{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:RTVDC3MWTYRMO23KNRTRWBDZ3X","short_pith_number":"pith:RTVDC3MW","schema_version":"1.0","canonical_sha256":"8cea316d969e22c76b6a6c671b0479ddf6e6c1f961c36a4c57c4ed142e21f193","source":{"kind":"arxiv","id":"1704.02899","version":1},"attestation_state":"computed","paper":{"title":"Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit Singer, Roy R. Lederman","submitted_at":"2017-04-10T15:15:25Z","abstract_excerpt":"Single particle cryo-electron microscopy (EM) is an increasingly popular method for determining the 3-D structure of macromolecules from noisy 2-D images of single macromolecules whose orientations and positions are random and unknown. One of the great opportunities in cryo-EM is to recover the structure of macromolecules in heterogeneous samples, where multiple types or multiple conformations are mixed together. Indeed, in recent years, many tools have been introduced for the analysis of multiple discrete classes of molecules mixed together in a cryo-EM experiment. However, many interesting s"},"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":"1704.02899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-10T15:15:25Z","cross_cats_sorted":[],"title_canon_sha256":"a9d398e82f3703150c728da7f72eeab316594a52ce5789988c0a4c8bd3a3f821","abstract_canon_sha256":"37d75e747d3e134511c8a444d55e156e4327f1fb600389c4b72cdf0e10d77a00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:43.725465Z","signature_b64":"NaJeog0MyZtbc6aYVwQg7bYVnYoc7S1gk+hj+Y4Q6Q5b77ZVx7mIW7eBiALEoYpsx/uNDbPYLap0VuhOa4zmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cea316d969e22c76b6a6c671b0479ddf6e6c1f961c36a4c57c4ed142e21f193","last_reissued_at":"2026-05-18T00:46:43.724734Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:43.724734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit Singer, Roy R. Lederman","submitted_at":"2017-04-10T15:15:25Z","abstract_excerpt":"Single particle cryo-electron microscopy (EM) is an increasingly popular method for determining the 3-D structure of macromolecules from noisy 2-D images of single macromolecules whose orientations and positions are random and unknown. One of the great opportunities in cryo-EM is to recover the structure of macromolecules in heterogeneous samples, where multiple types or multiple conformations are mixed together. Indeed, in recent years, many tools have been introduced for the analysis of multiple discrete classes of molecules mixed together in a cryo-EM experiment. However, many interesting s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02899","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":""},"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":"1704.02899","created_at":"2026-05-18T00:46:43.724846+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.02899v1","created_at":"2026-05-18T00:46:43.724846+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02899","created_at":"2026-05-18T00:46:43.724846+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTVDC3MWTYRM","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTVDC3MWTYRMO23K","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTVDC3MW","created_at":"2026-05-18T12:31:39.905425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.10412","citing_title":"Eigenvalue distribution analysis of multidimensional prolate matrices","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X","json":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X.json","graph_json":"https://pith.science/api/pith-number/RTVDC3MWTYRMO23KNRTRWBDZ3X/graph.json","events_json":"https://pith.science/api/pith-number/RTVDC3MWTYRMO23KNRTRWBDZ3X/events.json","paper":"https://pith.science/paper/RTVDC3MW"},"agent_actions":{"view_html":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X","download_json":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X.json","view_paper":"https://pith.science/paper/RTVDC3MW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.02899&json=true","fetch_graph":"https://pith.science/api/pith-number/RTVDC3MWTYRMO23KNRTRWBDZ3X/graph.json","fetch_events":"https://pith.science/api/pith-number/RTVDC3MWTYRMO23KNRTRWBDZ3X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X/action/storage_attestation","attest_author":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X/action/author_attestation","sign_citation":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X/action/citation_signature","submit_replication":"https://pith.science/pith/RTVDC3MWTYRMO23KNRTRWBDZ3X/action/replication_record"}},"created_at":"2026-05-18T00:46:43.724846+00:00","updated_at":"2026-05-18T00:46:43.724846+00:00"}