{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:XYRLZNCWJOOXREQRTYPZDYDNN6","short_pith_number":"pith:XYRLZNCW","canonical_record":{"source":{"id":"1903.00388","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-01T16:15:56Z","cross_cats_sorted":[],"title_canon_sha256":"d584d9678ebf85056ea2eb5a04fa096dddfa74789737488dc12a76972ab4ad50","abstract_canon_sha256":"ea8a9690c5fb27012c067cc005290cec1a0b88b20582a8066ae37df40e4896c6"},"schema_version":"1.0"},"canonical_sha256":"be22bcb4564b9d7892119e1f91e06d6f8756ca43912712be6854558e3c428315","source":{"kind":"arxiv","id":"1903.00388","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00388","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00388v2","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00388","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"pith_short_12","alias_value":"XYRLZNCWJOOX","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XYRLZNCWJOOXREQR","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XYRLZNCW","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:XYRLZNCWJOOXREQRTYPZDYDNN6","target":"record","payload":{"canonical_record":{"source":{"id":"1903.00388","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-01T16:15:56Z","cross_cats_sorted":[],"title_canon_sha256":"d584d9678ebf85056ea2eb5a04fa096dddfa74789737488dc12a76972ab4ad50","abstract_canon_sha256":"ea8a9690c5fb27012c067cc005290cec1a0b88b20582a8066ae37df40e4896c6"},"schema_version":"1.0"},"canonical_sha256":"be22bcb4564b9d7892119e1f91e06d6f8756ca43912712be6854558e3c428315","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:40.631088Z","signature_b64":"34OulsbRaR1y2CY8f1UQXdimEQYxv5s95gK9NpseSHRB62hDY1AHd0bVOZ5r2/+oxOsuczI+2xMsWpIGAxZ1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be22bcb4564b9d7892119e1f91e06d6f8756ca43912712be6854558e3c428315","last_reissued_at":"2026-05-17T23:50:40.630641Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:40.630641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.00388","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":"2026-05-17T23:50:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O8rDtC0q19xQmqn2TZE/R0z/06/4CX9JGlCW6y93rY1FtCnleKz6eW5oKJK44P98iYybKJAeIaLtk6kzYUq+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:12:56.761454Z"},"content_sha256":"c8573ca4fafc816273a1462ce2a3c6800b83e67091183c1c664d4ff5f885973f","schema_version":"1.0","event_id":"sha256:c8573ca4fafc816273a1462ce2a3c6800b83e67091183c1c664d4ff5f885973f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:XYRLZNCWJOOXREQRTYPZDYDNN6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hua Li, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, Shenghua He","submitted_at":"2019-03-01T16:15:56Z","abstract_excerpt":"Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new density regression-based method for automatic cell counting that reduces the need to manually annotate experimental images. A supervised learning-based density regression model (DRM) is trained with annotated synthetic images (the source domain) and their corresponding ground truth density maps. A domain adaptation model (DAM) is built to map experimental images (the target domain)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00388","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":"2026-05-17T23:50:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n7jCVGlNM12tRNAwB+nmRTUyH66zWGLtzQ8D2NJjzQziM0num1U+jpQKZtNqIRJp151g1F7j5BacLLywB4uWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T00:12:56.762158Z"},"content_sha256":"68fcee894fbe46ae8ad59c707bc0ce09bb004e9637ec41aa5a8b319071798617","schema_version":"1.0","event_id":"sha256:68fcee894fbe46ae8ad59c707bc0ce09bb004e9637ec41aa5a8b319071798617"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/bundle.json","state_url":"https://pith.science/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/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":"2026-06-12T00:12:56Z","links":{"resolver":"https://pith.science/pith/XYRLZNCWJOOXREQRTYPZDYDNN6","bundle":"https://pith.science/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/bundle.json","state":"https://pith.science/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XYRLZNCWJOOXREQRTYPZDYDNN6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:XYRLZNCWJOOXREQRTYPZDYDNN6","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":"ea8a9690c5fb27012c067cc005290cec1a0b88b20582a8066ae37df40e4896c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-01T16:15:56Z","title_canon_sha256":"d584d9678ebf85056ea2eb5a04fa096dddfa74789737488dc12a76972ab4ad50"},"schema_version":"1.0","source":{"id":"1903.00388","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00388","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00388v2","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00388","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"pith_short_12","alias_value":"XYRLZNCWJOOX","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XYRLZNCWJOOXREQR","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XYRLZNCW","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:68fcee894fbe46ae8ad59c707bc0ce09bb004e9637ec41aa5a8b319071798617","target":"graph","created_at":"2026-05-17T23:50:40Z","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":"Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new density regression-based method for automatic cell counting that reduces the need to manually annotate experimental images. A supervised learning-based density regression model (DRM) is trained with annotated synthetic images (the source domain) and their corresponding ground truth density maps. A domain adaptation model (DAM) is built to map experimental images (the target domain)","authors_text":"Hua Li, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, Shenghua He","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-01T16:15:56Z","title":"Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00388","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:c8573ca4fafc816273a1462ce2a3c6800b83e67091183c1c664d4ff5f885973f","target":"record","created_at":"2026-05-17T23:50:40Z","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":"ea8a9690c5fb27012c067cc005290cec1a0b88b20582a8066ae37df40e4896c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-01T16:15:56Z","title_canon_sha256":"d584d9678ebf85056ea2eb5a04fa096dddfa74789737488dc12a76972ab4ad50"},"schema_version":"1.0","source":{"id":"1903.00388","kind":"arxiv","version":2}},"canonical_sha256":"be22bcb4564b9d7892119e1f91e06d6f8756ca43912712be6854558e3c428315","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be22bcb4564b9d7892119e1f91e06d6f8756ca43912712be6854558e3c428315","first_computed_at":"2026-05-17T23:50:40.630641Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:40.630641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"34OulsbRaR1y2CY8f1UQXdimEQYxv5s95gK9NpseSHRB62hDY1AHd0bVOZ5r2/+oxOsuczI+2xMsWpIGAxZ1Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:40.631088Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00388","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8573ca4fafc816273a1462ce2a3c6800b83e67091183c1c664d4ff5f885973f","sha256:68fcee894fbe46ae8ad59c707bc0ce09bb004e9637ec41aa5a8b319071798617"],"state_sha256":"7cf9f59a10da3d571fc8e477ce02a2b71552fcd8aad1fdc140b665c70b03b272"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NPEI/3m0VrDZsIzPT2S/OrfjmLCn+kOC769O9kAdvDAI8pSXsj91O/KmJtPvtc488R3KddULObWuRcNaIpqMBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T00:12:56.765760Z","bundle_sha256":"52426292931bbce3b62bada870f23ace8802627d8594d27a001180ed9e90a8c1"}}