{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6WG3KAJH42ISMWNQFOLIHKETUX","short_pith_number":"pith:6WG3KAJH","canonical_record":{"source":{"id":"2208.04075","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-08T11:51:01Z","cross_cats_sorted":[],"title_canon_sha256":"f6641f700188c375e116562a0bfc09ae0c0d2c629335eb4c894e849ca9e6373d","abstract_canon_sha256":"40ddfef2ad81340dd76eb09af2e8928a37be34d4064871bc024c70ae48466e50"},"schema_version":"1.0"},"canonical_sha256":"f58db50127e6912659b02b9683a893a5e1487eed6ab00762b11dff7014d37232","source":{"kind":"arxiv","id":"2208.04075","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.04075","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"arxiv_version","alias_value":"2208.04075v1","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.04075","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_12","alias_value":"6WG3KAJH42IS","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_16","alias_value":"6WG3KAJH42ISMWNQ","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_8","alias_value":"6WG3KAJH","created_at":"2026-07-05T05:02:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6WG3KAJH42ISMWNQFOLIHKETUX","target":"record","payload":{"canonical_record":{"source":{"id":"2208.04075","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-08T11:51:01Z","cross_cats_sorted":[],"title_canon_sha256":"f6641f700188c375e116562a0bfc09ae0c0d2c629335eb4c894e849ca9e6373d","abstract_canon_sha256":"40ddfef2ad81340dd76eb09af2e8928a37be34d4064871bc024c70ae48466e50"},"schema_version":"1.0"},"canonical_sha256":"f58db50127e6912659b02b9683a893a5e1487eed6ab00762b11dff7014d37232","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:02:43.989167Z","signature_b64":"BgobEvzz1HKaz9lQmmAul5wxlNQFQKa4jRFdVtC8ekLXwCrWwN0/6Fn0ww9KePXYz2BIWdQbnnSbxnPYL7oRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f58db50127e6912659b02b9683a893a5e1487eed6ab00762b11dff7014d37232","last_reissued_at":"2026-07-05T05:02:43.988762Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:02:43.988762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.04075","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":"2026-07-05T05:02:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4UgTAQdwSh2mLS6bQBm2NhWMOUCyeaZRfG57c29o6bOVIrQWq/V9oNjJPek9aZ9GMcLs2mWcEpwXFBDOm7oJAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:36:36.056553Z"},"content_sha256":"f0156e1ab5c6bf392d0f0a656693ccc3b98b7e996aa264824ccd9d29d3512898","schema_version":"1.0","event_id":"sha256:f0156e1ab5c6bf392d0f0a656693ccc3b98b7e996aa264824ccd9d29d3512898"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6WG3KAJH42ISMWNQFOLIHKETUX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pairwise Learning via Stagewise Training in Proximal Setting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aliakbar Abdurahimov, Hilal AlQuabeh","submitted_at":"2022-08-08T11:51:01Z","abstract_excerpt":"The pairwise objective paradigms are an important and essential aspect of machine learning. Examples of machine learning approaches that use pairwise objective functions include differential network in face recognition, metric learning, bipartite learning, multiple kernel learning, and maximizing of area under the curve (AUC). Compared to pointwise learning, pairwise learning's sample size grows quadratically with the number of samples and thus its complexity. Researchers mostly address this challenge by utilizing an online learning system. Recent research has, however, offered adaptive sample"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.04075","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/2208.04075/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":"2026-07-05T05:02:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZZVyphyhEzWB8gFPA/PQ5m8yyreib1JALDBUhNtpHmcZXZO3xkmsPP89Kz+xrqdQiXvAf1HLJ3uERGYSJx4rCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:36:36.057047Z"},"content_sha256":"db8c20fd13e6bba40f68bed4aa2b44a76d4e84228959e6ffea71139916441c2b","schema_version":"1.0","event_id":"sha256:db8c20fd13e6bba40f68bed4aa2b44a76d4e84228959e6ffea71139916441c2b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6WG3KAJH42ISMWNQFOLIHKETUX/bundle.json","state_url":"https://pith.science/pith/6WG3KAJH42ISMWNQFOLIHKETUX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6WG3KAJH42ISMWNQFOLIHKETUX/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":"2026-08-04T23:36:36Z","links":{"resolver":"https://pith.science/pith/6WG3KAJH42ISMWNQFOLIHKETUX","bundle":"https://pith.science/pith/6WG3KAJH42ISMWNQFOLIHKETUX/bundle.json","state":"https://pith.science/pith/6WG3KAJH42ISMWNQFOLIHKETUX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6WG3KAJH42ISMWNQFOLIHKETUX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6WG3KAJH42ISMWNQFOLIHKETUX","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":"40ddfef2ad81340dd76eb09af2e8928a37be34d4064871bc024c70ae48466e50","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-08T11:51:01Z","title_canon_sha256":"f6641f700188c375e116562a0bfc09ae0c0d2c629335eb4c894e849ca9e6373d"},"schema_version":"1.0","source":{"id":"2208.04075","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.04075","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"arxiv_version","alias_value":"2208.04075v1","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.04075","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_12","alias_value":"6WG3KAJH42IS","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_16","alias_value":"6WG3KAJH42ISMWNQ","created_at":"2026-07-05T05:02:43Z"},{"alias_kind":"pith_short_8","alias_value":"6WG3KAJH","created_at":"2026-07-05T05:02:43Z"}],"graph_snapshots":[{"event_id":"sha256:db8c20fd13e6bba40f68bed4aa2b44a76d4e84228959e6ffea71139916441c2b","target":"graph","created_at":"2026-07-05T05:02:43Z","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/2208.04075/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The pairwise objective paradigms are an important and essential aspect of machine learning. Examples of machine learning approaches that use pairwise objective functions include differential network in face recognition, metric learning, bipartite learning, multiple kernel learning, and maximizing of area under the curve (AUC). Compared to pointwise learning, pairwise learning's sample size grows quadratically with the number of samples and thus its complexity. Researchers mostly address this challenge by utilizing an online learning system. Recent research has, however, offered adaptive sample","authors_text":"Aliakbar Abdurahimov, Hilal AlQuabeh","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-08T11:51:01Z","title":"Pairwise Learning via Stagewise Training in Proximal Setting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.04075","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:f0156e1ab5c6bf392d0f0a656693ccc3b98b7e996aa264824ccd9d29d3512898","target":"record","created_at":"2026-07-05T05:02:43Z","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":"40ddfef2ad81340dd76eb09af2e8928a37be34d4064871bc024c70ae48466e50","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-08T11:51:01Z","title_canon_sha256":"f6641f700188c375e116562a0bfc09ae0c0d2c629335eb4c894e849ca9e6373d"},"schema_version":"1.0","source":{"id":"2208.04075","kind":"arxiv","version":1}},"canonical_sha256":"f58db50127e6912659b02b9683a893a5e1487eed6ab00762b11dff7014d37232","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f58db50127e6912659b02b9683a893a5e1487eed6ab00762b11dff7014d37232","first_computed_at":"2026-07-05T05:02:43.988762Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:02:43.988762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BgobEvzz1HKaz9lQmmAul5wxlNQFQKa4jRFdVtC8ekLXwCrWwN0/6Fn0ww9KePXYz2BIWdQbnnSbxnPYL7oRDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:02:43.989167Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.04075","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0156e1ab5c6bf392d0f0a656693ccc3b98b7e996aa264824ccd9d29d3512898","sha256:db8c20fd13e6bba40f68bed4aa2b44a76d4e84228959e6ffea71139916441c2b"],"state_sha256":"cb42f0ff935d373b469e28fc23ef5532328ffd093e8bb71dd7bcd7439c856114"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ER6+1gZkiNMPKV+eUe6PcFRGqK3KiJU6asnHET8sq9mvpePkYoW2onHWbjA5OHuwdfn8eOthNpfUXyb9vXigBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T23:36:36.060459Z","bundle_sha256":"687340fad1708a1ec0507b9b14cbe8f477df2e543713b0c5b410e384005ece15"}}