19:33Z","links":{"resolver":"https://pith.science/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV","bundle":"https://pith.science/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/bundle.json","state":"https://pith.science/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TRBVIZMKWUN3IRGLV2T3ZN3SJV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TRBVIZMKWUN3IRGLV2T3ZN3SJV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"749dcb5b2ed191384a1ebef9ff91f3931818ab62a2bb08173008598792ecdfa7","cross_cats_sorted":["cs.AI","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-04T08:18:38Z","title_canon_sha256":"855ad677108acdaf71e734f0f73adf00c5905d9f4f4a2742205aa59dc07f45ab"},"schema_version":"1.0","source":{"id":"2606.06550","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.06550","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"arxiv_version","alias_value":"2606.06550v1","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06550","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_12","alias_value":"TRBVIZMKWUN3","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_16","alias_value":"TRBVIZMKWUN3IRGL","created_at":"2026-06-08T00:03:42Z"},{"alias_kind":"pith_short_8","alias_value":"TRBVIZMK","created_at":"2026-06-08T00:03:42Z"}],"graph_snapshots":[{"event_id":"sha256:6b329ed7e040255137ed401d555a9d628a2d43693489eb60e13f12de9519c455","target":"graph","created_at":"2026-06-08T00:03:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.06550/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised learning (SSL) yields powerful, context-rich representations for speech emotion recognition (SER), yet aggregating these representations into holistic descriptors remains a bottleneck. Conventional first-order aggregation implicitly assumes feature independence, which overlooks the latent Riemannian geometry and discards higher-order relationships essential to the representational power of the backbone. To address this problem, this paper proposes a novel Second-Order Correlation (SOC) layer. Instead of treating features in isolation, SOC models feature correlations as covarian","authors_text":"Ruxiao Qian, Shuanglin Li, Siyang Song","cross_cats":["cs.AI","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-04T08:18:38Z","title":"Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06550","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:71febd49056a9eb1623a468ec89be832ae38d110f99a8381059c7794bcedd964","target":"record","created_at":"2026-06-08T00:03:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"749dcb5b2ed191384a1ebef9ff91f3931818ab62a2bb08173008598792ecdfa7","cross_cats_sorted":["cs.AI","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-04T08:18:38Z","title_canon_sha256":"855ad677108acdaf71e734f0f73adf00c5905d9f4f4a2742205aa59dc07f45ab"},"schema_version":"1.0","source":{"id":"2606.06550","kind":"arxiv","version":1}},"canonical_sha256":"9c4354658ab51bb444cbaea7bcb7724d514000b385ed993aeeb7b3227deecdeb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c4354658ab51bb444cbaea7bcb7724d514000b385ed993aeeb7b3227deecdeb","first_computed_at":"2026-06-08T00:03:42.106975Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T00:03:42.106975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gJrytOAi0b/VcJJSCOVcAi8y+36Nitc/3OxbhtA9iVydQOyP6RuQZTt7JZzSKTNo824sgHGyj7aw1ZW97h+BBw==","signature_status":"signed_v1","signed_at":"2026-06-08T00:03:42.107803Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.06550","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71febd49056a9eb1623a468ec89be832ae38d110f99a8381059c7794bcedd964","sha256:6b329ed7e040255137ed401d555a9d628a2d43693489eb60e13f12de9519c455"],"state_sha256":"f1ec6e2328c62acf99f2a5e5cbc4fad209efc519913c140cb7793d09fac53aeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"877r3Dz0nFPRTKu4hCtV8TW8gjBLpKh7P2qJY0iwQHiWy6orY5oCmozbuVNoEZurnTM4nJ94Ue8r8RW/fktdDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T06:19:33.220777Z","bundle_sha256":"c26cb5f59bab235b89dac07dff7ad7dc2bd0d09e219d728ba531d62075a13729"}}