{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:AYEN24ZHMMCZATC4HWFCHCDS5X","short_pith_number":"pith:AYEN24ZH","canonical_record":{"source":{"id":"1707.07791","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-25T02:28:22Z","cross_cats_sorted":[],"title_canon_sha256":"8568bf1f6eaa05e471d2c38cfe696833c23bbe2082c4bb8da6f37666ebb4bcfd","abstract_canon_sha256":"19777cdab8df4929eca2fbb3777da5b5e0a81ae47da1b3a40d98cfbe85d37d9c"},"schema_version":"1.0"},"canonical_sha256":"0608dd73276305904c5c3d8a238872ede123fafd00ff6555489ccab83accbe05","source":{"kind":"arxiv","id":"1707.07791","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07791","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07791v1","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07791","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"pith_short_12","alias_value":"AYEN24ZHMMCZ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AYEN24ZHMMCZATC4","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AYEN24ZH","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:AYEN24ZHMMCZATC4HWFCHCDS5X","target":"record","payload":{"canonical_record":{"source":{"id":"1707.07791","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-25T02:28:22Z","cross_cats_sorted":[],"title_canon_sha256":"8568bf1f6eaa05e471d2c38cfe696833c23bbe2082c4bb8da6f37666ebb4bcfd","abstract_canon_sha256":"19777cdab8df4929eca2fbb3777da5b5e0a81ae47da1b3a40d98cfbe85d37d9c"},"schema_version":"1.0"},"canonical_sha256":"0608dd73276305904c5c3d8a238872ede123fafd00ff6555489ccab83accbe05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:30.340049Z","signature_b64":"SOC+hHOEXvPiL7NR7CfeJY+OXkBMavS+DWQWFc/2tBxyB6+k+QLJnrULGO47n8VRUPU/UzxZae5Ou6iAzV0WDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0608dd73276305904c5c3d8a238872ede123fafd00ff6555489ccab83accbe05","last_reissued_at":"2026-05-18T00:39:30.339311Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:30.339311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.07791","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":"2026-05-18T00:39:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pcYW6CzRZLnOK4IrMUTcAnWQLRMZ8pM7z1Uq/dcCx3s7iiq3IgE7LV2vLXtwtjdEWdbDZcLahvbnjGLL+nC1DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:19:17.631653Z"},"content_sha256":"c0552165e5dc2866f0b5653194414efc913463e999d719373d9d92d66f9b9559","schema_version":"1.0","event_id":"sha256:c0552165e5dc2866f0b5653194414efc913463e999d719373d9d92d66f9b9559"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:AYEN24ZHMMCZATC4HWFCHCDS5X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander G. Hauptmann, De Cheng, Nanning Zheng, Weiwei Shi, Yihong Gong, Zhihui Li","submitted_at":"2017-07-25T02:28:22Z","abstract_excerpt":"Learning the distance metric between pairs of examples is of great importance for visual recognition, especially for person re-identification (Re-Id). Recently, the contrastive and triplet loss are proposed to enhance the discriminative power of the deeply learned features, and have achieved remarkable success. As can be seen, either the contrastive or triplet loss is just one special case of the Euclidean distance relationships among these training samples. Therefore, we propose a structured graph Laplacian embedding algorithm, which can formulate all these structured distance relationships i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07791","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":""},"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":"2026-05-18T00:39:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hSP29iR215wgU4vd63Ycj46G/rJnx2GA/eOjmjD3DFJLQ6ZBBGnMh1VXQ0p5hrO11tcjSnGqC0tN0+SXLMpABQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:19:17.632248Z"},"content_sha256":"3e1bee801ccac97ac8f8e80069aa7f467a6c0c28232f62e7cc38165ba124cebb","schema_version":"1.0","event_id":"sha256:3e1bee801ccac97ac8f8e80069aa7f467a6c0c28232f62e7cc38165ba124cebb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/bundle.json","state_url":"https://pith.science/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/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":"2026-05-25T19:19:17Z","links":{"resolver":"https://pith.science/pith/AYEN24ZHMMCZATC4HWFCHCDS5X","bundle":"https://pith.science/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/bundle.json","state":"https://pith.science/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AYEN24ZHMMCZATC4HWFCHCDS5X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:AYEN24ZHMMCZATC4HWFCHCDS5X","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":"19777cdab8df4929eca2fbb3777da5b5e0a81ae47da1b3a40d98cfbe85d37d9c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-25T02:28:22Z","title_canon_sha256":"8568bf1f6eaa05e471d2c38cfe696833c23bbe2082c4bb8da6f37666ebb4bcfd"},"schema_version":"1.0","source":{"id":"1707.07791","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07791","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07791v1","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07791","created_at":"2026-05-18T00:39:30Z"},{"alias_kind":"pith_short_12","alias_value":"AYEN24ZHMMCZ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AYEN24ZHMMCZATC4","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AYEN24ZH","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:3e1bee801ccac97ac8f8e80069aa7f467a6c0c28232f62e7cc38165ba124cebb","target":"graph","created_at":"2026-05-18T00:39:30Z","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":"Learning the distance metric between pairs of examples is of great importance for visual recognition, especially for person re-identification (Re-Id). Recently, the contrastive and triplet loss are proposed to enhance the discriminative power of the deeply learned features, and have achieved remarkable success. As can be seen, either the contrastive or triplet loss is just one special case of the Euclidean distance relationships among these training samples. Therefore, we propose a structured graph Laplacian embedding algorithm, which can formulate all these structured distance relationships i","authors_text":"Alexander G. Hauptmann, De Cheng, Nanning Zheng, Weiwei Shi, Yihong Gong, Zhihui Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-25T02:28:22Z","title":"Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07791","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:c0552165e5dc2866f0b5653194414efc913463e999d719373d9d92d66f9b9559","target":"record","created_at":"2026-05-18T00:39:30Z","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":"19777cdab8df4929eca2fbb3777da5b5e0a81ae47da1b3a40d98cfbe85d37d9c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-25T02:28:22Z","title_canon_sha256":"8568bf1f6eaa05e471d2c38cfe696833c23bbe2082c4bb8da6f37666ebb4bcfd"},"schema_version":"1.0","source":{"id":"1707.07791","kind":"arxiv","version":1}},"canonical_sha256":"0608dd73276305904c5c3d8a238872ede123fafd00ff6555489ccab83accbe05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0608dd73276305904c5c3d8a238872ede123fafd00ff6555489ccab83accbe05","first_computed_at":"2026-05-18T00:39:30.339311Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:30.339311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SOC+hHOEXvPiL7NR7CfeJY+OXkBMavS+DWQWFc/2tBxyB6+k+QLJnrULGO47n8VRUPU/UzxZae5Ou6iAzV0WDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:30.340049Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.07791","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0552165e5dc2866f0b5653194414efc913463e999d719373d9d92d66f9b9559","sha256:3e1bee801ccac97ac8f8e80069aa7f467a6c0c28232f62e7cc38165ba124cebb"],"state_sha256":"7156e81168c43dda5ebbaf2862511cf70814635b6b995cc1ce1b1569bea7c136"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jxg8j66iIW4dqa82qFWyQUyT0pWrPDxPy3goQp9fObsjT/VDqM89ccSH8n9l2ggUUjqEr7l8x9GGivliYgXeCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:19:17.635777Z","bundle_sha256":"6856ec850209ff92a625a97bf77ebeaaf3050f289b061f749f7453889736adcc"}}