{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SH4KNTYYIHJ7DPEHWFEVFPCEKQ","short_pith_number":"pith:SH4KNTYY","canonical_record":{"source":{"id":"2401.10139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-18T17:06:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bea45c466233d7153aa567c77903199570120f0584c42d9ad2b4e71197b27732","abstract_canon_sha256":"3f217b7ba947ab2f2b886780c52558b0a0abd88a8c5cb67b3143e53d77e434cb"},"schema_version":"1.0"},"canonical_sha256":"91f8a6cf1841d3f1bc87b14952bc44541cddf8de41499a1ca047f6a5976b707c","source":{"kind":"arxiv","id":"2401.10139","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10139","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10139v1","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10139","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_12","alias_value":"SH4KNTYYIHJ7","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_16","alias_value":"SH4KNTYYIHJ7DPEH","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_8","alias_value":"SH4KNTYY","created_at":"2026-07-05T07:35:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SH4KNTYYIHJ7DPEHWFEVFPCEKQ","target":"record","payload":{"canonical_record":{"source":{"id":"2401.10139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-18T17:06:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bea45c466233d7153aa567c77903199570120f0584c42d9ad2b4e71197b27732","abstract_canon_sha256":"3f217b7ba947ab2f2b886780c52558b0a0abd88a8c5cb67b3143e53d77e434cb"},"schema_version":"1.0"},"canonical_sha256":"91f8a6cf1841d3f1bc87b14952bc44541cddf8de41499a1ca047f6a5976b707c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:06.958412Z","signature_b64":"Aao0LxiuIAKiPMWY0bKKkJ3EBFXtBwj0WT5rqqrzp9X5gHwMs775Oww28TamKoOC/I4hXxdPyJwLBeE2QnLZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91f8a6cf1841d3f1bc87b14952bc44541cddf8de41499a1ca047f6a5976b707c","last_reissued_at":"2026-07-05T07:35:06.957976Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:06.957976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.10139","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":"2026-07-05T07:35:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wSxnapQRUheoc+tVgPe6NhoAHrjRLwa+1w1Qh9QEllIzkFAL+PTn9N9v+E6btvYEyKsn8lbSj/rzcM6iKz/WCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:35:34.333426Z"},"content_sha256":"1021c2a89a4f242f88775c05c5686d5e048ab59beabaf7432e53b5bd918730d0","schema_version":"1.0","event_id":"sha256:1021c2a89a4f242f88775c05c5686d5e048ab59beabaf7432e53b5bd918730d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SH4KNTYYIHJ7DPEHWFEVFPCEKQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Model Compression Techniques in Biometrics Applications: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ana F. Sequeira, Eduarda Caldeira, Marco Huber, Naser Damer, Pedro C. Neto","submitted_at":"2024-01-18T17:06:21Z","abstract_excerpt":"The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. This paper aims to systematize the current literature on thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10139","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/2401.10139/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":"2026-07-05T07:35:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YodHa6fnqmQMk9XcxdwXKE7Y9MPHUeY3fonRdtCWmHnOoscnh5rbRLLlzLabG2bjKv3Ic6mCmDoTxqEhJD7VBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:35:34.333978Z"},"content_sha256":"80c64e9a0f3ed00aae896aa27a1ca0797c4cba2c41d8e966916948a7e687f737","schema_version":"1.0","event_id":"sha256:80c64e9a0f3ed00aae896aa27a1ca0797c4cba2c41d8e966916948a7e687f737"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/bundle.json","state_url":"https://pith.science/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/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":"2026-08-11T17:35:34Z","links":{"resolver":"https://pith.science/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ","bundle":"https://pith.science/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/bundle.json","state":"https://pith.science/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SH4KNTYYIHJ7DPEHWFEVFPCEKQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SH4KNTYYIHJ7DPEHWFEVFPCEKQ","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":"3f217b7ba947ab2f2b886780c52558b0a0abd88a8c5cb67b3143e53d77e434cb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-18T17:06:21Z","title_canon_sha256":"bea45c466233d7153aa567c77903199570120f0584c42d9ad2b4e71197b27732"},"schema_version":"1.0","source":{"id":"2401.10139","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10139","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10139v1","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10139","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_12","alias_value":"SH4KNTYYIHJ7","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_16","alias_value":"SH4KNTYYIHJ7DPEH","created_at":"2026-07-05T07:35:06Z"},{"alias_kind":"pith_short_8","alias_value":"SH4KNTYY","created_at":"2026-07-05T07:35:06Z"}],"graph_snapshots":[{"event_id":"sha256:80c64e9a0f3ed00aae896aa27a1ca0797c4cba2c41d8e966916948a7e687f737","target":"graph","created_at":"2026-07-05T07:35:06Z","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/2401.10139/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. This paper aims to systematize the current literature on thi","authors_text":"Ana F. Sequeira, Eduarda Caldeira, Marco Huber, Naser Damer, Pedro C. Neto","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-18T17:06:21Z","title":"Model Compression Techniques in Biometrics Applications: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10139","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":"sha256:1021c2a89a4f242f88775c05c5686d5e048ab59beabaf7432e53b5bd918730d0","target":"record","created_at":"2026-07-05T07:35:06Z","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":"3f217b7ba947ab2f2b886780c52558b0a0abd88a8c5cb67b3143e53d77e434cb","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-18T17:06:21Z","title_canon_sha256":"bea45c466233d7153aa567c77903199570120f0584c42d9ad2b4e71197b27732"},"schema_version":"1.0","source":{"id":"2401.10139","kind":"arxiv","version":1}},"canonical_sha256":"91f8a6cf1841d3f1bc87b14952bc44541cddf8de41499a1ca047f6a5976b707c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91f8a6cf1841d3f1bc87b14952bc44541cddf8de41499a1ca047f6a5976b707c","first_computed_at":"2026-07-05T07:35:06.957976Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:35:06.957976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Aao0LxiuIAKiPMWY0bKKkJ3EBFXtBwj0WT5rqqrzp9X5gHwMs775Oww28TamKoOC/I4hXxdPyJwLBeE2QnLZDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:35:06.958412Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.10139","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1021c2a89a4f242f88775c05c5686d5e048ab59beabaf7432e53b5bd918730d0","sha256:80c64e9a0f3ed00aae896aa27a1ca0797c4cba2c41d8e966916948a7e687f737"],"state_sha256":"555ebf53e579b363d2b40f38fc94fe78b6c933126cb720fab2a2d6213b09878b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZTlL2LjaK1CpmZ10VDG90Py8H9Ua+mq2sNWTmz4SoVMkKWLPLVkyZXk/1fwD5RqbBzHeT1DQw0WIPhUlQU2Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T17:35:34.339122Z","bundle_sha256":"ac71ae8497a72081c2623163a70251ae87880b72352aea26166092f3946ed2a6"}}