{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:536YR77EYMG27RPSYBCYHXJI5T","short_pith_number":"pith:536YR77E","canonical_record":{"source":{"id":"2504.11026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-15T09:50:03Z","cross_cats_sorted":[],"title_canon_sha256":"0171d24f5b2964ef3d419975e67de110488235a4778ad476bec5ef0102eaa497","abstract_canon_sha256":"48e56b898038e11b180c633d298fcf906c07afbdf2da51a7c4b195261fe10f0f"},"schema_version":"1.0"},"canonical_sha256":"eefd88ffe4c30dafc5f2c04583dd28ecd7903d0970f2dec10a85a7ba3e231b72","source":{"kind":"arxiv","id":"2504.11026","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.11026","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"arxiv_version","alias_value":"2504.11026v1","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11026","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_12","alias_value":"536YR77EYMG2","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_16","alias_value":"536YR77EYMG27RPS","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_8","alias_value":"536YR77E","created_at":"2026-07-05T10:49:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:536YR77EYMG27RPSYBCYHXJI5T","target":"record","payload":{"canonical_record":{"source":{"id":"2504.11026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-15T09:50:03Z","cross_cats_sorted":[],"title_canon_sha256":"0171d24f5b2964ef3d419975e67de110488235a4778ad476bec5ef0102eaa497","abstract_canon_sha256":"48e56b898038e11b180c633d298fcf906c07afbdf2da51a7c4b195261fe10f0f"},"schema_version":"1.0"},"canonical_sha256":"eefd88ffe4c30dafc5f2c04583dd28ecd7903d0970f2dec10a85a7ba3e231b72","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:28.031881Z","signature_b64":"VlsRlbOHpJWPOKtCoNmCnTqxYdtIuerXRSnS+Tn44554aCY9T3vLOzCFMtbKtDvCm/ly5JqNtfMFdOIeBHE6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eefd88ffe4c30dafc5f2c04583dd28ecd7903d0970f2dec10a85a7ba3e231b72","last_reissued_at":"2026-07-05T10:49:28.031399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:28.031399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.11026","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:49:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wdLSupt+99D7ATYpLIyyzN4tNT8ndd0OSo6VuveF0sVysTZZwexGkBi51HZKfs4Nwlq1jxOkDtT6HGokUJ8uAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:16:58.514210Z"},"content_sha256":"38b8f7f4188be95abb51ff316ba07c41d823ed047139c040b4530d762fd07009","schema_version":"1.0","event_id":"sha256:38b8f7f4188be95abb51ff316ba07c41d823ed047139c040b4530d762fd07009"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:536YR77EYMG27RPSYBCYHXJI5T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandru Vasilache, Florian Kaelber, Johannes Korsch, Jona Scholz, Juergen Becker, Sven Nitzsche, Vincent Schilling","submitted_at":"2025-04-15T09:50:03Z","abstract_excerpt":"Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than spike trains, necessitates the development of efficient encoding methods. This paper introduces a novel, open-source PyTorch-compatible Python framework for spike encoding, designed for neuromorphic applications in machine learning and reinforcement learning. The framework supports a range of encoding algorithms, including Leaky Integrate-and-Fire (LIF), Step"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11026","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/2504.11026/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":"2026-07-05T10:49:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VyUPhXMNZlMqYUnF1V93CQd63lLIkN89UJHBrLf/r5FedmpWpJcb46IJlRoKJfFSea4221e2K1RD1whiyagPDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T18:16:58.514709Z"},"content_sha256":"c97ab175ba5b965f35e2f536fe9506d6c53a1072e78ad37b493fac72ee4b925e","schema_version":"1.0","event_id":"sha256:c97ab175ba5b965f35e2f536fe9506d6c53a1072e78ad37b493fac72ee4b925e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/536YR77EYMG27RPSYBCYHXJI5T/bundle.json","state_url":"https://pith.science/pith/536YR77EYMG27RPSYBCYHXJI5T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/536YR77EYMG27RPSYBCYHXJI5T/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":"2026-08-06T18:16:58Z","links":{"resolver":"https://pith.science/pith/536YR77EYMG27RPSYBCYHXJI5T","bundle":"https://pith.science/pith/536YR77EYMG27RPSYBCYHXJI5T/bundle.json","state":"https://pith.science/pith/536YR77EYMG27RPSYBCYHXJI5T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/536YR77EYMG27RPSYBCYHXJI5T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:536YR77EYMG27RPSYBCYHXJI5T","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":"48e56b898038e11b180c633d298fcf906c07afbdf2da51a7c4b195261fe10f0f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-15T09:50:03Z","title_canon_sha256":"0171d24f5b2964ef3d419975e67de110488235a4778ad476bec5ef0102eaa497"},"schema_version":"1.0","source":{"id":"2504.11026","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.11026","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"arxiv_version","alias_value":"2504.11026v1","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11026","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_12","alias_value":"536YR77EYMG2","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_16","alias_value":"536YR77EYMG27RPS","created_at":"2026-07-05T10:49:28Z"},{"alias_kind":"pith_short_8","alias_value":"536YR77E","created_at":"2026-07-05T10:49:28Z"}],"graph_snapshots":[{"event_id":"sha256:c97ab175ba5b965f35e2f536fe9506d6c53a1072e78ad37b493fac72ee4b925e","target":"graph","created_at":"2026-07-05T10:49:28Z","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/2504.11026/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than spike trains, necessitates the development of efficient encoding methods. This paper introduces a novel, open-source PyTorch-compatible Python framework for spike encoding, designed for neuromorphic applications in machine learning and reinforcement learning. The framework supports a range of encoding algorithms, including Leaky Integrate-and-Fire (LIF), Step","authors_text":"Alexandru Vasilache, Florian Kaelber, Johannes Korsch, Jona Scholz, Juergen Becker, Sven Nitzsche, Vincent Schilling","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-15T09:50:03Z","title":"A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11026","kind":"arxiv","version":1},"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":"sha256:38b8f7f4188be95abb51ff316ba07c41d823ed047139c040b4530d762fd07009","target":"record","created_at":"2026-07-05T10:49:28Z","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":"48e56b898038e11b180c633d298fcf906c07afbdf2da51a7c4b195261fe10f0f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-15T09:50:03Z","title_canon_sha256":"0171d24f5b2964ef3d419975e67de110488235a4778ad476bec5ef0102eaa497"},"schema_version":"1.0","source":{"id":"2504.11026","kind":"arxiv","version":1}},"canonical_sha256":"eefd88ffe4c30dafc5f2c04583dd28ecd7903d0970f2dec10a85a7ba3e231b72","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eefd88ffe4c30dafc5f2c04583dd28ecd7903d0970f2dec10a85a7ba3e231b72","first_computed_at":"2026-07-05T10:49:28.031399Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:49:28.031399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VlsRlbOHpJWPOKtCoNmCnTqxYdtIuerXRSnS+Tn44554aCY9T3vLOzCFMtbKtDvCm/ly5JqNtfMFdOIeBHE6BA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:49:28.031881Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.11026","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38b8f7f4188be95abb51ff316ba07c41d823ed047139c040b4530d762fd07009","sha256:c97ab175ba5b965f35e2f536fe9506d6c53a1072e78ad37b493fac72ee4b925e"],"state_sha256":"1ffc374c4d17307cca7c0352a9ddc1f2cf891aab3b8e2184e96e085f174902f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3uR+OJ3RJYFff1Eb8ZbYPLEUsycs3SgPIO6Wk/Z632HN7Z3AhL0FGQc90SqcWBgUI7CFk2d91tDjLMSFVFb0Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T18:16:58.518858Z","bundle_sha256":"953389b99c60e178e9e05274198bdce53d87cbd832c939048310717274f1d8aa"}}