{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3WMD4NUUCHBCJ3Y4M6RSNQZ4MA","short_pith_number":"pith:3WMD4NUU","canonical_record":{"source":{"id":"2012.09093","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-12-16T17:21:23Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"b75e5ea33b5cc0aedf6c680bd1b774cbc1ab567192d90ce470ad2c4c16b98e52","abstract_canon_sha256":"9df1879bc7ddfb44a5b8efaaf7ddc11abc91c05cae1a63a8dddc9b1fb8a3909a"},"schema_version":"1.0"},"canonical_sha256":"dd983e369411c224ef1c67a326c33c602677bd04fd390675fce69a0753c135d4","source":{"kind":"arxiv","id":"2012.09093","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.09093","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"2012.09093v2","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09093","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"3WMD4NUUCHBC","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_16","alias_value":"3WMD4NUUCHBCJ3Y4","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_8","alias_value":"3WMD4NUU","created_at":"2026-07-05T02:16:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3WMD4NUUCHBCJ3Y4M6RSNQZ4MA","target":"record","payload":{"canonical_record":{"source":{"id":"2012.09093","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-12-16T17:21:23Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"b75e5ea33b5cc0aedf6c680bd1b774cbc1ab567192d90ce470ad2c4c16b98e52","abstract_canon_sha256":"9df1879bc7ddfb44a5b8efaaf7ddc11abc91c05cae1a63a8dddc9b1fb8a3909a"},"schema_version":"1.0"},"canonical_sha256":"dd983e369411c224ef1c67a326c33c602677bd04fd390675fce69a0753c135d4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:16:51.854070Z","signature_b64":"Gi7uRlD6Cvonkx2bvmc9K3feKBI+zLYxMjm6S97vYzoDCjFheuhc/3Wxg6xIxa6NCwIlWWFaXh8wceuuvzsHDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd983e369411c224ef1c67a326c33c602677bd04fd390675fce69a0753c135d4","last_reissued_at":"2026-07-05T02:16:51.853607Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:16:51.853607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.09093","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":"2026-07-05T02:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"95TnYXFgHaCdOKl3SZeQBSmg+sZ+pWWz9bhYNwWo+p7WXrfcU0tJcnKfkqPEfOHGfOOr47FkU4CZeruAPJ+aBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T04:49:56.042000Z"},"content_sha256":"b2002dae7439abd14ca480bec3da5d2e6541129f71491b617c6c7e961e4fdb2c","schema_version":"1.0","event_id":"sha256:b2002dae7439abd14ca480bec3da5d2e6541129f71491b617c6c7e961e4fdb2c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3WMD4NUUCHBCJ3Y4M6RSNQZ4MA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Chunyang Wang, Huolin L. Xin, Rui Zhang, Ruoqian Lin, Xiao-Qing Yang","submitted_at":"2020-12-16T17:21:23Z","abstract_excerpt":"Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09093","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2012.09093/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":"2026-07-05T02:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oNYQ7F49tH2G0VDLXr+qu+gmCpvuPr+QDVfSdgVhQl3UIvohjoh+hvvJqigymBxOyiLuNL83jijswpIajVkwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T04:49:56.042547Z"},"content_sha256":"9a1b597ccd1d0f1d94e17197ad11de8d77fec1186c339cb490346a064f94d256","schema_version":"1.0","event_id":"sha256:9a1b597ccd1d0f1d94e17197ad11de8d77fec1186c339cb490346a064f94d256"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/bundle.json","state_url":"https://pith.science/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/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":"2026-08-01T04:49:56Z","links":{"resolver":"https://pith.science/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA","bundle":"https://pith.science/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/bundle.json","state":"https://pith.science/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3WMD4NUUCHBCJ3Y4M6RSNQZ4MA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3WMD4NUUCHBCJ3Y4M6RSNQZ4MA","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":"9df1879bc7ddfb44a5b8efaaf7ddc11abc91c05cae1a63a8dddc9b1fb8a3909a","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-12-16T17:21:23Z","title_canon_sha256":"b75e5ea33b5cc0aedf6c680bd1b774cbc1ab567192d90ce470ad2c4c16b98e52"},"schema_version":"1.0","source":{"id":"2012.09093","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.09093","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"2012.09093v2","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09093","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"3WMD4NUUCHBC","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_16","alias_value":"3WMD4NUUCHBCJ3Y4","created_at":"2026-07-05T02:16:51Z"},{"alias_kind":"pith_short_8","alias_value":"3WMD4NUU","created_at":"2026-07-05T02:16:51Z"}],"graph_snapshots":[{"event_id":"sha256:9a1b597ccd1d0f1d94e17197ad11de8d77fec1186c339cb490346a064f94d256","target":"graph","created_at":"2026-07-05T02:16:51Z","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/2012.09093/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic c","authors_text":"Chunyang Wang, Huolin L. Xin, Rui Zhang, Ruoqian Lin, Xiao-Qing Yang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-12-16T17:21:23Z","title":"TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09093","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:b2002dae7439abd14ca480bec3da5d2e6541129f71491b617c6c7e961e4fdb2c","target":"record","created_at":"2026-07-05T02:16:51Z","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":"9df1879bc7ddfb44a5b8efaaf7ddc11abc91c05cae1a63a8dddc9b1fb8a3909a","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2020-12-16T17:21:23Z","title_canon_sha256":"b75e5ea33b5cc0aedf6c680bd1b774cbc1ab567192d90ce470ad2c4c16b98e52"},"schema_version":"1.0","source":{"id":"2012.09093","kind":"arxiv","version":2}},"canonical_sha256":"dd983e369411c224ef1c67a326c33c602677bd04fd390675fce69a0753c135d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd983e369411c224ef1c67a326c33c602677bd04fd390675fce69a0753c135d4","first_computed_at":"2026-07-05T02:16:51.853607Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:16:51.853607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Gi7uRlD6Cvonkx2bvmc9K3feKBI+zLYxMjm6S97vYzoDCjFheuhc/3Wxg6xIxa6NCwIlWWFaXh8wceuuvzsHDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:16:51.854070Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.09093","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2002dae7439abd14ca480bec3da5d2e6541129f71491b617c6c7e961e4fdb2c","sha256:9a1b597ccd1d0f1d94e17197ad11de8d77fec1186c339cb490346a064f94d256"],"state_sha256":"a36e205accc4d43df018a693d5d41420022960c70848e1869b7c6cbd96f06309"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"krfUwE+izDzeMZqnkStyALg4L5/vhcaAMSec4BP+Y+v8Xf6VvzZ344d36smQhfni0TiZbteC3SNOVKYUvyvABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T04:49:56.048891Z","bundle_sha256":"fcb3b9ca590a55372328c9af6363a50b9fe54d1550a774e8737040d4154315c1"}}