{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:7YZW7WANLIW6EHH2YM4AOCXP5V","short_pith_number":"pith:7YZW7WAN","canonical_record":{"source":{"id":"1602.06049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-19T05:55:08Z","cross_cats_sorted":[],"title_canon_sha256":"9a85172c5fb3b86ce65180923d6c8c18958cf4a67336efbdf761cafbb3225083","abstract_canon_sha256":"2ec383f33b32140a1b0bdf3479b278f1b4041801bcb6114c173a50aae07a397d"},"schema_version":"1.0"},"canonical_sha256":"fe336fd80d5a2de21cfac338070aefed58b737ca8139f40c0e50511f0888669f","source":{"kind":"arxiv","id":"1602.06049","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.06049","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"arxiv_version","alias_value":"1602.06049v1","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.06049","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"pith_short_12","alias_value":"7YZW7WANLIW6","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7YZW7WANLIW6EHH2","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7YZW7WAN","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:7YZW7WANLIW6EHH2YM4AOCXP5V","target":"record","payload":{"canonical_record":{"source":{"id":"1602.06049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-19T05:55:08Z","cross_cats_sorted":[],"title_canon_sha256":"9a85172c5fb3b86ce65180923d6c8c18958cf4a67336efbdf761cafbb3225083","abstract_canon_sha256":"2ec383f33b32140a1b0bdf3479b278f1b4041801bcb6114c173a50aae07a397d"},"schema_version":"1.0"},"canonical_sha256":"fe336fd80d5a2de21cfac338070aefed58b737ca8139f40c0e50511f0888669f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:21.752695Z","signature_b64":"F2P40C8DXb4t7MQ3qIbvYcRVJlu4CYVuw5u6lKmuUClC04yyvDZhziDXOzXWH2uQOj049fwMDSOu5F7KsPn5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe336fd80d5a2de21cfac338070aefed58b737ca8139f40c0e50511f0888669f","last_reissued_at":"2026-05-18T01:20:21.751850Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:21.751850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.06049","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":"2026-05-18T01:20:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tFFkg1G34M6NDqenCk6XgtQBfR1r2ohc1uceK0CbkvGaspOyc8Al+POa2x0Ud7CAJQT4eKFd2PrEJirXxbuwBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:51:11.113741Z"},"content_sha256":"28bf44381ca00e90ce0a003470868bdd6792450b8fc8e73f99602fbdeb4fff94","schema_version":"1.0","event_id":"sha256:28bf44381ca00e90ce0a003470868bdd6792450b8fc8e73f99602fbdeb4fff94"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:7YZW7WANLIW6EHH2YM4AOCXP5V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaling up Dynamic Topic Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu","submitted_at":"2016-02-19T05:55:08Z","abstract_excerpt":"Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics.\n  This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.06049","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":"2026-05-18T01:20:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X8b82C1P2DOo1KEAG5O2FEJH4dgbosOCiKSaazHbj7YeuMpqviA77630nEJcaNoRMeFo1Qe0ybbXo2fltsonDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:51:11.114536Z"},"content_sha256":"d3e178bb41d49f7cd879935d3bc50f05f06a3b1c8bccae88f3ab86be55c441be","schema_version":"1.0","event_id":"sha256:d3e178bb41d49f7cd879935d3bc50f05f06a3b1c8bccae88f3ab86be55c441be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/bundle.json","state_url":"https://pith.science/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/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":"2026-05-30T13:51:11Z","links":{"resolver":"https://pith.science/pith/7YZW7WANLIW6EHH2YM4AOCXP5V","bundle":"https://pith.science/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/bundle.json","state":"https://pith.science/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7YZW7WANLIW6EHH2YM4AOCXP5V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:7YZW7WANLIW6EHH2YM4AOCXP5V","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":"2ec383f33b32140a1b0bdf3479b278f1b4041801bcb6114c173a50aae07a397d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-19T05:55:08Z","title_canon_sha256":"9a85172c5fb3b86ce65180923d6c8c18958cf4a67336efbdf761cafbb3225083"},"schema_version":"1.0","source":{"id":"1602.06049","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.06049","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"arxiv_version","alias_value":"1602.06049v1","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.06049","created_at":"2026-05-18T01:20:21Z"},{"alias_kind":"pith_short_12","alias_value":"7YZW7WANLIW6","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7YZW7WANLIW6EHH2","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7YZW7WAN","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:d3e178bb41d49f7cd879935d3bc50f05f06a3b1c8bccae88f3ab86be55c441be","target":"graph","created_at":"2026-05-18T01:20:21Z","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":"Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods are all batch algorithms that scan the full dataset before each update of the model and make inexact variational approximations with mean-field assumptions. Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics.\n  This paper fills this research void, and presents a fast and parallelizable inference algorithm using Gibbs Sampling with Stochastic Gradie","authors_text":"Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-19T05:55:08Z","title":"Scaling up Dynamic Topic Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.06049","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":"sha256:28bf44381ca00e90ce0a003470868bdd6792450b8fc8e73f99602fbdeb4fff94","target":"record","created_at":"2026-05-18T01:20:21Z","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":"2ec383f33b32140a1b0bdf3479b278f1b4041801bcb6114c173a50aae07a397d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-19T05:55:08Z","title_canon_sha256":"9a85172c5fb3b86ce65180923d6c8c18958cf4a67336efbdf761cafbb3225083"},"schema_version":"1.0","source":{"id":"1602.06049","kind":"arxiv","version":1}},"canonical_sha256":"fe336fd80d5a2de21cfac338070aefed58b737ca8139f40c0e50511f0888669f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe336fd80d5a2de21cfac338070aefed58b737ca8139f40c0e50511f0888669f","first_computed_at":"2026-05-18T01:20:21.751850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:21.751850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F2P40C8DXb4t7MQ3qIbvYcRVJlu4CYVuw5u6lKmuUClC04yyvDZhziDXOzXWH2uQOj049fwMDSOu5F7KsPn5Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:21.752695Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.06049","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28bf44381ca00e90ce0a003470868bdd6792450b8fc8e73f99602fbdeb4fff94","sha256:d3e178bb41d49f7cd879935d3bc50f05f06a3b1c8bccae88f3ab86be55c441be"],"state_sha256":"36e17a10046fc337c32e4495e9500b4034ca7d30da484e109c350dcfcba3dcc2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xoLieE165EqExd5P0cL4FkJGue4q+AW7etddBwNxXNHW5xQzHdr1KnZe7lmKlDc4jf3/WqbeIcK+wf9zr4C3Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T13:51:11.118210Z","bundle_sha256":"9b04bd465d72d49216a68cd78961b55a10638773b7d6bf879ee4bfa866225a5d"}}