{"as_of":"2026-08-17T04:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2c5d4022aeee4c8c73b8d128cc3a53da0a688eba9161ac95b75db62fb2e1ebee","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:44:02.592775Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2504.14782/citation-record","integrity":"/paper/2504.14782/integrity","json":"/paper/2504.14782/citation-record.json","paper":"/paper/2504.14782"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.579983Z","title":"Clemens, S","venue":null,"work_id":"61899df9-75b4-4b71-bf85-bbef15cae344","year":2017},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.226373Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:732fd170e1521eb442021e8623cdb94f0322244dfdab9030fc1bf4f4aa830f8a","observation_id":"115ed68d-3b61-4bfa-9749-32defaad41b5","resolution":{"observed_at":"2026-08-16T11:44:03.585207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.561099Z","title":"Review—Modeling Methods for Analysis of Electromigration Degradation in Nano-Interconnects,","venue":null,"work_id":"51b7aff9-9479-49b1-b8d7-aeb68e6f38d8","year":2021},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.232227Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:0b004a279264e9faaf15d2e0d64816eae9286dd53f9581f1abae2b558451b8f8","observation_id":"87292b65-259d-48d2-9fa4-26a10ceed7b3","resolution":{"observed_at":"2026-08-16T11:44:03.567111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.542710Z","title":"Effect of metal line width on electromigration of BEOL Cu interconnects,","venue":null,"work_id":"a1b20672-4453-428a-8f42-34083101668c","year":2018},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.239537Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:7f1f1ce1920b723b09317c7d5c30e7026cdb71f4157c6047d2da72690fef08a5","observation_id":"17e94967-c78a-4992-9579-b40323fce09c","resolution":{"observed_at":"2026-08-16T11:44:03.549387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.522440Z","title":"Microstructure Evolution and Effect on Resistivity for Cu Nanointerconnects and Beyond,","venue":null,"work_id":"fc1d8b2b-5b1d-4304-9fd6-e18cdcc03b9c","year":2018},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.245582Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:b5fe7fc3dc716872dd1c187ea28ec7a1257a48ee0eb5cc1dc73e45281d6469b1","observation_id":"fefc4038-223c-4fc5-a8df-5274fe735a78","resolution":{"observed_at":"2026-08-16T11:44:03.528175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.502849Z","title":null,"venue":null,"work_id":"0b7b5f6b-7d93-4f65-9624-48d49017ba2c","year":1973},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.252282Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:84909e62a431472de19e7624a28a60da0e18c1cd1c42ba2062c27555ba33d27d","observation_id":"cd2d71c8-6fc1-4cd5-ac71-2204d0fb0a0a","resolution":{"observed_at":"2026-08-16T11:44:03.508290Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.483781Z","title":null,"venue":null,"work_id":"440ac744-fff1-41ae-bd96-2909bf8566b4","year":1990},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.258779Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:ffcff31573f834b979f7b8a35f3395739c2c168f22f318b065448e08892da7ca","observation_id":"214e2e23-6ac1-4a87-a4b1-2ec4362b1164","resolution":{"observed_at":"2026-08-16T11:44:03.488928Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.467176Z","title":null,"venue":null,"work_id":"9455e1f7-ffd4-4678-bd6a-4a6a9b14c5cd","year":1997},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.265526Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:01bf63c4b72fc31d8ad8fd32e4b45371276ecab0ab87623053cb534d54172861","observation_id":"9a736ba1-73fd-4a58-b1c4-273a48d87e80","resolution":{"observed_at":"2026-08-16T11:44:03.471767Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.447292Z","title":"Machine vision for three - dimensional scenes","venue":null,"work_id":"dd826a3e-fe23-4899-8366-bc3a08b16afe","year":1990},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.270627Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:b046b937314ba087adf8e47f5a2792365a40618ed18c657b9a730d7c81106bdb","observation_id":"8f184ad7-82ae-4f0b-b001-e49f555154b8","resolution":{"observed_at":"2026-08-16T11:44:03.453645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.430321Z","title":"A Computational Approach to Edge Detection,","venue":null,"work_id":"b1656a4f-1913-45fc-adaf-088e48553039","year":1986},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.278336Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:85b389347a9c2cd4ff84ff9ca11037c1699fa0ca5c9f3b1c2784279f07b57cb5","observation_id":"73b1053d-d15d-4034-a904-265de7a0b046","resolution":{"observed_at":"2026-08-16T11:44:03.435780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.413764Z","title":"Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform,","venue":null,"work_id":"f81cd4ea-b444-44bf-abb3-2cce0eda1cf1","year":2018},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.282825Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:5fa300ea2de8a3e72d16ab6981da8fd430f4cd02690862e71e0491afc11b7323","observation_id":"1a3cb5e8-f156-45b3-922d-905dd0160d46","resolution":{"observed_at":"2026-08-16T11:44:03.419704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.387574Z","title":"An automated methodology for grain segmentation and grain size measurement from optical micrographs,","venue":null,"work_id":"0fecee94-0328-48a1-a34b-3b8a7831cbf7","year":2019},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.287779Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:573ded63e95f5686e06a20f0413626792abfef5d5213fd26deacf7c4c235e821","observation_id":"a7866a64-df35-4b71-8f72-cc0a2fdb499a","resolution":{"observed_at":"2026-08-16T11:44:03.392939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.365965Z","title":"De-noising Filters for TEM (Transmission Electron Microscopy) Image of Nanomaterials,","venue":null,"work_id":"2d5f4ac2-b006-4180-8a9f-f2d67e91d2f5","year":2012},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.292441Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:38ec64105f61b8cd296520242fccfdf6a1c7caa7173414ebb659badf95accaf8","observation_id":"337c45c7-7ac0-4bd9-aa73-03d7fd578b45","resolution":{"observed_at":"2026-08-16T11:44:03.374482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.340155Z","title":"New methods for automatic quantification of microstructural features using digital image processing,","venue":null,"work_id":"36a7e2f6-8a64-4a1b-95e2-d20653fe6b10","year":2018},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.297068Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:01d6f1f52aadeb16549aa8da5688d9ad39dab77bac1ab29ca06d2b836f10fc53","observation_id":"4d744e89-0ae2-487f-8669-74aadb9abcc8","resolution":{"observed_at":"2026-08-16T11:44:03.348407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.308638Z","title":"Deep learning object detection in materials science: Current state and future directions,","venue":null,"work_id":"2d05add0-f2e9-4662-ae6e-aac05de88dc5","year":2022},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.301596Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:259a97469d79748a7b07197796bb16fd09228f91930a2ba3f9ceb6a2ae417c10","observation_id":"30d2b32e-1f5f-4397-9a46-4110d7320547","resolution":{"observed_at":"2026-08-16T11:44:03.315299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.288030Z","title":"Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm,","venue":null,"work_id":"cbd106bf-dd30-4645-a69e-a23d5c728dbf","year":2024},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.308099Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:192e5cef0dd3c8e6e0fbbd254ad48cce08965c5ebcf6be2bcf828f7214afce55","observation_id":"fa6b6ada-3470-4129-8ac0-143e57bc1985","resolution":{"observed_at":"2026-08-16T11:44:03.293635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.267714Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics,","venue":null,"work_id":"65158718-c40b-4516-9cc7-84ff4988aa51","year":2015},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.312651Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:763305c206b11cabf3a218bb528c5c8b21b0e51d8f5d01e7a440b21479525b9c","observation_id":"c38f5a0d-8082-4c54-8db5-065572aa33dc","resolution":{"observed_at":"2026-08-16T11:44:03.275620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-16T11:44:02.317720Z","title":"Denoising Diffusion Probabilistic Models,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.317720Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:9f11b35bc3e6fcf111ef11c09eb4b4f5dafebde98b49d63422590e90912d8ee1","observation_id":"39434fb8-78de-4a8c-83b7-04c7df85df91","resolution":{"observed_at":"2026-08-16T11:44:02.317720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.248236Z","title":"SDXK: improving latent diffusion models for high-resolution image synthesis,","venue":null,"work_id":"f6682a5c-2a0d-4124-a501-dff2eeb38698","year":2024},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.323409Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:957001b0514fdee7c0e2af9083453afe64e955a513b695996bdd9e0d1ab8bc8e","observation_id":"1f80e431-6e72-4d5d-ae7c-b1403d0ca9fa","resolution":{"observed_at":"2026-08-16T11:44:03.254580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.218538Z","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis,","venue":null,"work_id":"fb16ef6b-4774-4363-b8b4-8efc06d79a20","year":2021},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.329677Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:8ffe52e14b7d93940a36609fa6eb5eb3f7fb40953170bc4dba683faf6b4b0105","observation_id":"7e5ec794-0768-43ce-adef-6337c9d57c38","resolution":{"observed_at":"2026-08-16T11:44:03.225037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.197163Z","title":"Predicting sample size required for classification performance,","venue":null,"work_id":"18270e27-265a-45e1-9125-dab01739c1cd","year":2012},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.336016Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:710eae99c934ab8eafa554c6392c4d5f37cdf60ef50c3a9a35f80bf1245ef8cf","observation_id":"10c0153b-cc73-4c80-833f-457fdb8d3fdb","resolution":{"observed_at":"2026-08-16T11:44:03.204339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.172425Z","title":"Chakravorti, Electric Field Analysis, CRC Press, 2017","venue":null,"work_id":"ef72ebfc-e63a-4fb6-a57a-2e93b46a991b","year":2017},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.340659Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:894c242a8637962156c8c0b4897b7a5f52fb86c67359bfdf83af9670db0ee0e4","observation_id":"54d50052-d2ab-47be-9901-b0860ddeb159","resolution":{"observed_at":"2026-08-16T11:44:03.178079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.148902Z","title":"An electrostatic study of curvature effects on electric field stress in high voltage differentials,","venue":null,"work_id":"fd564661-00d0-4017-bd87-1e3ce8c7437e","year":2019},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.345610Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:6c34e80cf9d59cb160b96ccd84cf33d14a4e0e67477650c5470c8202b2a29c6e","observation_id":"d8ffcf1f-c8a5-4212-aa81-4cb1e504a416","resolution":{"observed_at":"2026-08-16T11:44:03.156660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.127843Z","title":"Kleppner and R","venue":null,"work_id":"81aec58a-a67f-4cb1-8adb-a3fb36376a3c","year":2013},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.350631Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:9b2177abc0c036ca08bde7c6b6f7bdf0c35ddc1662a713f331022d0f35f2067f","observation_id":"a7132752-870a-4cfc-b63a-16774f1edb4f","resolution":{"observed_at":"2026-08-16T11:44:03.135036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.101443Z","title":"Computer Simulation of Powder Compaction of Spherical Particles,","venue":null,"work_id":"e4a9e52d-043c-43fd-8fbd-c0e50ce04c32","year":1998},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.355291Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:d5640855287ee9185b7c2eabab6f4731897bf5f9932f200d5df355ff5d951a1f","observation_id":"e31966af-d36e-4fac-9ef8-070e5edd973d","resolution":{"observed_at":"2026-08-16T11:44:03.107889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.065401Z","title":"Simulation of polycrystalline structure with Voronoi diagram in Laguerre geometry based on random closed packing of spheres,","venue":null,"work_id":"7db46fe7-0491-4e5b-81da-d73a5bb536e3","year":2004},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.359787Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:423c504ee99260f61ba536b8debf873c4cad41a14af339834de2e25ce31911fd","observation_id":"cb7e8708-20b5-4a1d-9b89-dd42b1f2eeb2","resolution":{"observed_at":"2026-08-16T11:44:03.076783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.035916Z","title":null,"venue":null,"work_id":"7801df87-15b0-42e3-aaf7-a46a335ca704","year":2010},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.364654Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:10cddf74ab0d00293fe9486fff96ebf303fa03db680cf835c766a235dfa6b91d","observation_id":"e9886a76-51ac-4788-9e75-86504fc377b8","resolution":{"observed_at":"2026-08-16T11:44:03.043338Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:03.012181Z","title":"An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types,","venue":null,"work_id":"1e5fcd29-9927-4377-9159-667949f36168","year":2022},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.369486Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:946cb7736cd75f35b7aed12afd4f87e558d9169a780791247078e2fae33cb669","observation_id":"b5e6e863-5aad-4418-885a-01b02b805d86","resolution":{"observed_at":"2026-08-16T11:44:03.020082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.983432Z","title":"A Review of Classic Edge Detectors,","venue":null,"work_id":"c791a44c-32f2-49ac-ba0e-1d621378d58c","year":2015},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.525558Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:06dbd669d53077da4520f9c0528a59b9ead70544a778fb7fe370940410d19d70","observation_id":"e9e993fa-b9fa-4cbd-b4e5-6ff363687100","resolution":{"observed_at":"2026-08-16T11:44:02.997536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.961734Z","title":"Numerical evaluation of grain boundary electron scattering in molybdenum thin films: A critical analysis for advanced interconnects,","venue":null,"work_id":"06849e51-134e-40a4-b772-8f8c23f62aa0","year":2024},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.530899Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:8908e9840f9e10de7c4aaafad467aa190e816818729420026ad5f807a2b2fae6","observation_id":"21ad7ace-07ad-4763-81be-f6aec69f46b5","resolution":{"observed_at":"2026-08-16T11:44:02.967798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.938278Z","title":"Consistency Models,","venue":null,"work_id":"54b071ed-d535-412c-9504-7b24e6a02d20","year":2023},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.536780Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:0aa47839a1d8419c3e7b78e049326a93436055e1bf8a97b7de2d88b8749a96e8","observation_id":"ca6dc13b-a31c-427c-9e47-01602328c53a","resolution":{"observed_at":"2026-08-16T11:44:02.944962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.915058Z","title":"Generative Modeling by Estimating Gradients of the Data Distribution,","venue":null,"work_id":"18bcdba9-345e-4502-bff8-4c56aa2bf0d3","year":2019},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.541720Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:cbf365837184dd9439680884e2336f9fd433ee862f48d546a0eb482f60f864d1","observation_id":"32feeba2-5841-45d4-9f48-9b1fac1a49e5","resolution":{"observed_at":"2026-08-16T11:44:02.920626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.894789Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations,","venue":null,"work_id":"36f24512-b2be-49f7-9bde-2f2078bc69da","year":2021},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.546309Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:f772d5bc5c237c60092762d8e382e9bd1d69e5358b6b0643b69054311cdcb28c","observation_id":"bce951f9-71b9-4bcb-bc07-4db0fe6b9dde","resolution":{"observed_at":"2026-08-16T11:44:02.901324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.858690Z","title":"Two‐Dimensional Motion of Idealized Grain Boundaries,","venue":null,"work_id":"6375dd2d-7d47-4a95-86b6-34fd1da59687","year":1956},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.550615Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:587e4750d98952a1ddb5dd0336985b284c09b5f0111870695cfce55632bf2597","observation_id":"8347b79c-10ee-40be-ad6b-c32efecfae28","resolution":{"observed_at":"2026-08-16T11:44:02.867206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.834799Z","title":"Grain Shapes and Other Metallurgical Applications of Topology,","venue":null,"work_id":"9124bd7f-d354-45ff-937a-0ed8692b2b01","year":2015},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.555097Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:6a201259b35d034030132fd15fa3195aa2756e61b956eb858b5ad54b48ea2294","observation_id":"f1259c66-7905-414f-b1c1-a3c3cbdc7223","resolution":{"observed_at":"2026-08-16T11:44:02.842405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.808779Z","title":null,"venue":null,"work_id":"4ed0f0e8-619d-4c81-94da-212b42d84ac6","year":2009},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.559710Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:4365959f7bd41c0821d227fe3b719c8b8e334c88de36510fdef850431f9fa4a4","observation_id":"30443196-35a9-4f16-b94f-fe358c57fb86","resolution":{"observed_at":"2026-08-16T11:44:02.817228Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.781243Z","title":"A strategy for synthetic microstructure generation and crystal plasticity parameter calibration of fine-grain-structured dual-phase steel,","venue":null,"work_id":"db75107a-a49b-4a60-be8f-3b476e0b4346","year":2020},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.564865Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:a8a8aee464f2a538f57679bd6481b054e0e7dbad3f5868495e41462beba4861d","observation_id":"6f859836-6d70-4bea-9b17-0735ddbe99e0","resolution":{"observed_at":"2026-08-16T11:44:02.789479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.756842Z","title":"A novel point inclusion test for convex polygons based on Voronoi tessellations,","venue":null,"work_id":"7d2c74c5-666c-47de-a107-8cce51f3f1c0","year":2021},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.569206Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:0397065d76016a478a06c254f5a40ba4703390879da78e3a4c0bef87b1d12c0c","observation_id":"8cc3ffd9-6c9d-4df0-8ea8-e55cf09369e2","resolution":{"observed_at":"2026-08-16T11:44:02.763736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-16T11:44:02.573696Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.573696Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:f0e4f5ae4cd33bf5a4f19a8490992f024b50908689a3595f0d1ec7a622323333","observation_id":"2d1ed945-f58a-4981-b2d6-eb45995dcec9","resolution":{"observed_at":"2026-08-16T11:44:02.573696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07085","last_updated":"2023-01-17T03:58:13Z","snapshot_observed_at":"2026-08-16T16:53:00.589268Z","submitted_at":"2022-06-14T18:19:05Z","title":"Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07085","snapshot_observed_at":"2026-08-16T11:44:02.578489Z","title":"Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.578489Z"},"links":{"cited_paper":"/paper/2206.07085","citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:8cfd9f650dff7da0c3a2089965c7e3467e4d31e5e0445f40a82e096697b96d88","observation_id":"e62e9cab-57a3-459c-9018-d1eb34c95cf5","resolution":{"observed_at":"2026-08-16T11:44:02.578489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.730187Z","title":"An Overview of Overfitting and its Solutions,","venue":null,"work_id":"2d57af49-7ad1-4765-830f-6b62b5880303","year":2019},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.583255Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:81370767c83497d8557ad9fd6094f2461d54d8a1e61c9048ef732339feabbfb3","observation_id":"6ac3a2a8-c4d3-43bf-a418-45be7e9c013a","resolution":{"observed_at":"2026-08-16T11:44:02.741054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.711128Z","title":"Attention Is All You Need,","venue":null,"work_id":"ce57a99e-13bd-4a52-b1df-30e53ff7b6e5","year":2017},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.587841Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:bfb138ef1223595a81b0f31f069df03b9e7b8d27417523a52e2fd74462ad14d0","observation_id":"7ec73c7c-dbef-4463-8c61-d2eba79dfe4b","resolution":{"observed_at":"2026-08-16T11:44:02.717130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:44:02.682398Z","title":"Physics -informed machine learning,","venue":null,"work_id":"686ab28b-8b4c-4b35-bab5-5cb2103e3f89","year":2021},"citing_paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T11:44:02.592775Z"},"links":{"citing_paper":"/paper/2504.14782"},"observation_digest":"sha256:487513fdb72e67fa79c03e93c51d9d2fd4f1846cceb0357ff2e9412b6bdba7b1","observation_id":"968b087e-161d-4bc8-b736-a84a5fa3d927","resolution":{"observed_at":"2026-08-16T11:44:02.689712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.14782","last_updated":"2025-04-21T00:46:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T11:38:44.004432Z","submitted_at":"2025-04-21T00:46:28Z","title":"Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":34},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2504.14782."}