{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:TAUKDRRTVLWQDCDR6PYOSPSNVQ","short_pith_number":"pith:TAUKDRRT","canonical_record":{"source":{"id":"2312.10602","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-17T04:41:07Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"172f98546279765aed0de306982487b54b4c292a998541097d57db100c99e122","abstract_canon_sha256":"ef61bae340bfb14ccb3d41bbd6d764671511b05fed165d21aa695ee68c994d67"},"schema_version":"1.0"},"canonical_sha256":"9828a1c633aaed018871f3f0e93e4dac04485fe70d93eebd070c176bb0f3a416","source":{"kind":"arxiv","id":"2312.10602","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.10602","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"arxiv_version","alias_value":"2312.10602v1","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10602","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_12","alias_value":"TAUKDRRTVLWQ","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_16","alias_value":"TAUKDRRTVLWQDCDR","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_8","alias_value":"TAUKDRRT","created_at":"2026-07-05T07:25:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:TAUKDRRTVLWQDCDR6PYOSPSNVQ","target":"record","payload":{"canonical_record":{"source":{"id":"2312.10602","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-17T04:41:07Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"172f98546279765aed0de306982487b54b4c292a998541097d57db100c99e122","abstract_canon_sha256":"ef61bae340bfb14ccb3d41bbd6d764671511b05fed165d21aa695ee68c994d67"},"schema_version":"1.0"},"canonical_sha256":"9828a1c633aaed018871f3f0e93e4dac04485fe70d93eebd070c176bb0f3a416","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:25:09.290571Z","signature_b64":"n1YAfDh70M7NdmJlzIqi424EEaO++kmrS53jgESjYmb4WCRmemrHleSUMLSDEfBV46CQUU91y+LHaDL0nGstCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9828a1c633aaed018871f3f0e93e4dac04485fe70d93eebd070c176bb0f3a416","last_reissued_at":"2026-07-05T07:25:09.290207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:25:09.290207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.10602","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":"2026-07-05T07:25:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0vANKfX9daoVTqRnQhUAdSfRrVzmvP2AZbvTc0hidvH9Yvfcc1JQ4CQLHXIsxJYsCUzKpkRxJeL+pk/DZegWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:51:24.625604Z"},"content_sha256":"ecd15d83adca4bbf01a6b95827434930890d1068abac08e828a8176304687e21","schema_version":"1.0","event_id":"sha256:ecd15d83adca4bbf01a6b95827434930890d1068abac08e828a8176304687e21"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:TAUKDRRTVLWQDCDR6PYOSPSNVQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Weighted K-Center Algorithm for Data Subset Selection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Pranjal Awasthi, Sanjiv Kumar, Srikumar Ramalingam","submitted_at":"2023-12-17T04:41:07Z","abstract_excerpt":"The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior methods are shown to achieve impressive results: (1) margin sampling that focuses on selecting points with high uncertainty, and (2) core-sets or clustering methods such as k-center for informative and diverse subsets. We are not aware of any"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10602","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/2312.10602/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":"2026-07-05T07:25:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4iqr6Tw+PHfUBtEs/bSt945T7cv0ekgy2IB4zDuBrjaabtqfu29CkLM4ZAuBh5kPRhBaIgmQdfscxflm5y+9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:51:24.626143Z"},"content_sha256":"73ec5e679413afc4c992d697d6bef149be7fce8b90720e327f2825eef1b84343","schema_version":"1.0","event_id":"sha256:73ec5e679413afc4c992d697d6bef149be7fce8b90720e327f2825eef1b84343"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/bundle.json","state_url":"https://pith.science/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/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":"2026-08-10T22:51:24Z","links":{"resolver":"https://pith.science/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ","bundle":"https://pith.science/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/bundle.json","state":"https://pith.science/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TAUKDRRTVLWQDCDR6PYOSPSNVQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TAUKDRRTVLWQDCDR6PYOSPSNVQ","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":"ef61bae340bfb14ccb3d41bbd6d764671511b05fed165d21aa695ee68c994d67","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-17T04:41:07Z","title_canon_sha256":"172f98546279765aed0de306982487b54b4c292a998541097d57db100c99e122"},"schema_version":"1.0","source":{"id":"2312.10602","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.10602","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"arxiv_version","alias_value":"2312.10602v1","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10602","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_12","alias_value":"TAUKDRRTVLWQ","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_16","alias_value":"TAUKDRRTVLWQDCDR","created_at":"2026-07-05T07:25:09Z"},{"alias_kind":"pith_short_8","alias_value":"TAUKDRRT","created_at":"2026-07-05T07:25:09Z"}],"graph_snapshots":[{"event_id":"sha256:73ec5e679413afc4c992d697d6bef149be7fce8b90720e327f2825eef1b84343","target":"graph","created_at":"2026-07-05T07:25:09Z","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/2312.10602/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The success of deep learning hinges on enormous data and large models, which require labor-intensive annotations and heavy computation costs. Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data, which can then be used to produce similar models as the ones trained with full data. Two prior methods are shown to achieve impressive results: (1) margin sampling that focuses on selecting points with high uncertainty, and (2) core-sets or clustering methods such as k-center for informative and diverse subsets. We are not aware of any","authors_text":"Pranjal Awasthi, Sanjiv Kumar, Srikumar Ramalingam","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-17T04:41:07Z","title":"A Weighted K-Center Algorithm for Data Subset Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10602","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:ecd15d83adca4bbf01a6b95827434930890d1068abac08e828a8176304687e21","target":"record","created_at":"2026-07-05T07:25:09Z","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":"ef61bae340bfb14ccb3d41bbd6d764671511b05fed165d21aa695ee68c994d67","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-17T04:41:07Z","title_canon_sha256":"172f98546279765aed0de306982487b54b4c292a998541097d57db100c99e122"},"schema_version":"1.0","source":{"id":"2312.10602","kind":"arxiv","version":1}},"canonical_sha256":"9828a1c633aaed018871f3f0e93e4dac04485fe70d93eebd070c176bb0f3a416","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9828a1c633aaed018871f3f0e93e4dac04485fe70d93eebd070c176bb0f3a416","first_computed_at":"2026-07-05T07:25:09.290207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:25:09.290207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n1YAfDh70M7NdmJlzIqi424EEaO++kmrS53jgESjYmb4WCRmemrHleSUMLSDEfBV46CQUU91y+LHaDL0nGstCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:25:09.290571Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.10602","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ecd15d83adca4bbf01a6b95827434930890d1068abac08e828a8176304687e21","sha256:73ec5e679413afc4c992d697d6bef149be7fce8b90720e327f2825eef1b84343"],"state_sha256":"e27b977fd495e25918344137baf46718186c6e7b28946911d99b86d4cbfd3792"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MPm3Lnx5qovvE/XAql2o/M35w7fCnZfnz02Hf9j6C6yotUi3U44htGis97pbQvaXft36DEOx1UKn6arFoeKcDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T22:51:24.630159Z","bundle_sha256":"16852f68b116813e2cb4a57231f271ff408967c12f14c996fb99a0e6152242c3"}}