{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:NGIPY7VQW3PLWTGRJHDARKSXRD","short_pith_number":"pith:NGIPY7VQ","canonical_record":{"source":{"id":"2001.01917","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-07T07:28:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b3bbcd0264ebb6f397912a9e0ddec21cb80261645df945945918e229258916ae","abstract_canon_sha256":"f461459990a5ffb6710c1517b272d4494e861cfac827a825afe1e843904e3c06"},"schema_version":"1.0"},"canonical_sha256":"6990fc7eb0b6debb4cd149c608aa5788df0c8c9f0e02e73e8bc1ac3569d0459b","source":{"kind":"arxiv","id":"2001.01917","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.01917","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"arxiv_version","alias_value":"2001.01917v1","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01917","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_12","alias_value":"NGIPY7VQW3PL","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_16","alias_value":"NGIPY7VQW3PLWTGR","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_8","alias_value":"NGIPY7VQ","created_at":"2026-07-05T00:30:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:NGIPY7VQW3PLWTGRJHDARKSXRD","target":"record","payload":{"canonical_record":{"source":{"id":"2001.01917","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-07T07:28:21Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b3bbcd0264ebb6f397912a9e0ddec21cb80261645df945945918e229258916ae","abstract_canon_sha256":"f461459990a5ffb6710c1517b272d4494e861cfac827a825afe1e843904e3c06"},"schema_version":"1.0"},"canonical_sha256":"6990fc7eb0b6debb4cd149c608aa5788df0c8c9f0e02e73e8bc1ac3569d0459b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:30:42.237182Z","signature_b64":"UuzdxFcHyoBFW3SYvaQADxCWhGDiKUouxjKBXXvyBLslrSXo/rafUD7rmrSTiMrqV2EVwz1QE9VXAi/2CBoaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6990fc7eb0b6debb4cd149c608aa5788df0c8c9f0e02e73e8bc1ac3569d0459b","last_reissued_at":"2026-07-05T00:30:42.236842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:30:42.236842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.01917","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":"2026-07-05T00:30:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/hc68E4Gn1H7SR0yPK5wtNrtKQc06Jud/KUfOOFEn3r98a+/A6UWO6o2zmFrqU9OxLGSY34iSoPquSkuYC8zCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:54:57.120704Z"},"content_sha256":"2621133cc2266ccf1009d9cd0664198883e69490fe1fe2d9dacba74ffa401c91","schema_version":"1.0","event_id":"sha256:2621133cc2266ccf1009d9cd0664198883e69490fe1fe2d9dacba74ffa401c91"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:NGIPY7VQW3PLWTGRJHDARKSXRD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinkyoo Park, Yohan Jung","submitted_at":"2020-01-07T07:28:21Z","abstract_excerpt":"Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission can be effectively used to estimate the hidden state with a sequence of complex input-output relational observations. Especially when the spectral mixture (SM) kernel is used for GP emission, we call this model as a hybrid HMM-GPSM. This model can effectively model the sequence of time-series data. However, because of a large number of parameters for the SM kernel, this model can not effectively be trained with a large volume of data having (1) long sequence for state transition and 2) a large number of time-series dataset "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01917","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/2001.01917/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":"2026-07-05T00:30:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s/XO2V9KyeQ4Ezdou0sSon9vdCZkUBYEaZ7VmElvkcFdmljax+YWRr9VGJM5YR0FjmGUAviqms49SrbtSHA/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T13:54:57.121428Z"},"content_sha256":"3399181da9bcae5a821dc79a79953d286c35110840fce7d81407e6a5f0a79f63","schema_version":"1.0","event_id":"sha256:3399181da9bcae5a821dc79a79953d286c35110840fce7d81407e6a5f0a79f63"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/bundle.json","state_url":"https://pith.science/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/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":"2026-08-09T13:54:57Z","links":{"resolver":"https://pith.science/pith/NGIPY7VQW3PLWTGRJHDARKSXRD","bundle":"https://pith.science/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/bundle.json","state":"https://pith.science/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NGIPY7VQW3PLWTGRJHDARKSXRD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NGIPY7VQW3PLWTGRJHDARKSXRD","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":"f461459990a5ffb6710c1517b272d4494e861cfac827a825afe1e843904e3c06","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-07T07:28:21Z","title_canon_sha256":"b3bbcd0264ebb6f397912a9e0ddec21cb80261645df945945918e229258916ae"},"schema_version":"1.0","source":{"id":"2001.01917","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.01917","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"arxiv_version","alias_value":"2001.01917v1","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.01917","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_12","alias_value":"NGIPY7VQW3PL","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_16","alias_value":"NGIPY7VQW3PLWTGR","created_at":"2026-07-05T00:30:42Z"},{"alias_kind":"pith_short_8","alias_value":"NGIPY7VQ","created_at":"2026-07-05T00:30:42Z"}],"graph_snapshots":[{"event_id":"sha256:3399181da9bcae5a821dc79a79953d286c35110840fce7d81407e6a5f0a79f63","target":"graph","created_at":"2026-07-05T00:30:42Z","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/2001.01917/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission can be effectively used to estimate the hidden state with a sequence of complex input-output relational observations. Especially when the spectral mixture (SM) kernel is used for GP emission, we call this model as a hybrid HMM-GPSM. This model can effectively model the sequence of time-series data. However, because of a large number of parameters for the SM kernel, this model can not effectively be trained with a large volume of data having (1) long sequence for state transition and 2) a large number of time-series dataset ","authors_text":"Jinkyoo Park, Yohan Jung","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-07T07:28:21Z","title":"Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.01917","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":"sha256:2621133cc2266ccf1009d9cd0664198883e69490fe1fe2d9dacba74ffa401c91","target":"record","created_at":"2026-07-05T00:30:42Z","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":"f461459990a5ffb6710c1517b272d4494e861cfac827a825afe1e843904e3c06","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-01-07T07:28:21Z","title_canon_sha256":"b3bbcd0264ebb6f397912a9e0ddec21cb80261645df945945918e229258916ae"},"schema_version":"1.0","source":{"id":"2001.01917","kind":"arxiv","version":1}},"canonical_sha256":"6990fc7eb0b6debb4cd149c608aa5788df0c8c9f0e02e73e8bc1ac3569d0459b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6990fc7eb0b6debb4cd149c608aa5788df0c8c9f0e02e73e8bc1ac3569d0459b","first_computed_at":"2026-07-05T00:30:42.236842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:30:42.236842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UuzdxFcHyoBFW3SYvaQADxCWhGDiKUouxjKBXXvyBLslrSXo/rafUD7rmrSTiMrqV2EVwz1QE9VXAi/2CBoaDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:30:42.237182Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.01917","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2621133cc2266ccf1009d9cd0664198883e69490fe1fe2d9dacba74ffa401c91","sha256:3399181da9bcae5a821dc79a79953d286c35110840fce7d81407e6a5f0a79f63"],"state_sha256":"04bcbd9775be23dd7123d3a2b086657b28b713af1ca0280560a321ab3e803578"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZT0abb81Yt1eWBfW9Ee/fLvdLGVkb9FYVxMaXydsHhC+jhZdxhz5evswRE7UvwMHsuB9h6KnAxukEfcxESSfBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T13:54:57.159393Z","bundle_sha256":"181e69e6fbfd240733c6e370e4b8adef4036e6ee6cd372cfbdaae8b3420ee748"}}