{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6NNX5Q5AN3G3RR54QBLVNZTG2F","short_pith_number":"pith:6NNX5Q5A","schema_version":"1.0","canonical_sha256":"f35b7ec3a06ecdb8c7bc805756e666d140428f12b3eb2856fa98a729da5862e3","source":{"kind":"arxiv","id":"2301.03495","version":1},"attestation_state":"computed","paper":{"title":"On the challenges to learn from Natural Data Streams","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Davide Maltoni, Gabriele Graffieti, Guido Borghi","submitted_at":"2023-01-09T16:32:02Z","abstract_excerpt":"In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e. data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges. Moreover, a clear separation between the traditional training and deployment phases is usually lacking. This data organization and fruition represents an interesting and challenging scenario for both traditional Machine and Deep Learning algorithms and incremental learning agents, i.e. agents that have the ability to incrementally improve their knowl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2301.03495","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-09T16:32:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7b874f70e6c9210af06e3a0a598bff0f1b7910ae6633ee71c4d089efef35c656","abstract_canon_sha256":"9766a3785d443de516a39df02d29ad336c48c79e42da943200b341ac406703b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:36.407396Z","signature_b64":"QrzBOTNSYuJHWd1bsVF3XJoXfaWa6DLLD5FBySzyYK6Ze92rtjdbCNiURtjch+zviOb1n5puwbEgMFWxTmbIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f35b7ec3a06ecdb8c7bc805756e666d140428f12b3eb2856fa98a729da5862e3","last_reissued_at":"2026-07-05T05:31:36.407017Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:36.407017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the challenges to learn from Natural Data Streams","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Davide Maltoni, Gabriele Graffieti, Guido Borghi","submitted_at":"2023-01-09T16:32:02Z","abstract_excerpt":"In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e. data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges. Moreover, a clear separation between the traditional training and deployment phases is usually lacking. This data organization and fruition represents an interesting and challenging scenario for both traditional Machine and Deep Learning algorithms and incremental learning agents, i.e. agents that have the ability to incrementally improve their knowl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03495","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/2301.03495/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2301.03495","created_at":"2026-07-05T05:31:36.407074+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.03495v1","created_at":"2026-07-05T05:31:36.407074+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03495","created_at":"2026-07-05T05:31:36.407074+00:00"},{"alias_kind":"pith_short_12","alias_value":"6NNX5Q5AN3G3","created_at":"2026-07-05T05:31:36.407074+00:00"},{"alias_kind":"pith_short_16","alias_value":"6NNX5Q5AN3G3RR54","created_at":"2026-07-05T05:31:36.407074+00:00"},{"alias_kind":"pith_short_8","alias_value":"6NNX5Q5A","created_at":"2026-07-05T05:31:36.407074+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F","json":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F.json","graph_json":"https://pith.science/api/pith-number/6NNX5Q5AN3G3RR54QBLVNZTG2F/graph.json","events_json":"https://pith.science/api/pith-number/6NNX5Q5AN3G3RR54QBLVNZTG2F/events.json","paper":"https://pith.science/paper/6NNX5Q5A"},"agent_actions":{"view_html":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F","download_json":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F.json","view_paper":"https://pith.science/paper/6NNX5Q5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.03495&json=true","fetch_graph":"https://pith.science/api/pith-number/6NNX5Q5AN3G3RR54QBLVNZTG2F/graph.json","fetch_events":"https://pith.science/api/pith-number/6NNX5Q5AN3G3RR54QBLVNZTG2F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F/action/storage_attestation","attest_author":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F/action/author_attestation","sign_citation":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F/action/citation_signature","submit_replication":"https://pith.science/pith/6NNX5Q5AN3G3RR54QBLVNZTG2F/action/replication_record"}},"created_at":"2026-07-05T05:31:36.407074+00:00","updated_at":"2026-07-05T05:31:36.407074+00:00"}