{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:23FKCN73LUECFJPTTOTNCQFOBD","short_pith_number":"pith:23FKCN73","canonical_record":{"source":{"id":"2409.09323","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T05:53:33Z","cross_cats_sorted":[],"title_canon_sha256":"798d514dea99bc61711c46625a28a52e7101123e4c329d98aba1003251263e9e","abstract_canon_sha256":"7ba283b406842d72aefabab70a68296eb6d39e7e9901964f945927ede0f061ec"},"schema_version":"1.0"},"canonical_sha256":"d6caa137fb5d0822a5f39ba6d140ae08f4ca4548460fd721a5ee658b7af45441","source":{"kind":"arxiv","id":"2409.09323","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.09323","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"arxiv_version","alias_value":"2409.09323v3","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09323","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_12","alias_value":"23FKCN73LUEC","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_16","alias_value":"23FKCN73LUECFJPT","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_8","alias_value":"23FKCN73","created_at":"2026-07-05T10:00:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:23FKCN73LUECFJPTTOTNCQFOBD","target":"record","payload":{"canonical_record":{"source":{"id":"2409.09323","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T05:53:33Z","cross_cats_sorted":[],"title_canon_sha256":"798d514dea99bc61711c46625a28a52e7101123e4c329d98aba1003251263e9e","abstract_canon_sha256":"7ba283b406842d72aefabab70a68296eb6d39e7e9901964f945927ede0f061ec"},"schema_version":"1.0"},"canonical_sha256":"d6caa137fb5d0822a5f39ba6d140ae08f4ca4548460fd721a5ee658b7af45441","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:41.150657Z","signature_b64":"Q+fU3FZh8ENkO3hbBTWGAQbecKxeSQ2JUgLu4u3g+xFWu436nJ0rDU7vm+FbMnFlBvI3AbkesSEBAYT3Jz8EAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6caa137fb5d0822a5f39ba6d140ae08f4ca4548460fd721a5ee658b7af45441","last_reissued_at":"2026-07-05T10:00:41.150249Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:41.150249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.09323","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:00:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"inNOh8X2Ku7+9rD/+KXEPOcLziZxT5dinRuTyIUWX5QqmWW1PgH+OV2gHu6pIefMyYL6OXMBUYO54zlocHKgAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:17:13.773879Z"},"content_sha256":"f338e349b358ee1b2bfe6f140410c77def65a8529ca138840b5e4e80be095d47","schema_version":"1.0","event_id":"sha256:f338e349b358ee1b2bfe6f140410c77def65a8529ca138840b5e4e80be095d47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:23FKCN73LUECFJPTTOTNCQFOBD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Mehrabian, Ilker Hacihaliloglu, Moein Heidari, Parsa Mojarad Adi","submitted_at":"2024-09-14T05:53:33Z","abstract_excerpt":"Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. However, existing INR models often fail to capture important frequency components specific to each task. To address this issue, in this paper, we propose a Fourier Kolmogorov Arnold network (FKAN) for INRs. The proposed FKAN utilizes learnable activation functions modeled as Fourier series in the first layer to effectively control and learn the task-specific frequency components. In addition, the activation functions w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09323","kind":"arxiv","version":3},"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/2409.09323/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":"2026-07-05T10:00:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cPJmhmHpl/QoswF9zJrpARpzlUlOJ9MFW+1LmZGdBOFqSqtoTJsIjrXp08vH8frQsLRfBVLl6AUuvtwY3JCGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:17:13.774375Z"},"content_sha256":"7c0ce092282c0820af00ce3eb28f68dd0779c7dc24fb098d2eed7427e0be637e","schema_version":"1.0","event_id":"sha256:7c0ce092282c0820af00ce3eb28f68dd0779c7dc24fb098d2eed7427e0be637e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/23FKCN73LUECFJPTTOTNCQFOBD/bundle.json","state_url":"https://pith.science/pith/23FKCN73LUECFJPTTOTNCQFOBD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/23FKCN73LUECFJPTTOTNCQFOBD/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":"2026-08-09T10:17:13Z","links":{"resolver":"https://pith.science/pith/23FKCN73LUECFJPTTOTNCQFOBD","bundle":"https://pith.science/pith/23FKCN73LUECFJPTTOTNCQFOBD/bundle.json","state":"https://pith.science/pith/23FKCN73LUECFJPTTOTNCQFOBD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/23FKCN73LUECFJPTTOTNCQFOBD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:23FKCN73LUECFJPTTOTNCQFOBD","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":"7ba283b406842d72aefabab70a68296eb6d39e7e9901964f945927ede0f061ec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T05:53:33Z","title_canon_sha256":"798d514dea99bc61711c46625a28a52e7101123e4c329d98aba1003251263e9e"},"schema_version":"1.0","source":{"id":"2409.09323","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.09323","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"arxiv_version","alias_value":"2409.09323v3","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.09323","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_12","alias_value":"23FKCN73LUEC","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_16","alias_value":"23FKCN73LUECFJPT","created_at":"2026-07-05T10:00:41Z"},{"alias_kind":"pith_short_8","alias_value":"23FKCN73","created_at":"2026-07-05T10:00:41Z"}],"graph_snapshots":[{"event_id":"sha256:7c0ce092282c0820af00ce3eb28f68dd0779c7dc24fb098d2eed7427e0be637e","target":"graph","created_at":"2026-07-05T10:00:41Z","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/2409.09323/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. However, existing INR models often fail to capture important frequency components specific to each task. To address this issue, in this paper, we propose a Fourier Kolmogorov Arnold network (FKAN) for INRs. The proposed FKAN utilizes learnable activation functions modeled as Fourier series in the first layer to effectively control and learn the task-specific frequency components. In addition, the activation functions w","authors_text":"Ali Mehrabian, Ilker Hacihaliloglu, Moein Heidari, Parsa Mojarad Adi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T05:53:33Z","title":"Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.09323","kind":"arxiv","version":3},"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:f338e349b358ee1b2bfe6f140410c77def65a8529ca138840b5e4e80be095d47","target":"record","created_at":"2026-07-05T10:00:41Z","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":"7ba283b406842d72aefabab70a68296eb6d39e7e9901964f945927ede0f061ec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-14T05:53:33Z","title_canon_sha256":"798d514dea99bc61711c46625a28a52e7101123e4c329d98aba1003251263e9e"},"schema_version":"1.0","source":{"id":"2409.09323","kind":"arxiv","version":3}},"canonical_sha256":"d6caa137fb5d0822a5f39ba6d140ae08f4ca4548460fd721a5ee658b7af45441","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6caa137fb5d0822a5f39ba6d140ae08f4ca4548460fd721a5ee658b7af45441","first_computed_at":"2026-07-05T10:00:41.150249Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:00:41.150249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q+fU3FZh8ENkO3hbBTWGAQbecKxeSQ2JUgLu4u3g+xFWu436nJ0rDU7vm+FbMnFlBvI3AbkesSEBAYT3Jz8EAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:00:41.150657Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.09323","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f338e349b358ee1b2bfe6f140410c77def65a8529ca138840b5e4e80be095d47","sha256:7c0ce092282c0820af00ce3eb28f68dd0779c7dc24fb098d2eed7427e0be637e"],"state_sha256":"471c7b9fad29c85956db36ebc09596360df4a7605869d9a4fdee8be8ba00f3f7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uUER9xWtzjf3jq4JdnMNR6rsUlUtIecPgkyupMmFBiQcVedZK8Qzl9OYunJH0RzvJMi8sKH9IlOERqsFsxbXDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T10:17:13.779540Z","bundle_sha256":"eeb0f97af2c575718a040beb672782ec2c0f77784a3f79d196dfb86261afe6ac"}}