{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:ZVD6OE6PA7OGK6NAC4OYEV7DJM","short_pith_number":"pith:ZVD6OE6P","canonical_record":{"source":{"id":"2405.15132","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-24T01:08:05Z","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"title_canon_sha256":"5a10f56baeb2e27b5f49cf64c7d6af4394169d58b847691d3062b2d3025e1e1d","abstract_canon_sha256":"66747d267353687ec6b26be94e49c4a085dc3b8469426ce74f6948ae620ee49a"},"schema_version":"1.0"},"canonical_sha256":"cd47e713cf07dc6579a0171d8257e34b3b28e519e39f9f6a03814370a651bd5d","source":{"kind":"arxiv","id":"2405.15132","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.15132","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"arxiv_version","alias_value":"2405.15132v6","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15132","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_12","alias_value":"ZVD6OE6PA7OG","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_16","alias_value":"ZVD6OE6PA7OGK6NA","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_8","alias_value":"ZVD6OE6P","created_at":"2026-07-29T01:25:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:ZVD6OE6PA7OGK6NAC4OYEV7DJM","target":"record","payload":{"canonical_record":{"source":{"id":"2405.15132","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-24T01:08:05Z","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"title_canon_sha256":"5a10f56baeb2e27b5f49cf64c7d6af4394169d58b847691d3062b2d3025e1e1d","abstract_canon_sha256":"66747d267353687ec6b26be94e49c4a085dc3b8469426ce74f6948ae620ee49a"},"schema_version":"1.0"},"canonical_sha256":"cd47e713cf07dc6579a0171d8257e34b3b28e519e39f9f6a03814370a651bd5d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:24.658212Z","signature_b64":"kAAWwFS/454sU3T6aDHpbZGdKjKZpXMakGeTGV8IYlPHC+5+JHfffabR5zS1z4VP0yJlXRcpZlR7pkukEoO2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd47e713cf07dc6579a0171d8257e34b3b28e519e39f9f6a03814370a651bd5d","last_reissued_at":"2026-07-29T01:25:24.657226Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:24.657226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.15132","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T01:25:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RHQBW6B2Ly1889BiLC7pJymTXxfFIviTTjyjAI08dA04BxOGGjdnfh0LG34Cclt+2lCn3Ii2by0OKA2BYsPnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T19:43:02.305627Z"},"content_sha256":"f49523b2431c66b1fbd5e3a137a08954780d762bb266a35c29b7c40fcc8ca1db","schema_version":"1.0","event_id":"sha256:f49523b2431c66b1fbd5e3a137a08954780d762bb266a35c29b7c40fcc8ca1db"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:ZVD6OE6PA7OGK6NAC4OYEV7DJM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"primary_cat":"stat.ML","authors_text":"Aldo Glielmo, Alessandro Laio, Antonietta Mira, Antonio Di Noia, Iuri Macocco","submitted_at":"2024-05-24T01:08:05Z","abstract_excerpt":"The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typically at a small scale, the ID is very large, as the data are affected by measurement errors. At large scale, the ID can also appear erroneously large, due to the curvature and the topology of the manifold containing the data. In this work, we introduce an automatic protocol to select the sweet spot, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15132","kind":"arxiv","version":6},"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/2405.15132/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":"2026-07-29T01:25:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OJeGdHCwR2LXUNhxWiMdRgG4A6ehLY0T8dotiZ5HSRy62Ny4d1hY0Ab+qPphLA0Cv/tI/EabCidKCmuU0dNrBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T19:43:02.306243Z"},"content_sha256":"d58cf5e69ac5b077c5b264734457e7abe32d7bb5db66a1dc3cdc00a91e24bf42","schema_version":"1.0","event_id":"sha256:d58cf5e69ac5b077c5b264734457e7abe32d7bb5db66a1dc3cdc00a91e24bf42"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/bundle.json","state_url":"https://pith.science/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/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":"2026-08-09T19:43:02Z","links":{"resolver":"https://pith.science/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM","bundle":"https://pith.science/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/bundle.json","state":"https://pith.science/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZVD6OE6PA7OGK6NAC4OYEV7DJM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZVD6OE6PA7OGK6NAC4OYEV7DJM","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":"66747d267353687ec6b26be94e49c4a085dc3b8469426ce74f6948ae620ee49a","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-24T01:08:05Z","title_canon_sha256":"5a10f56baeb2e27b5f49cf64c7d6af4394169d58b847691d3062b2d3025e1e1d"},"schema_version":"1.0","source":{"id":"2405.15132","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.15132","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"arxiv_version","alias_value":"2405.15132v6","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15132","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_12","alias_value":"ZVD6OE6PA7OG","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_16","alias_value":"ZVD6OE6PA7OGK6NA","created_at":"2026-07-29T01:25:24Z"},{"alias_kind":"pith_short_8","alias_value":"ZVD6OE6P","created_at":"2026-07-29T01:25:24Z"}],"graph_snapshots":[{"event_id":"sha256:d58cf5e69ac5b077c5b264734457e7abe32d7bb5db66a1dc3cdc00a91e24bf42","target":"graph","created_at":"2026-07-29T01:25:24Z","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/2405.15132/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typically at a small scale, the ID is very large, as the data are affected by measurement errors. At large scale, the ID can also appear erroneously large, due to the curvature and the topology of the manifold containing the data. In this work, we introduce an automatic protocol to select the sweet spot, ","authors_text":"Aldo Glielmo, Alessandro Laio, Antonietta Mira, Antonio Di Noia, Iuri Macocco","cross_cats":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-24T01:08:05Z","title":"Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15132","kind":"arxiv","version":6},"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:f49523b2431c66b1fbd5e3a137a08954780d762bb266a35c29b7c40fcc8ca1db","target":"record","created_at":"2026-07-29T01:25:24Z","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":"66747d267353687ec6b26be94e49c4a085dc3b8469426ce74f6948ae620ee49a","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-24T01:08:05Z","title_canon_sha256":"5a10f56baeb2e27b5f49cf64c7d6af4394169d58b847691d3062b2d3025e1e1d"},"schema_version":"1.0","source":{"id":"2405.15132","kind":"arxiv","version":6}},"canonical_sha256":"cd47e713cf07dc6579a0171d8257e34b3b28e519e39f9f6a03814370a651bd5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd47e713cf07dc6579a0171d8257e34b3b28e519e39f9f6a03814370a651bd5d","first_computed_at":"2026-07-29T01:25:24.657226Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-29T01:25:24.657226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kAAWwFS/454sU3T6aDHpbZGdKjKZpXMakGeTGV8IYlPHC+5+JHfffabR5zS1z4VP0yJlXRcpZlR7pkukEoO2DQ==","signature_status":"signed_v1","signed_at":"2026-07-29T01:25:24.658212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.15132","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f49523b2431c66b1fbd5e3a137a08954780d762bb266a35c29b7c40fcc8ca1db","sha256:d58cf5e69ac5b077c5b264734457e7abe32d7bb5db66a1dc3cdc00a91e24bf42"],"state_sha256":"232dc80e509ab859976ab47f2abb71ebdf67cb149aaad05c517dfb96c658a358"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QXNQgcF21KyW9Jv+TTtfciHBVaD+0YWjpicFgt/oMVSdptsRMgDWizAtVDfmPG036sM8XJIr1L94md2rDW2gDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T19:43:02.312648Z","bundle_sha256":"d837d31d98918cefccfea8de2f6b4722ad2c19b08d95f7937a0c2f121c8d1064"}}