{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:XXWSOFUU6LCI2GGQKPV3KL3TI4","short_pith_number":"pith:XXWSOFUU","canonical_record":{"source":{"id":"2006.08896","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-06-16T03:14:30Z","cross_cats_sorted":[],"title_canon_sha256":"19a253bf25f3737bacfbed929107b58846189081fca114c615c232e30f210400","abstract_canon_sha256":"0e7ede7a61a7810e283ef5b32abd5320823fda8fc76134a9831b2b73f939155c"},"schema_version":"1.0"},"canonical_sha256":"bded271694f2c48d18d053ebb52f734717f55eb95f67f3e138a6b25334513a55","source":{"kind":"arxiv","id":"2006.08896","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.08896","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"2006.08896v1","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08896","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"XXWSOFUU6LCI","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_16","alias_value":"XXWSOFUU6LCI2GGQ","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_8","alias_value":"XXWSOFUU","created_at":"2026-07-05T01:10:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:XXWSOFUU6LCI2GGQKPV3KL3TI4","target":"record","payload":{"canonical_record":{"source":{"id":"2006.08896","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-06-16T03:14:30Z","cross_cats_sorted":[],"title_canon_sha256":"19a253bf25f3737bacfbed929107b58846189081fca114c615c232e30f210400","abstract_canon_sha256":"0e7ede7a61a7810e283ef5b32abd5320823fda8fc76134a9831b2b73f939155c"},"schema_version":"1.0"},"canonical_sha256":"bded271694f2c48d18d053ebb52f734717f55eb95f67f3e138a6b25334513a55","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:10:44.231631Z","signature_b64":"9Gm0yMqIpOs180HrQZADxttNJAlBZZapFPoWtUcSMGsBkKMsudtRGIIb4F1EhmyqfWwR1M5SrDf1SwNsD5P2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bded271694f2c48d18d053ebb52f734717f55eb95f67f3e138a6b25334513a55","last_reissued_at":"2026-07-05T01:10:44.231244Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:10:44.231244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.08896","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":"2026-07-05T01:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Pg/Uk2C7Cu0mVvDrNqaz99kcOjH5JsNduLlKQG+iPGB+cRml4J6T9IUGiA/yy4ify+IT6Ba59gLUWgPdQp0BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:14:06.910713Z"},"content_sha256":"762031b21ab913291d6ec5e8d722fc4316b3a22bfcf1c01efa1ea3343ae0d9cc","schema_version":"1.0","event_id":"sha256:762031b21ab913291d6ec5e8d722fc4316b3a22bfcf1c01efa1ea3343ae0d9cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:XXWSOFUU6LCI2GGQKPV3KL3TI4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Chao-Kai Wen, Geoffrey Ye Li, Jing Zhang, Shi Jin, Yunfeng He","submitted_at":"2020-06-16T03:14:30Z","abstract_excerpt":"This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently trai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08896","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/2006.08896/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":"2026-07-05T01:10:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hJyjeRCJWe4kQLyxkK4/hCUhhBqI3BozAJ/N/bjOJ3WomsqGdOTNm/FBsX6a07y8AkQorodgUYw7gyw2eh2WDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:14:06.911586Z"},"content_sha256":"f5ee8e4d7c05030c134a6a718a1e8f201ec2dfdcc051d4f6e6052d9809206872","schema_version":"1.0","event_id":"sha256:f5ee8e4d7c05030c134a6a718a1e8f201ec2dfdcc051d4f6e6052d9809206872"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/bundle.json","state_url":"https://pith.science/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/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":"2026-08-05T16:14:06Z","links":{"resolver":"https://pith.science/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4","bundle":"https://pith.science/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/bundle.json","state":"https://pith.science/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXWSOFUU6LCI2GGQKPV3KL3TI4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:XXWSOFUU6LCI2GGQKPV3KL3TI4","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":"0e7ede7a61a7810e283ef5b32abd5320823fda8fc76134a9831b2b73f939155c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-06-16T03:14:30Z","title_canon_sha256":"19a253bf25f3737bacfbed929107b58846189081fca114c615c232e30f210400"},"schema_version":"1.0","source":{"id":"2006.08896","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.08896","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"arxiv_version","alias_value":"2006.08896v1","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08896","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_12","alias_value":"XXWSOFUU6LCI","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_16","alias_value":"XXWSOFUU6LCI2GGQ","created_at":"2026-07-05T01:10:44Z"},{"alias_kind":"pith_short_8","alias_value":"XXWSOFUU","created_at":"2026-07-05T01:10:44Z"}],"graph_snapshots":[{"event_id":"sha256:f5ee8e4d7c05030c134a6a718a1e8f201ec2dfdcc051d4f6e6052d9809206872","target":"graph","created_at":"2026-07-05T01:10:44Z","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/2006.08896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently trai","authors_text":"Chao-Kai Wen, Geoffrey Ye Li, Jing Zhang, Shi Jin, Yunfeng He","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-06-16T03:14:30Z","title":"Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08896","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":"sha256:762031b21ab913291d6ec5e8d722fc4316b3a22bfcf1c01efa1ea3343ae0d9cc","target":"record","created_at":"2026-07-05T01:10:44Z","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":"0e7ede7a61a7810e283ef5b32abd5320823fda8fc76134a9831b2b73f939155c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-06-16T03:14:30Z","title_canon_sha256":"19a253bf25f3737bacfbed929107b58846189081fca114c615c232e30f210400"},"schema_version":"1.0","source":{"id":"2006.08896","kind":"arxiv","version":1}},"canonical_sha256":"bded271694f2c48d18d053ebb52f734717f55eb95f67f3e138a6b25334513a55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bded271694f2c48d18d053ebb52f734717f55eb95f67f3e138a6b25334513a55","first_computed_at":"2026-07-05T01:10:44.231244Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:10:44.231244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Gm0yMqIpOs180HrQZADxttNJAlBZZapFPoWtUcSMGsBkKMsudtRGIIb4F1EhmyqfWwR1M5SrDf1SwNsD5P2Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:10:44.231631Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.08896","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:762031b21ab913291d6ec5e8d722fc4316b3a22bfcf1c01efa1ea3343ae0d9cc","sha256:f5ee8e4d7c05030c134a6a718a1e8f201ec2dfdcc051d4f6e6052d9809206872"],"state_sha256":"e1ed98b9960c0d2ce5e814093096acea1849dfb559d30b3523227c41115470be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B8spA7DvqVyOdl2VYAXGEhDDkmQ5tXMLoTV4mpcpAHKLgoREe/80r5acY54DH6iwKD9SkfZyq7wI1EVRtPQlAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:14:06.917944Z","bundle_sha256":"61a2eeee6eed303160dc81477462893f72caa1c4911ba8d3a3d007c2ff006d22"}}