{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:HWL6IPB3K2BVHTGCRXQPQOJQ5E","short_pith_number":"pith:HWL6IPB3","schema_version":"1.0","canonical_sha256":"3d97e43c3b568353ccc28de0f83930e92a1030f9e9207bbee49db0d111a904b2","source":{"kind":"arxiv","id":"1604.03877","version":2},"attestation_state":"computed","paper":{"title":"Maximum Entropy Functions: Approximate Gacs-Korner for Distributed Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Asaf Cohen, Muriel M\\'edard, Salman Salamatian","submitted_at":"2016-04-13T17:26:45Z","abstract_excerpt":"Consider two correlated sources $X$ and $Y$ generated from a joint distribution $p_{X,Y}$. Their G\\'acs-K\\\"orner Common Information, a measure of common information that exploits the combinatorial structure of the distribution $p_{X,Y}$, leads to a source decomposition that exhibits the latent common parts in $X$ and $Y$. Using this source decomposition we construct an efficient distributed compression scheme, which can be efficiently used in the network setting as well. Then, we relax the combinatorial conditions on the source distribution, which results in an efficient scheme with a helper n"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1604.03877","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-04-13T17:26:45Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"e3fef75fc7f57aa561c7192d941f6bb40614c9d5c78edb10f5d7b8146e7e6cfc","abstract_canon_sha256":"6c7fd4971eddcb3c6eced72c2654472be87b2c710d9714a9d02869bffe0a01b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:07.185030Z","signature_b64":"2AkdJpX892o92Df2ryjjSPUBKUen0rkoHXcJH4sC7ZKagksFB2/jJtTFLczxkNvBn2tzSAcEhToCMG4HUrTLBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d97e43c3b568353ccc28de0f83930e92a1030f9e9207bbee49db0d111a904b2","last_reissued_at":"2026-05-18T01:17:07.184061Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:07.184061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Maximum Entropy Functions: Approximate Gacs-Korner for Distributed Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Asaf Cohen, Muriel M\\'edard, Salman Salamatian","submitted_at":"2016-04-13T17:26:45Z","abstract_excerpt":"Consider two correlated sources $X$ and $Y$ generated from a joint distribution $p_{X,Y}$. Their G\\'acs-K\\\"orner Common Information, a measure of common information that exploits the combinatorial structure of the distribution $p_{X,Y}$, leads to a source decomposition that exhibits the latent common parts in $X$ and $Y$. Using this source decomposition we construct an efficient distributed compression scheme, which can be efficiently used in the network setting as well. Then, we relax the combinatorial conditions on the source distribution, which results in an efficient scheme with a helper n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.03877","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1604.03877","created_at":"2026-05-18T01:17:07.184154+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.03877v2","created_at":"2026-05-18T01:17:07.184154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.03877","created_at":"2026-05-18T01:17:07.184154+00:00"},{"alias_kind":"pith_short_12","alias_value":"HWL6IPB3K2BV","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"HWL6IPB3K2BVHTGC","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"HWL6IPB3","created_at":"2026-05-18T12:30:22.444734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E","json":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E.json","graph_json":"https://pith.science/api/pith-number/HWL6IPB3K2BVHTGCRXQPQOJQ5E/graph.json","events_json":"https://pith.science/api/pith-number/HWL6IPB3K2BVHTGCRXQPQOJQ5E/events.json","paper":"https://pith.science/paper/HWL6IPB3"},"agent_actions":{"view_html":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E","download_json":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E.json","view_paper":"https://pith.science/paper/HWL6IPB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.03877&json=true","fetch_graph":"https://pith.science/api/pith-number/HWL6IPB3K2BVHTGCRXQPQOJQ5E/graph.json","fetch_events":"https://pith.science/api/pith-number/HWL6IPB3K2BVHTGCRXQPQOJQ5E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E/action/storage_attestation","attest_author":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E/action/author_attestation","sign_citation":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E/action/citation_signature","submit_replication":"https://pith.science/pith/HWL6IPB3K2BVHTGCRXQPQOJQ5E/action/replication_record"}},"created_at":"2026-05-18T01:17:07.184154+00:00","updated_at":"2026-05-18T01:17:07.184154+00:00"}