{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:C7QZY4CTM27CYAYSQ6HCWZ7GT3","short_pith_number":"pith:C7QZY4CT","canonical_record":{"source":{"id":"2304.14541","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-04-27T21:45:21Z","cross_cats_sorted":[],"title_canon_sha256":"2e618378042448890ac1d47e7decf69dc07a42ef038d81d946ce92ae243f057f","abstract_canon_sha256":"01960655106ecc9fa5e1c8d80d9b3bc8fb67c619f7b2dd5fb6d01f557bf7f5b2"},"schema_version":"1.0"},"canonical_sha256":"17e19c705366be2c0312878e2b67e69ee394c0daa6f8888fde5834ab11c75868","source":{"kind":"arxiv","id":"2304.14541","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.14541","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"arxiv_version","alias_value":"2304.14541v2","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14541","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_12","alias_value":"C7QZY4CTM27C","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_16","alias_value":"C7QZY4CTM27CYAYS","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_8","alias_value":"C7QZY4CT","created_at":"2026-07-05T06:50:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:C7QZY4CTM27CYAYSQ6HCWZ7GT3","target":"record","payload":{"canonical_record":{"source":{"id":"2304.14541","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-04-27T21:45:21Z","cross_cats_sorted":[],"title_canon_sha256":"2e618378042448890ac1d47e7decf69dc07a42ef038d81d946ce92ae243f057f","abstract_canon_sha256":"01960655106ecc9fa5e1c8d80d9b3bc8fb67c619f7b2dd5fb6d01f557bf7f5b2"},"schema_version":"1.0"},"canonical_sha256":"17e19c705366be2c0312878e2b67e69ee394c0daa6f8888fde5834ab11c75868","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:28.945646Z","signature_b64":"EpXK+EC349o6mzEvnzkWzmNMrIAVmcWiPqQFa2jPUbfk/OMtwqgkNiss/maT1DoZuJgP6qLCqKMzhf5Kg3tRDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17e19c705366be2c0312878e2b67e69ee394c0daa6f8888fde5834ab11c75868","last_reissued_at":"2026-07-05T06:50:28.945295Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:28.945295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.14541","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:50:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HCiw2pIPnD3K4Z6GpOtslkgescMi1WjysUjHCiI/alOIhIkhDs7fWAtfliNNfZ3+NrrX3ev/FU52cMRMyGZQCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:27:39.208002Z"},"content_sha256":"60ec61f850ce142dae2278be53b9812e4ea154e3bb369fe4c0a24c008df1a6a5","schema_version":"1.0","event_id":"sha256:60ec61f850ce142dae2278be53b9812e4ea154e3bb369fe4c0a24c008df1a6a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:C7QZY4CTM27CYAYSQ6HCWZ7GT3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Francis Ndikum Nji, Jianwu Wang, Mostafa Cham, Omar Faruque, Rohan Mandar Salvi, Xue Zheng","submitted_at":"2023-04-27T21:45:21Z","abstract_excerpt":"Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method. Inspired by the U-net ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14541","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.14541/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":"2026-07-05T06:50:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"huKLW+zLzPMhezs/FeP6fmgojqrElOaZ+FX7eG0SzN2JO2Oytcc7y2QAQLAI8nLV3WoYmicpIktbMy1E+AxhDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T07:27:39.208543Z"},"content_sha256":"0f6d35f515577bc67e486db58cbc16374522596c434de9a14f5de13f6166bd44","schema_version":"1.0","event_id":"sha256:0f6d35f515577bc67e486db58cbc16374522596c434de9a14f5de13f6166bd44"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/bundle.json","state_url":"https://pith.science/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/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":"2026-08-10T07:27:39Z","links":{"resolver":"https://pith.science/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3","bundle":"https://pith.science/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/bundle.json","state":"https://pith.science/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C7QZY4CTM27CYAYSQ6HCWZ7GT3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:C7QZY4CTM27CYAYSQ6HCWZ7GT3","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":"01960655106ecc9fa5e1c8d80d9b3bc8fb67c619f7b2dd5fb6d01f557bf7f5b2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-04-27T21:45:21Z","title_canon_sha256":"2e618378042448890ac1d47e7decf69dc07a42ef038d81d946ce92ae243f057f"},"schema_version":"1.0","source":{"id":"2304.14541","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.14541","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"arxiv_version","alias_value":"2304.14541v2","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14541","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_12","alias_value":"C7QZY4CTM27C","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_16","alias_value":"C7QZY4CTM27CYAYS","created_at":"2026-07-05T06:50:28Z"},{"alias_kind":"pith_short_8","alias_value":"C7QZY4CT","created_at":"2026-07-05T06:50:28Z"}],"graph_snapshots":[{"event_id":"sha256:0f6d35f515577bc67e486db58cbc16374522596c434de9a14f5de13f6166bd44","target":"graph","created_at":"2026-07-05T06:50:28Z","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/2304.14541/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method. Inspired by the U-net ar","authors_text":"Francis Ndikum Nji, Jianwu Wang, Mostafa Cham, Omar Faruque, Rohan Mandar Salvi, Xue Zheng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-04-27T21:45:21Z","title":"Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14541","kind":"arxiv","version":2},"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":"sha256:60ec61f850ce142dae2278be53b9812e4ea154e3bb369fe4c0a24c008df1a6a5","target":"record","created_at":"2026-07-05T06:50:28Z","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":"01960655106ecc9fa5e1c8d80d9b3bc8fb67c619f7b2dd5fb6d01f557bf7f5b2","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-04-27T21:45:21Z","title_canon_sha256":"2e618378042448890ac1d47e7decf69dc07a42ef038d81d946ce92ae243f057f"},"schema_version":"1.0","source":{"id":"2304.14541","kind":"arxiv","version":2}},"canonical_sha256":"17e19c705366be2c0312878e2b67e69ee394c0daa6f8888fde5834ab11c75868","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17e19c705366be2c0312878e2b67e69ee394c0daa6f8888fde5834ab11c75868","first_computed_at":"2026-07-05T06:50:28.945295Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:28.945295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EpXK+EC349o6mzEvnzkWzmNMrIAVmcWiPqQFa2jPUbfk/OMtwqgkNiss/maT1DoZuJgP6qLCqKMzhf5Kg3tRDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:28.945646Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.14541","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60ec61f850ce142dae2278be53b9812e4ea154e3bb369fe4c0a24c008df1a6a5","sha256:0f6d35f515577bc67e486db58cbc16374522596c434de9a14f5de13f6166bd44"],"state_sha256":"c95b2bf8f2d7f8ea3f7b46a79871684b0a8e91720aa22abfa8b0d95835b64526"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WgZlbj0vtDFx51q+FMyd5aQBxgK5zZcpWvJjs7TGowxFPv0mjXNDzFX98f+9qYP+MtWiBMtes5navGeYTYXJBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T07:27:39.212289Z","bundle_sha256":"df89d869c8894d645b3060e6d3359b1e2d904b825cf2df121514e0610ea37492"}}