{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3ZAC7MKWUUZE7HNOLYWFJP3ZGI","short_pith_number":"pith:3ZAC7MKW","schema_version":"1.0","canonical_sha256":"de402fb156a5324f9dae5e2c54bf79320c4b5a0bc20074c8031d8b4d0aef64a3","source":{"kind":"arxiv","id":"1908.05271","version":1},"attestation_state":"computed","paper":{"title":"End-to-End Machine Learning for Experimental Physics: Using Simulated Data to Train a Neural Network for Object Detection in Video Microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.soft","authors_text":"Adam A. S. Green, Cheol S. Park, Eric N. Minor, Noel A. Clark, Stian D. Howard","submitted_at":"2019-08-13T00:32:45Z","abstract_excerpt":"We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data. Modern machine learning methods require large, robust training data sets to generate accurate predictions. Generating these large training sets requires a significant up-front time investment that is often impractical for small-scale applications. Here we demonstrate a `full-stack' computational solution, where the training data set is generated on-the-fly using a noise injection process to produce simulated data characteristic of the experimental system."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1908.05271","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.soft","submitted_at":"2019-08-13T00:32:45Z","cross_cats_sorted":["cond-mat.dis-nn"],"title_canon_sha256":"6b006eef6d06f06f982d60e89e2e075f386003e6264106cea7b8ddf88545d53e","abstract_canon_sha256":"b1b49abdb68fccb610ef42703f63c71241529bf5d3fe467aab24bcf570fe437f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:56:38.134095Z","signature_b64":"BK8dOy2INT2B67tDDfWIVDWRKZB9hUdm1CNz9twob9OEjqhixV1xlhIWP6NC4aYkAso9e8BjYqxK+Dg9CajLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de402fb156a5324f9dae5e2c54bf79320c4b5a0bc20074c8031d8b4d0aef64a3","last_reissued_at":"2026-07-04T23:56:38.133620Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:56:38.133620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Machine Learning for Experimental Physics: Using Simulated Data to Train a Neural Network for Object Detection in Video Microscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn"],"primary_cat":"cond-mat.soft","authors_text":"Adam A. S. Green, Cheol S. Park, Eric N. Minor, Noel A. Clark, Stian D. Howard","submitted_at":"2019-08-13T00:32:45Z","abstract_excerpt":"We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data. Modern machine learning methods require large, robust training data sets to generate accurate predictions. Generating these large training sets requires a significant up-front time investment that is often impractical for small-scale applications. Here we demonstrate a `full-stack' computational solution, where the training data set is generated on-the-fly using a noise injection process to produce simulated data characteristic of the experimental system."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05271","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.05271/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.05271","created_at":"2026-07-04T23:56:38.133681+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.05271v1","created_at":"2026-07-04T23:56:38.133681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05271","created_at":"2026-07-04T23:56:38.133681+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZAC7MKWUUZE","created_at":"2026-07-04T23:56:38.133681+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZAC7MKWUUZE7HNO","created_at":"2026-07-04T23:56:38.133681+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZAC7MKW","created_at":"2026-07-04T23:56:38.133681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI","json":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI.json","graph_json":"https://pith.science/api/pith-number/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/graph.json","events_json":"https://pith.science/api/pith-number/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/events.json","paper":"https://pith.science/paper/3ZAC7MKW"},"agent_actions":{"view_html":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI","download_json":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI.json","view_paper":"https://pith.science/paper/3ZAC7MKW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.05271&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/action/storage_attestation","attest_author":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/action/author_attestation","sign_citation":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/action/citation_signature","submit_replication":"https://pith.science/pith/3ZAC7MKWUUZE7HNOLYWFJP3ZGI/action/replication_record"}},"created_at":"2026-07-04T23:56:38.133681+00:00","updated_at":"2026-07-04T23:56:38.133681+00:00"}