{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:JEXSSNJWQATHYLNHY6NRHV35XK","short_pith_number":"pith:JEXSSNJW","schema_version":"1.0","canonical_sha256":"492f29353680267c2da7c79b13d77dbaba08dcc7408ff3bec81a6ab90c0ea73e","source":{"kind":"arxiv","id":"2104.03439","version":1},"attestation_state":"computed","paper":{"title":"Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","physics.optics"],"primary_cat":"cs.LG","authors_text":"Kshitij Bhardwaj, Maya Gokhale","submitted_at":"2021-04-08T00:20:36Z","abstract_excerpt":"Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental analysis technique used to determine the chemical composition of target samples, such as in industrial analysis of metals or in space exploration. Recently, there has been a rise in the use of machine learning (ML) techniques for LIBS data processing. However, ML for LIBS is challenging as: (i) the predictive models must be lightweight since they need to be deployed in highly resource-constrained and battery-operated portable LIBS systems; and (ii) since these systems can be remote, the models must be able to self-adapt t"},"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":"2104.03439","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-08T00:20:36Z","cross_cats_sorted":["eess.SP","physics.optics"],"title_canon_sha256":"d190f00b92dbf53bd800b6a3081162b2ac229b9eefcfd7de3e266c14f5ab5ecc","abstract_canon_sha256":"e0c9c243c6a37b2e9f1bbcba79b568300006e6374cd5b62714697c9ef0d8d8d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:30:26.207131Z","signature_b64":"RQzNi85otUFjIxDxC7OPcXdOp4QRRW7huJfDtz8Rzcxul9TVCyktSrsMQCizQZIVIedNmQZ2Ef6uhQYumf3hCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"492f29353680267c2da7c79b13d77dbaba08dcc7408ff3bec81a6ab90c0ea73e","last_reissued_at":"2026-07-05T02:30:26.206640Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:30:26.206640Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","physics.optics"],"primary_cat":"cs.LG","authors_text":"Kshitij Bhardwaj, Maya Gokhale","submitted_at":"2021-04-08T00:20:36Z","abstract_excerpt":"Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental analysis technique used to determine the chemical composition of target samples, such as in industrial analysis of metals or in space exploration. Recently, there has been a rise in the use of machine learning (ML) techniques for LIBS data processing. However, ML for LIBS is challenging as: (i) the predictive models must be lightweight since they need to be deployed in highly resource-constrained and battery-operated portable LIBS systems; and (ii) since these systems can be remote, the models must be able to self-adapt t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.03439","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/2104.03439/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":"2104.03439","created_at":"2026-07-05T02:30:26.206698+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.03439v1","created_at":"2026-07-05T02:30:26.206698+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.03439","created_at":"2026-07-05T02:30:26.206698+00:00"},{"alias_kind":"pith_short_12","alias_value":"JEXSSNJWQATH","created_at":"2026-07-05T02:30:26.206698+00:00"},{"alias_kind":"pith_short_16","alias_value":"JEXSSNJWQATHYLNH","created_at":"2026-07-05T02:30:26.206698+00:00"},{"alias_kind":"pith_short_8","alias_value":"JEXSSNJW","created_at":"2026-07-05T02:30:26.206698+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/JEXSSNJWQATHYLNHY6NRHV35XK","json":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK.json","graph_json":"https://pith.science/api/pith-number/JEXSSNJWQATHYLNHY6NRHV35XK/graph.json","events_json":"https://pith.science/api/pith-number/JEXSSNJWQATHYLNHY6NRHV35XK/events.json","paper":"https://pith.science/paper/JEXSSNJW"},"agent_actions":{"view_html":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK","download_json":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK.json","view_paper":"https://pith.science/paper/JEXSSNJW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.03439&json=true","fetch_graph":"https://pith.science/api/pith-number/JEXSSNJWQATHYLNHY6NRHV35XK/graph.json","fetch_events":"https://pith.science/api/pith-number/JEXSSNJWQATHYLNHY6NRHV35XK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK/action/storage_attestation","attest_author":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK/action/author_attestation","sign_citation":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK/action/citation_signature","submit_replication":"https://pith.science/pith/JEXSSNJWQATHYLNHY6NRHV35XK/action/replication_record"}},"created_at":"2026-07-05T02:30:26.206698+00:00","updated_at":"2026-07-05T02:30:26.206698+00:00"}