{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QTFJHRSSHQIFCGLS4ZW23JAIGM","short_pith_number":"pith:QTFJHRSS","canonical_record":{"source":{"id":"1905.13307","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-22T20:03:13Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2aa281ab8113698fb39e26446d671a0407228d3159b134133711c6339a375121","abstract_canon_sha256":"8c8ad4d53a859375b26b9c89dc48fc1b78c68b62ae6e54aa3f8100273eeebba5"},"schema_version":"1.0"},"canonical_sha256":"84ca93c6523c10511972e66dada408330bf92e84ebb95a81973fd3ce6325f524","source":{"kind":"arxiv","id":"1905.13307","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.13307","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"arxiv_version","alias_value":"1905.13307v1","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.13307","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"pith_short_12","alias_value":"QTFJHRSSHQIF","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QTFJHRSSHQIFCGLS","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QTFJHRSS","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QTFJHRSSHQIFCGLS4ZW23JAIGM","target":"record","payload":{"canonical_record":{"source":{"id":"1905.13307","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-22T20:03:13Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2aa281ab8113698fb39e26446d671a0407228d3159b134133711c6339a375121","abstract_canon_sha256":"8c8ad4d53a859375b26b9c89dc48fc1b78c68b62ae6e54aa3f8100273eeebba5"},"schema_version":"1.0"},"canonical_sha256":"84ca93c6523c10511972e66dada408330bf92e84ebb95a81973fd3ce6325f524","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:35.356831Z","signature_b64":"5uoktNfWtMC/vGDOiSIR98iizeYieej5Zl5L4y2D6kbYSXqg0m18BwnTqUHgpzpA+82lj5wHCVSRtqnj9aUZAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84ca93c6523c10511972e66dada408330bf92e84ebb95a81973fd3ce6325f524","last_reissued_at":"2026-05-17T23:44:35.356153Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:35.356153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.13307","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KUuBPhdjy8onUIMPwekib1nmOjnVAkl+sr5zqZIK1gf9DfuVrbKQac9jzKSKSybMGIKis6S3BM3UjfzMJsUACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T12:36:31.253357Z"},"content_sha256":"f4b01330e4c55a01347e3c19821a936638d1acd8acad79697384f269a9d8bf3a","schema_version":"1.0","event_id":"sha256:f4b01330e4c55a01347e3c19821a936638d1acd8acad79697384f269a9d8bf3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QTFJHRSSHQIFCGLS4ZW23JAIGM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Real-time Approximate Bayesian Computation for Scene Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"David G\\'omez-Guti\\'errez, Javier Felip, Nilesh Ahuja, Omesh Tickoo, Vikash Mansinghka","submitted_at":"2019-05-22T20:03:13Z","abstract_excerpt":"Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable street crossings of pedestrians at a busy intersection. This paper shows how to solve these problems using Approximate Bayesian Computation. The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data. The simulators are drawn from off-the-shelf computer graphics, video game, and traffic simulation code. The paper int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.13307","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iH02Lch/jg5ap+Mk1/0KIi0bnzg+49SpaIgtanW7WCJpBPHeWnPCIUp5SQelWEd//FyqCiUFX0CNuc1oxlhHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T12:36:31.253704Z"},"content_sha256":"2da82611a3f0022498c661349a19a831d3e64e52f55ade1c5f4839449a2ddbd8","schema_version":"1.0","event_id":"sha256:2da82611a3f0022498c661349a19a831d3e64e52f55ade1c5f4839449a2ddbd8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/bundle.json","state_url":"https://pith.science/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/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":"2026-05-28T12:36:31Z","links":{"resolver":"https://pith.science/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM","bundle":"https://pith.science/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/bundle.json","state":"https://pith.science/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QTFJHRSSHQIFCGLS4ZW23JAIGM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QTFJHRSSHQIFCGLS4ZW23JAIGM","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":"8c8ad4d53a859375b26b9c89dc48fc1b78c68b62ae6e54aa3f8100273eeebba5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-22T20:03:13Z","title_canon_sha256":"2aa281ab8113698fb39e26446d671a0407228d3159b134133711c6339a375121"},"schema_version":"1.0","source":{"id":"1905.13307","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.13307","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"arxiv_version","alias_value":"1905.13307v1","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.13307","created_at":"2026-05-17T23:44:35Z"},{"alias_kind":"pith_short_12","alias_value":"QTFJHRSSHQIF","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QTFJHRSSHQIFCGLS","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QTFJHRSS","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:2da82611a3f0022498c661349a19a831d3e64e52f55ade1c5f4839449a2ddbd8","target":"graph","created_at":"2026-05-17T23:44:35Z","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":"Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable street crossings of pedestrians at a busy intersection. This paper shows how to solve these problems using Approximate Bayesian Computation. The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data. The simulators are drawn from off-the-shelf computer graphics, video game, and traffic simulation code. The paper int","authors_text":"David G\\'omez-Guti\\'errez, Javier Felip, Nilesh Ahuja, Omesh Tickoo, Vikash Mansinghka","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-22T20:03:13Z","title":"Real-time Approximate Bayesian Computation for Scene Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.13307","kind":"arxiv","version":1},"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:f4b01330e4c55a01347e3c19821a936638d1acd8acad79697384f269a9d8bf3a","target":"record","created_at":"2026-05-17T23:44:35Z","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":"8c8ad4d53a859375b26b9c89dc48fc1b78c68b62ae6e54aa3f8100273eeebba5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-22T20:03:13Z","title_canon_sha256":"2aa281ab8113698fb39e26446d671a0407228d3159b134133711c6339a375121"},"schema_version":"1.0","source":{"id":"1905.13307","kind":"arxiv","version":1}},"canonical_sha256":"84ca93c6523c10511972e66dada408330bf92e84ebb95a81973fd3ce6325f524","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84ca93c6523c10511972e66dada408330bf92e84ebb95a81973fd3ce6325f524","first_computed_at":"2026-05-17T23:44:35.356153Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:35.356153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5uoktNfWtMC/vGDOiSIR98iizeYieej5Zl5L4y2D6kbYSXqg0m18BwnTqUHgpzpA+82lj5wHCVSRtqnj9aUZAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:35.356831Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.13307","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4b01330e4c55a01347e3c19821a936638d1acd8acad79697384f269a9d8bf3a","sha256:2da82611a3f0022498c661349a19a831d3e64e52f55ade1c5f4839449a2ddbd8"],"state_sha256":"265e02b4eec59a2b4cafe4e9c8365fbd1bc0885cf8b7219edf3102c0d0734cab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lNpLELfPIIqohQZznFAcf94hSThd7W4/lvnIdN38BwTj8bPF/i4qYEoCbtnqtKLDmpj9XPy/pfTyu+nAUsomBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T12:36:31.255605Z","bundle_sha256":"2ee8f25fa82ee46bf3b0fa6e76557e19cf08ff6aef815a8438b95242f1ae4ef3"}}