{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FT6Y2SSRMQNCEYWVNJU5CPG3L4","short_pith_number":"pith:FT6Y2SSR","canonical_record":{"source":{"id":"1805.06115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T03:27:02Z","cross_cats_sorted":[],"title_canon_sha256":"cfb8827e9f48d3f93b116a519cbb17fc054eb13257a179d23b44fec28f58a225","abstract_canon_sha256":"d1af2421a0ae94ee662e15eb69bb3ea7c4a124daf5a556d618d852a3cd2a47a0"},"schema_version":"1.0"},"canonical_sha256":"2cfd8d4a51641a2262d56a69d13cdb5f106cae6a80cba9eed3051a5efd7bc050","source":{"kind":"arxiv","id":"1805.06115","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06115","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06115v2","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06115","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"pith_short_12","alias_value":"FT6Y2SSRMQNC","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FT6Y2SSRMQNCEYWV","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FT6Y2SSR","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FT6Y2SSRMQNCEYWVNJU5CPG3L4","target":"record","payload":{"canonical_record":{"source":{"id":"1805.06115","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T03:27:02Z","cross_cats_sorted":[],"title_canon_sha256":"cfb8827e9f48d3f93b116a519cbb17fc054eb13257a179d23b44fec28f58a225","abstract_canon_sha256":"d1af2421a0ae94ee662e15eb69bb3ea7c4a124daf5a556d618d852a3cd2a47a0"},"schema_version":"1.0"},"canonical_sha256":"2cfd8d4a51641a2262d56a69d13cdb5f106cae6a80cba9eed3051a5efd7bc050","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:19.268109Z","signature_b64":"o8x4MVNuiEugzb6P40j1S3FJq8dVw5j9o3+iklR9//+kH1PPSbzM41KWzpVqS+OiWTnu/ZqevATa3sqSROWTBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cfd8d4a51641a2262d56a69d13cdb5f106cae6a80cba9eed3051a5efd7bc050","last_reissued_at":"2026-05-18T00:05:19.267564Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:19.267564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.06115","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":"2026-05-18T00:05:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PJGctljCvnzHC+3l0tJxTqYTuh/gyobRXPcfjk8owJbrtZUDPW+x6PfzWaw2LCbr9nyd/8I7Ip5lbXvBcTMDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T17:14:40.730196Z"},"content_sha256":"7001f6b08fc41e5494f616780c2b0826ef06b613611aed8380f41e4243c6650a","schema_version":"1.0","event_id":"sha256:7001f6b08fc41e5494f616780c2b0826ef06b613611aed8380f41e4243c6650a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FT6Y2SSRMQNCEYWVNJU5CPG3L4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antoni Chan, Di Kang","submitted_at":"2018-05-16T03:27:02Z","abstract_excerpt":"Because of the powerful learning capability of deep neural networks, counting performance via density map estimation has improved significantly during the past several years. However, it is still very challenging due to severe occlusion, large scale variations, and perspective distortion. Scale variations (from image to image) coupled with perspective distortion (within one image) result in huge scale changes of the object size. Earlier methods based on convolutional neural networks (CNN) typically did not handle this scale variation explicitly, until Hydra-CNN and MCNN. MCNN uses three column"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06115","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":""},"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":"2026-05-18T00:05:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xwPehEjNkps14m1GtpKmmiOrhqxkfxmC7OjUkDXOWfMbLpRHqiWhv4H2ohscTI34dUDVPnjeDYGhe/BlNpicDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T17:14:40.730912Z"},"content_sha256":"d78ee29c54b40fd9be29f0e49bc62df68e3766c8be6f8f8027ede98085193b87","schema_version":"1.0","event_id":"sha256:d78ee29c54b40fd9be29f0e49bc62df68e3766c8be6f8f8027ede98085193b87"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/bundle.json","state_url":"https://pith.science/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/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":"2026-06-06T17:14:40Z","links":{"resolver":"https://pith.science/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4","bundle":"https://pith.science/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/bundle.json","state":"https://pith.science/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FT6Y2SSRMQNCEYWVNJU5CPG3L4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FT6Y2SSRMQNCEYWVNJU5CPG3L4","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":"d1af2421a0ae94ee662e15eb69bb3ea7c4a124daf5a556d618d852a3cd2a47a0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T03:27:02Z","title_canon_sha256":"cfb8827e9f48d3f93b116a519cbb17fc054eb13257a179d23b44fec28f58a225"},"schema_version":"1.0","source":{"id":"1805.06115","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06115","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06115v2","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06115","created_at":"2026-05-18T00:05:19Z"},{"alias_kind":"pith_short_12","alias_value":"FT6Y2SSRMQNC","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FT6Y2SSRMQNCEYWV","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FT6Y2SSR","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:d78ee29c54b40fd9be29f0e49bc62df68e3766c8be6f8f8027ede98085193b87","target":"graph","created_at":"2026-05-18T00:05:19Z","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"},"paper":{"abstract_excerpt":"Because of the powerful learning capability of deep neural networks, counting performance via density map estimation has improved significantly during the past several years. However, it is still very challenging due to severe occlusion, large scale variations, and perspective distortion. Scale variations (from image to image) coupled with perspective distortion (within one image) result in huge scale changes of the object size. Earlier methods based on convolutional neural networks (CNN) typically did not handle this scale variation explicitly, until Hydra-CNN and MCNN. MCNN uses three column","authors_text":"Antoni Chan, Di Kang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T03:27:02Z","title":"Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06115","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":"sha256:7001f6b08fc41e5494f616780c2b0826ef06b613611aed8380f41e4243c6650a","target":"record","created_at":"2026-05-18T00:05:19Z","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":"d1af2421a0ae94ee662e15eb69bb3ea7c4a124daf5a556d618d852a3cd2a47a0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-16T03:27:02Z","title_canon_sha256":"cfb8827e9f48d3f93b116a519cbb17fc054eb13257a179d23b44fec28f58a225"},"schema_version":"1.0","source":{"id":"1805.06115","kind":"arxiv","version":2}},"canonical_sha256":"2cfd8d4a51641a2262d56a69d13cdb5f106cae6a80cba9eed3051a5efd7bc050","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2cfd8d4a51641a2262d56a69d13cdb5f106cae6a80cba9eed3051a5efd7bc050","first_computed_at":"2026-05-18T00:05:19.267564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:19.267564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o8x4MVNuiEugzb6P40j1S3FJq8dVw5j9o3+iklR9//+kH1PPSbzM41KWzpVqS+OiWTnu/ZqevATa3sqSROWTBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:19.268109Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.06115","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7001f6b08fc41e5494f616780c2b0826ef06b613611aed8380f41e4243c6650a","sha256:d78ee29c54b40fd9be29f0e49bc62df68e3766c8be6f8f8027ede98085193b87"],"state_sha256":"d2eb7ec2197b9e65c472169cf480b4d2d025e323f1c4ad744664ff09298d0201"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iNdC53QD3Op7pGRDfqL3qsVeJrIkyIMEeCYVYgc0VXXhUvyszDF6TQHosekpDgUfuJG8LY2UYQaWaCeAYtesAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T17:14:40.735477Z","bundle_sha256":"1183474e770b73f29c1b7a3aacf8207d153a1d7effc27339b44c82b1849da7de"}}