{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:L4SMG66LKLKAC4KNK5RN6D2M3G","short_pith_number":"pith:L4SMG66L","canonical_record":{"source":{"id":"1806.07533","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-06-20T03:20:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"aa63c1cea6a2133b282e77bd1a1f03e4f35018b096a3eadb4532b5439a7d90ad","abstract_canon_sha256":"f92aa60e3f534b0230a1fabfaf23d63267eaa72839b0a3abcd35ab869bd6fb2c"},"schema_version":"1.0"},"canonical_sha256":"5f24c37bcb52d401714d5762df0f4cd98894caec993936e6cd3ea508daeb0a0d","source":{"kind":"arxiv","id":"1806.07533","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.07533","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"arxiv_version","alias_value":"1806.07533v1","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07533","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"pith_short_12","alias_value":"L4SMG66LKLKA","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L4SMG66LKLKAC4KN","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L4SMG66L","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:L4SMG66LKLKAC4KNK5RN6D2M3G","target":"record","payload":{"canonical_record":{"source":{"id":"1806.07533","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-06-20T03:20:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"aa63c1cea6a2133b282e77bd1a1f03e4f35018b096a3eadb4532b5439a7d90ad","abstract_canon_sha256":"f92aa60e3f534b0230a1fabfaf23d63267eaa72839b0a3abcd35ab869bd6fb2c"},"schema_version":"1.0"},"canonical_sha256":"5f24c37bcb52d401714d5762df0f4cd98894caec993936e6cd3ea508daeb0a0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:47.472533Z","signature_b64":"vlVTsR+PCgF5n24JE9qqNoRfGX3mvr/WnRFVmr1bLeKoiZAl0a1CrnXVeSJQxJol/8rDaw+j/3YTxxbamfLdCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f24c37bcb52d401714d5762df0f4cd98894caec993936e6cd3ea508daeb0a0d","last_reissued_at":"2026-05-18T00:12:47.471784Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:47.471784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.07533","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":"2026-05-18T00:12:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IzQAou1YORDEQJLgSsJelii3vKzn0NTcex2c1e3lHAi1LWFzBj2H84H0bKc3zAh3jcPq4gXWzjaAO7dKN8gcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T06:27:18.817221Z"},"content_sha256":"6c2b83d1377c39797acde7de20bb5b11a0b2a955abbd1430044aa4fe3b48edff","schema_version":"1.0","event_id":"sha256:6c2b83d1377c39797acde7de20bb5b11a0b2a955abbd1430044aa4fe3b48edff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:L4SMG66LKLKAC4KNK5RN6D2M3G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Chuanhai Liu, Glen DePalma, Sanvesh Srivastava","submitted_at":"2018-06-20T03:20:04Z","abstract_excerpt":"The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it requires multiple passes through the full data. We address this problem by proposing an asynchronous and distributed generalization of the EM called the Distributed EM (DEM). Using DEM, existing EM-type algorithms are easily extended to massive data settings by exploiting the divide-and-conquer technique and widely available computing power, such as grid "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07533","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":"2026-05-18T00:12:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aGEdNnKnMbZh2DwwRPBGgBsCjQs9cMsIK+4BZ0VHtpqXD/js4/j87xm3EZNdHueoTVuUndBcUE/eOY1pNqoLBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T06:27:18.817824Z"},"content_sha256":"58cf2488f504e10fb9ce96d369959c00595bcb98b6db7ea4acdb9b4d4f889df4","schema_version":"1.0","event_id":"sha256:58cf2488f504e10fb9ce96d369959c00595bcb98b6db7ea4acdb9b4d4f889df4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/bundle.json","state_url":"https://pith.science/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/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":"2026-06-12T06:27:18Z","links":{"resolver":"https://pith.science/pith/L4SMG66LKLKAC4KNK5RN6D2M3G","bundle":"https://pith.science/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/bundle.json","state":"https://pith.science/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L4SMG66LKLKAC4KNK5RN6D2M3G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:L4SMG66LKLKAC4KNK5RN6D2M3G","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":"f92aa60e3f534b0230a1fabfaf23d63267eaa72839b0a3abcd35ab869bd6fb2c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-06-20T03:20:04Z","title_canon_sha256":"aa63c1cea6a2133b282e77bd1a1f03e4f35018b096a3eadb4532b5439a7d90ad"},"schema_version":"1.0","source":{"id":"1806.07533","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.07533","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"arxiv_version","alias_value":"1806.07533v1","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07533","created_at":"2026-05-18T00:12:47Z"},{"alias_kind":"pith_short_12","alias_value":"L4SMG66LKLKA","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L4SMG66LKLKAC4KN","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L4SMG66L","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:58cf2488f504e10fb9ce96d369959c00595bcb98b6db7ea4acdb9b4d4f889df4","target":"graph","created_at":"2026-05-18T00:12:47Z","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":"The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it requires multiple passes through the full data. We address this problem by proposing an asynchronous and distributed generalization of the EM called the Distributed EM (DEM). Using DEM, existing EM-type algorithms are easily extended to massive data settings by exploiting the divide-and-conquer technique and widely available computing power, such as grid ","authors_text":"Chuanhai Liu, Glen DePalma, Sanvesh Srivastava","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-06-20T03:20:04Z","title":"An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07533","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":"sha256:6c2b83d1377c39797acde7de20bb5b11a0b2a955abbd1430044aa4fe3b48edff","target":"record","created_at":"2026-05-18T00:12:47Z","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":"f92aa60e3f534b0230a1fabfaf23d63267eaa72839b0a3abcd35ab869bd6fb2c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-06-20T03:20:04Z","title_canon_sha256":"aa63c1cea6a2133b282e77bd1a1f03e4f35018b096a3eadb4532b5439a7d90ad"},"schema_version":"1.0","source":{"id":"1806.07533","kind":"arxiv","version":1}},"canonical_sha256":"5f24c37bcb52d401714d5762df0f4cd98894caec993936e6cd3ea508daeb0a0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f24c37bcb52d401714d5762df0f4cd98894caec993936e6cd3ea508daeb0a0d","first_computed_at":"2026-05-18T00:12:47.471784Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:47.471784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vlVTsR+PCgF5n24JE9qqNoRfGX3mvr/WnRFVmr1bLeKoiZAl0a1CrnXVeSJQxJol/8rDaw+j/3YTxxbamfLdCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:47.472533Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.07533","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c2b83d1377c39797acde7de20bb5b11a0b2a955abbd1430044aa4fe3b48edff","sha256:58cf2488f504e10fb9ce96d369959c00595bcb98b6db7ea4acdb9b4d4f889df4"],"state_sha256":"8b1a643015845f2759d5192eaf3f574550cc973f689fb5ff747f7cb67b5eb110"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Spwl/7N60NGF5w4jftsm4+Yr5pOa+PhZnGV/aykci0YSrwpeWkYxHBibhgi5dMV29rfszzQjJz+EtQBxZhmOAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T06:27:18.820968Z","bundle_sha256":"33cc686f1842dffbdacba6f2f8b6d8e9cf8af86bc2f57f0c8ecd6fa9ea9704db"}}