{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2BIV6GLQI6VUBDUOLMX27ZD7AL","short_pith_number":"pith:2BIV6GLQ","schema_version":"1.0","canonical_sha256":"d0515f197047ab408e8e5b2fafe47f02edf57917fa2eeeae772077489266ae4f","source":{"kind":"arxiv","id":"2606.06671","version":1},"attestation_state":"computed","paper":{"title":"JA-SIREN: Deterministic Initialization for Sinusoidal Networks via Spectral Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"John M. Dolan, Kejia Hu, Mohammed Alsakabi, Ozan K. Tonguz","submitted_at":"2026-06-04T19:45:43Z","abstract_excerpt":"Existing implicit neural representation (INR) approaches suffer from stochastic initialization that does not guarantee consistent or high-quality performance across runs, with variations reaching more than 2.5 dB (78%) in image regression. This variation is problematic for scientific computing and simulation, where result reproducibility is crucial. To address this problem, we present Jacobi-Anger Sinusoidal Representation Network (JA-SIREN), a deterministic initialization scheme for sinusoidal networks grounded in classical spectral analysis. By computing the Discrete Sine Transform (DST) of "},"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":"2606.06671","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-04T19:45:43Z","cross_cats_sorted":[],"title_canon_sha256":"ce09135d81eaa82286448ad54a7927da55805010898ffd05f74d14610df9c91d","abstract_canon_sha256":"7f54ea7b72cff600db2df6814ab5a467899a4bcc3d18a74eec96a44c571e2ddd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:21.749674Z","signature_b64":"ANVLVxLkCve49LjX4eFop17KpbdRS+TBlxVDYNZfsg3ao5Y+JiaDYvc5TrWxf+FknynStiYwyAf+H5Fet8Q+Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0515f197047ab408e8e5b2fafe47f02edf57917fa2eeeae772077489266ae4f","last_reissued_at":"2026-06-08T01:04:21.749044Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:21.749044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"JA-SIREN: Deterministic Initialization for Sinusoidal Networks via Spectral Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"John M. Dolan, Kejia Hu, Mohammed Alsakabi, Ozan K. Tonguz","submitted_at":"2026-06-04T19:45:43Z","abstract_excerpt":"Existing implicit neural representation (INR) approaches suffer from stochastic initialization that does not guarantee consistent or high-quality performance across runs, with variations reaching more than 2.5 dB (78%) in image regression. This variation is problematic for scientific computing and simulation, where result reproducibility is crucial. To address this problem, we present Jacobi-Anger Sinusoidal Representation Network (JA-SIREN), a deterministic initialization scheme for sinusoidal networks grounded in classical spectral analysis. By computing the Discrete Sine Transform (DST) of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06671","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/2606.06671/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":"2606.06671","created_at":"2026-06-08T01:04:21.749136+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06671v1","created_at":"2026-06-08T01:04:21.749136+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06671","created_at":"2026-06-08T01:04:21.749136+00:00"},{"alias_kind":"pith_short_12","alias_value":"2BIV6GLQI6VU","created_at":"2026-06-08T01:04:21.749136+00:00"},{"alias_kind":"pith_short_16","alias_value":"2BIV6GLQI6VUBDUO","created_at":"2026-06-08T01:04:21.749136+00:00"},{"alias_kind":"pith_short_8","alias_value":"2BIV6GLQ","created_at":"2026-06-08T01:04:21.749136+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/2BIV6GLQI6VUBDUOLMX27ZD7AL","json":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL.json","graph_json":"https://pith.science/api/pith-number/2BIV6GLQI6VUBDUOLMX27ZD7AL/graph.json","events_json":"https://pith.science/api/pith-number/2BIV6GLQI6VUBDUOLMX27ZD7AL/events.json","paper":"https://pith.science/paper/2BIV6GLQ"},"agent_actions":{"view_html":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL","download_json":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL.json","view_paper":"https://pith.science/paper/2BIV6GLQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06671&json=true","fetch_graph":"https://pith.science/api/pith-number/2BIV6GLQI6VUBDUOLMX27ZD7AL/graph.json","fetch_events":"https://pith.science/api/pith-number/2BIV6GLQI6VUBDUOLMX27ZD7AL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL/action/storage_attestation","attest_author":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL/action/author_attestation","sign_citation":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL/action/citation_signature","submit_replication":"https://pith.science/pith/2BIV6GLQI6VUBDUOLMX27ZD7AL/action/replication_record"}},"created_at":"2026-06-08T01:04:21.749136+00:00","updated_at":"2026-06-08T01:04:21.749136+00:00"}