{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EWUIO5VAQSS7GLDJOKSALL3UTD","short_pith_number":"pith:EWUIO5VA","canonical_record":{"source":{"id":"1902.02455","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-07T02:55:02Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"2f3c1b296f616824de772656956b57b98c27b42a710bc9dd92ba89e9c4ee4e16","abstract_canon_sha256":"a2070b89501ca582970621bca4d83628d2e48fa1dc107688455516060f5cda8b"},"schema_version":"1.0"},"canonical_sha256":"25a88776a084a5f32c6972a405af7498c37fa0428485508f2ed7423386548b6e","source":{"kind":"arxiv","id":"1902.02455","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.02455","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"arxiv_version","alias_value":"1902.02455v3","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02455","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"pith_short_12","alias_value":"EWUIO5VAQSS7","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EWUIO5VAQSS7GLDJ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EWUIO5VA","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EWUIO5VAQSS7GLDJOKSALL3UTD","target":"record","payload":{"canonical_record":{"source":{"id":"1902.02455","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-07T02:55:02Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"2f3c1b296f616824de772656956b57b98c27b42a710bc9dd92ba89e9c4ee4e16","abstract_canon_sha256":"a2070b89501ca582970621bca4d83628d2e48fa1dc107688455516060f5cda8b"},"schema_version":"1.0"},"canonical_sha256":"25a88776a084a5f32c6972a405af7498c37fa0428485508f2ed7423386548b6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:23.682539Z","signature_b64":"89OSeNFUCeyBSyH9sPetI2IHozfosv1TdOhbK+FxNqKg8EG3uquk02DaJTAVVqDt3MlPGwkaBGmw52jWpMxPDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25a88776a084a5f32c6972a405af7498c37fa0428485508f2ed7423386548b6e","last_reissued_at":"2026-05-17T23:40:23.681855Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:23.681855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.02455","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UZ7ueCeSloDnP6lX9KptOGncqeYCdhaKPuVQuDqN4vBmKDm1KMtTTg6c/ARnrczHYg0ybJSNH6Z65nI+KcXmDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T02:02:47.559660Z"},"content_sha256":"ef87c23524c8d59eac4cb2b85dd92669e19411617f905d787eb314c7da615109","schema_version":"1.0","event_id":"sha256:ef87c23524c8d59eac4cb2b85dd92669e19411617f905d787eb314c7da615109"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EWUIO5VAQSS7GLDJOKSALL3UTD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Ha-Jin Yu, Hee-Soo Heo, Hye-Jin Shim, IL-Ho Yang, Jee-weon Jung, Sung-Hyun Yoon","submitted_at":"2019-02-07T02:55:02Z","abstract_excerpt":"In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filterbank energies. Studies that design various loss functions, including metric learning have been widely explored. In this study, we propose two end-to-end loss functions for speaker verification using the concept of speaker bases, which are trainable parameters. One loss function is designed to further increase the inter-speaker variation, and the other is designed to conduct the identical concept with hard negative m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02455","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-17T23:40:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BFtYzc/+pQxlpG6nkDov1BVSw6mxTHFRVc2QrS8X+dTIft7jJv4fB3iYhNYIzqfvwlVT/c1Pas7Rd01IdaOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T02:02:47.559999Z"},"content_sha256":"858f45c68c1ecefce3a3758f67c5c5b3a425320782e8a0cf5cce2e56489ad9f7","schema_version":"1.0","event_id":"sha256:858f45c68c1ecefce3a3758f67c5c5b3a425320782e8a0cf5cce2e56489ad9f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/bundle.json","state_url":"https://pith.science/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/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":"2026-07-26T02:02:47Z","links":{"resolver":"https://pith.science/pith/EWUIO5VAQSS7GLDJOKSALL3UTD","bundle":"https://pith.science/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/bundle.json","state":"https://pith.science/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EWUIO5VAQSS7GLDJOKSALL3UTD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EWUIO5VAQSS7GLDJOKSALL3UTD","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":"a2070b89501ca582970621bca4d83628d2e48fa1dc107688455516060f5cda8b","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-07T02:55:02Z","title_canon_sha256":"2f3c1b296f616824de772656956b57b98c27b42a710bc9dd92ba89e9c4ee4e16"},"schema_version":"1.0","source":{"id":"1902.02455","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.02455","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"arxiv_version","alias_value":"1902.02455v3","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02455","created_at":"2026-05-17T23:40:23Z"},{"alias_kind":"pith_short_12","alias_value":"EWUIO5VAQSS7","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EWUIO5VAQSS7GLDJ","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EWUIO5VA","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:858f45c68c1ecefce3a3758f67c5c5b3a425320782e8a0cf5cce2e56489ad9f7","target":"graph","created_at":"2026-05-17T23:40:23Z","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"},"paper":{"abstract_excerpt":"In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filterbank energies. Studies that design various loss functions, including metric learning have been widely explored. In this study, we propose two end-to-end loss functions for speaker verification using the concept of speaker bases, which are trainable parameters. One loss function is designed to further increase the inter-speaker variation, and the other is designed to conduct the identical concept with hard negative m","authors_text":"Ha-Jin Yu, Hee-Soo Heo, Hye-Jin Shim, IL-Ho Yang, Jee-weon Jung, Sung-Hyun Yoon","cross_cats":["cs.LG","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-07T02:55:02Z","title":"End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02455","kind":"arxiv","version":3},"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:ef87c23524c8d59eac4cb2b85dd92669e19411617f905d787eb314c7da615109","target":"record","created_at":"2026-05-17T23:40:23Z","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":"a2070b89501ca582970621bca4d83628d2e48fa1dc107688455516060f5cda8b","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-07T02:55:02Z","title_canon_sha256":"2f3c1b296f616824de772656956b57b98c27b42a710bc9dd92ba89e9c4ee4e16"},"schema_version":"1.0","source":{"id":"1902.02455","kind":"arxiv","version":3}},"canonical_sha256":"25a88776a084a5f32c6972a405af7498c37fa0428485508f2ed7423386548b6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25a88776a084a5f32c6972a405af7498c37fa0428485508f2ed7423386548b6e","first_computed_at":"2026-05-17T23:40:23.681855Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:23.681855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"89OSeNFUCeyBSyH9sPetI2IHozfosv1TdOhbK+FxNqKg8EG3uquk02DaJTAVVqDt3MlPGwkaBGmw52jWpMxPDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:23.682539Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.02455","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef87c23524c8d59eac4cb2b85dd92669e19411617f905d787eb314c7da615109","sha256:858f45c68c1ecefce3a3758f67c5c5b3a425320782e8a0cf5cce2e56489ad9f7"],"state_sha256":"5d5e324d3e3e1d58870fae7efa88e90c6d711751fe37372b5ff8d1e2ae7d050b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FQYL2wA/FtoGRWCt92J7P9nk8jN1AvH3kOC/xzhtTckKFajouoT+hoxhZZQVeY3cXNgc0ZBoAYl1eMIc7YlzDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-26T02:02:47.562441Z","bundle_sha256":"4053c6f01ee68cce600e53801188d89f20b7a3b0cdfd0ccdd24324d95fdaacaa"}}