{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3RLEY2EMFVDRSDGOF5P57G6IMU","short_pith_number":"pith:3RLEY2EM","canonical_record":{"source":{"id":"2310.04978","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T02:56:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dbbc8946330c920a8394803d1198318a97347868cf3fba9f16b21af15e698b97","abstract_canon_sha256":"26c1abe91bb3a5fb9ec4749788867c19ed5182c8a998b9015f409f1fdfd370bb"},"schema_version":"1.0"},"canonical_sha256":"dc564c688c2d47190cce2f5fdf9bc8651cdf989b27204e845fc16cd73708c933","source":{"kind":"arxiv","id":"2310.04978","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04978","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04978v1","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04978","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_12","alias_value":"3RLEY2EMFVDR","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_16","alias_value":"3RLEY2EMFVDRSDGO","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_8","alias_value":"3RLEY2EM","created_at":"2026-07-05T06:58:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3RLEY2EMFVDRSDGOF5P57G6IMU","target":"record","payload":{"canonical_record":{"source":{"id":"2310.04978","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T02:56:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dbbc8946330c920a8394803d1198318a97347868cf3fba9f16b21af15e698b97","abstract_canon_sha256":"26c1abe91bb3a5fb9ec4749788867c19ed5182c8a998b9015f409f1fdfd370bb"},"schema_version":"1.0"},"canonical_sha256":"dc564c688c2d47190cce2f5fdf9bc8651cdf989b27204e845fc16cd73708c933","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:25.177953Z","signature_b64":"bhQ4QQqF/yuxcRXDjEVGEOrZvnrgNKKh5N4z5eHRV9CyemuUnHxXVeNlHB+hKxksv9JrelZhzjxpincKViteAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc564c688c2d47190cce2f5fdf9bc8651cdf989b27204e845fc16cd73708c933","last_reissued_at":"2026-07-05T06:58:25.177536Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:25.177536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.04978","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":"2026-07-05T06:58:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/5PZfJSRrgKFFfOk9AG0clv4DTw+YesRYFQz7lkCMIB6/xbbaGkWuVxmm2zg3dwfl2F+u0E2TrJhmSjKMbuYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:48:51.724048Z"},"content_sha256":"0292423b7efd7cfc77a98aca649b14ab3bdda2349943eb192e56f62b8872aa2a","schema_version":"1.0","event_id":"sha256:0292423b7efd7cfc77a98aca649b14ab3bdda2349943eb192e56f62b8872aa2a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3RLEY2EMFVDRSDGOF5P57G6IMU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TopicAdapt- An Inter-Corpora Topics Adaptation Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Kevin Chen-Chuan Chang, Pritom Saha Akash, Trisha Das","submitted_at":"2023-10-08T02:56:44Z","abstract_excerpt":"Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivity to user guidance, sensitivity to the amount and quality of data, and the inability to adapt learned topics from one corpus to another. To address these challenges, this paper proposes a neural topic model, TopicAdapt, that can adapt relevant topics from a related source corpus and also discover new topics in a target corpus that are absent in the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04978","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/2310.04978/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":"2026-07-05T06:58:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pLK1SKF7mfx4LxSKT+RB1UEVAJNO1GNOxFqsN2FIZAPn1xeIWw8PjcActJ4ggHzkuWKn5iMWZxMuIGhQ3+LBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:48:51.725028Z"},"content_sha256":"ab606f2697dbeeef76333f4556cfb3b1a43133d1ca717f4cad6e6619e459ed59","schema_version":"1.0","event_id":"sha256:ab606f2697dbeeef76333f4556cfb3b1a43133d1ca717f4cad6e6619e459ed59"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/bundle.json","state_url":"https://pith.science/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/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":"2026-08-05T20:48:51Z","links":{"resolver":"https://pith.science/pith/3RLEY2EMFVDRSDGOF5P57G6IMU","bundle":"https://pith.science/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/bundle.json","state":"https://pith.science/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3RLEY2EMFVDRSDGOF5P57G6IMU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3RLEY2EMFVDRSDGOF5P57G6IMU","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":"26c1abe91bb3a5fb9ec4749788867c19ed5182c8a998b9015f409f1fdfd370bb","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T02:56:44Z","title_canon_sha256":"dbbc8946330c920a8394803d1198318a97347868cf3fba9f16b21af15e698b97"},"schema_version":"1.0","source":{"id":"2310.04978","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04978","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04978v1","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04978","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_12","alias_value":"3RLEY2EMFVDR","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_16","alias_value":"3RLEY2EMFVDRSDGO","created_at":"2026-07-05T06:58:25Z"},{"alias_kind":"pith_short_8","alias_value":"3RLEY2EM","created_at":"2026-07-05T06:58:25Z"}],"graph_snapshots":[{"event_id":"sha256:ab606f2697dbeeef76333f4556cfb3b1a43133d1ca717f4cad6e6619e459ed59","target":"graph","created_at":"2026-07-05T06:58:25Z","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/2310.04978/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivity to user guidance, sensitivity to the amount and quality of data, and the inability to adapt learned topics from one corpus to another. To address these challenges, this paper proposes a neural topic model, TopicAdapt, that can adapt relevant topics from a related source corpus and also discover new topics in a target corpus that are absent in the","authors_text":"Kevin Chen-Chuan Chang, Pritom Saha Akash, Trisha Das","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T02:56:44Z","title":"TopicAdapt- An Inter-Corpora Topics Adaptation Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04978","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":"sha256:0292423b7efd7cfc77a98aca649b14ab3bdda2349943eb192e56f62b8872aa2a","target":"record","created_at":"2026-07-05T06:58:25Z","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":"26c1abe91bb3a5fb9ec4749788867c19ed5182c8a998b9015f409f1fdfd370bb","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T02:56:44Z","title_canon_sha256":"dbbc8946330c920a8394803d1198318a97347868cf3fba9f16b21af15e698b97"},"schema_version":"1.0","source":{"id":"2310.04978","kind":"arxiv","version":1}},"canonical_sha256":"dc564c688c2d47190cce2f5fdf9bc8651cdf989b27204e845fc16cd73708c933","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc564c688c2d47190cce2f5fdf9bc8651cdf989b27204e845fc16cd73708c933","first_computed_at":"2026-07-05T06:58:25.177536Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:58:25.177536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bhQ4QQqF/yuxcRXDjEVGEOrZvnrgNKKh5N4z5eHRV9CyemuUnHxXVeNlHB+hKxksv9JrelZhzjxpincKViteAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:58:25.177953Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.04978","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0292423b7efd7cfc77a98aca649b14ab3bdda2349943eb192e56f62b8872aa2a","sha256:ab606f2697dbeeef76333f4556cfb3b1a43133d1ca717f4cad6e6619e459ed59"],"state_sha256":"72ffaecebb929330c12c18392aee773be3d5d7a097d7c9f5e89abce1473d1026"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tLPVHQXwwtS57beYuqr4OVrlNdK5bZTbzeQfJoBCW6kmhtP+GjFhoEQKrnDW5HbIFrKlB0yDvlFLR3B4nzK0Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T20:48:51.766165Z","bundle_sha256":"63d5b69f2dbc26795bec2ba2cb03d0de14973f80dfcabb8d3a91d8b92ae233f8"}}