{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AZECDTJUAQ2ODP44NLHSCBN2HA","short_pith_number":"pith:AZECDTJU","canonical_record":{"source":{"id":"2506.16098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-19T07:35:48Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"fa1edc079aad4ccbbd954073c8480ceed977cb7a31a5c64aa3b74f0fc0bcd6e2","abstract_canon_sha256":"3039ec9557ee859c7d26b4fdf436df5a10923b97b1ece995961620d956db7e6b"},"schema_version":"1.0"},"canonical_sha256":"064821cd340434e1bf9c6acf2105ba38316bb3d628d6e0d5f8e2fb1f2f839d83","source":{"kind":"arxiv","id":"2506.16098","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16098","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16098v1","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16098","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"AZECDTJUAQ2O","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"AZECDTJUAQ2ODP44","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"AZECDTJU","created_at":"2026-07-05T11:24:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AZECDTJUAQ2ODP44NLHSCBN2HA","target":"record","payload":{"canonical_record":{"source":{"id":"2506.16098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-19T07:35:48Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"fa1edc079aad4ccbbd954073c8480ceed977cb7a31a5c64aa3b74f0fc0bcd6e2","abstract_canon_sha256":"3039ec9557ee859c7d26b4fdf436df5a10923b97b1ece995961620d956db7e6b"},"schema_version":"1.0"},"canonical_sha256":"064821cd340434e1bf9c6acf2105ba38316bb3d628d6e0d5f8e2fb1f2f839d83","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:30.479744Z","signature_b64":"sIJPt2/6KZq0zS23xeqbQem5G+uA22eu4hoJRchoNzye38p/F29Asn2z9TAKiDfxWMF1cXIfar4aAAhjdY5bBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"064821cd340434e1bf9c6acf2105ba38316bb3d628d6e0d5f8e2fb1f2f839d83","last_reissued_at":"2026-07-05T11:24:30.479263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:30.479263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.16098","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":"2026-07-05T11:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2C48+PTmeht7x3LHGTugnERlJXP2nR8LfECKh54DeXiyWsWTtraV4tAzmjCgzxtmVofuWmVtnCeMh/W6vn6UAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:42:03.437733Z"},"content_sha256":"3dd92a6fc261069666aa553d6563e20db81a79cfa25ba71e4f7289b576a08382","schema_version":"1.0","event_id":"sha256:3dd92a6fc261069666aa553d6563e20db81a79cfa25ba71e4f7289b576a08382"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AZECDTJUAQ2ODP44NLHSCBN2HA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Laurent Schmalen, Shrinivas Chimmalgi, Vahid Aref","submitted_at":"2025-06-19T07:35:48Z","abstract_excerpt":"Probabilistic constellation shaping enables easy rate adaption and has been proven to reduce the gap to Shannon capacity. Constellation point probabilities are optimized to maximize either the mutual information or the bit-wise mutual information. The optimization problem is however challenging even for simple channel models. While autoencoder-based machine learning has been applied successfully to solve this problem [1], it requires manual computation of additional terms for the gradient which is an error-prone task. In this work, we present novel loss functions for autoencoder-based learning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16098","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/2506.16098/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":"2026-07-05T11:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v7XZlqvTym2t+hOO6OfRohh1s1a5SuuGm/T1WR77Ataxb8OU6ZngK1qO4RyQbATZDW4lHIe5oP72GuBGIL8ZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T02:42:03.438254Z"},"content_sha256":"c2b8bae794dcafea2b1b3106b03a005d58d7e0e3fc5650361ab1cad427254f1d","schema_version":"1.0","event_id":"sha256:c2b8bae794dcafea2b1b3106b03a005d58d7e0e3fc5650361ab1cad427254f1d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/bundle.json","state_url":"https://pith.science/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/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":"2026-08-16T02:42:03Z","links":{"resolver":"https://pith.science/pith/AZECDTJUAQ2ODP44NLHSCBN2HA","bundle":"https://pith.science/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/bundle.json","state":"https://pith.science/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AZECDTJUAQ2ODP44NLHSCBN2HA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AZECDTJUAQ2ODP44NLHSCBN2HA","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":"3039ec9557ee859c7d26b4fdf436df5a10923b97b1ece995961620d956db7e6b","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-19T07:35:48Z","title_canon_sha256":"fa1edc079aad4ccbbd954073c8480ceed977cb7a31a5c64aa3b74f0fc0bcd6e2"},"schema_version":"1.0","source":{"id":"2506.16098","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.16098","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2506.16098v1","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16098","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"AZECDTJUAQ2O","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"AZECDTJUAQ2ODP44","created_at":"2026-07-05T11:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"AZECDTJU","created_at":"2026-07-05T11:24:30Z"}],"graph_snapshots":[{"event_id":"sha256:c2b8bae794dcafea2b1b3106b03a005d58d7e0e3fc5650361ab1cad427254f1d","target":"graph","created_at":"2026-07-05T11:24:30Z","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/2506.16098/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic constellation shaping enables easy rate adaption and has been proven to reduce the gap to Shannon capacity. Constellation point probabilities are optimized to maximize either the mutual information or the bit-wise mutual information. The optimization problem is however challenging even for simple channel models. While autoencoder-based machine learning has been applied successfully to solve this problem [1], it requires manual computation of additional terms for the gradient which is an error-prone task. In this work, we present novel loss functions for autoencoder-based learning","authors_text":"Laurent Schmalen, Shrinivas Chimmalgi, Vahid Aref","cross_cats":["eess.SP","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-19T07:35:48Z","title":"End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16098","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":"sha256:3dd92a6fc261069666aa553d6563e20db81a79cfa25ba71e4f7289b576a08382","target":"record","created_at":"2026-07-05T11:24:30Z","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":"3039ec9557ee859c7d26b4fdf436df5a10923b97b1ece995961620d956db7e6b","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-06-19T07:35:48Z","title_canon_sha256":"fa1edc079aad4ccbbd954073c8480ceed977cb7a31a5c64aa3b74f0fc0bcd6e2"},"schema_version":"1.0","source":{"id":"2506.16098","kind":"arxiv","version":1}},"canonical_sha256":"064821cd340434e1bf9c6acf2105ba38316bb3d628d6e0d5f8e2fb1f2f839d83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"064821cd340434e1bf9c6acf2105ba38316bb3d628d6e0d5f8e2fb1f2f839d83","first_computed_at":"2026-07-05T11:24:30.479263Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:24:30.479263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sIJPt2/6KZq0zS23xeqbQem5G+uA22eu4hoJRchoNzye38p/F29Asn2z9TAKiDfxWMF1cXIfar4aAAhjdY5bBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:24:30.479744Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.16098","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3dd92a6fc261069666aa553d6563e20db81a79cfa25ba71e4f7289b576a08382","sha256:c2b8bae794dcafea2b1b3106b03a005d58d7e0e3fc5650361ab1cad427254f1d"],"state_sha256":"1192017674900576578d688466bc1ad857c1d94ba55e79149c124c4c7e7b2a8c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ENPD65+ymr4UBFxhsg+b6KlNlIKJsJ703W9eHJwf5AXvjIcm/Appz4Gd/QgemczvWohYrjBFmajEieHaImJqDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T02:42:03.444076Z","bundle_sha256":"82bb7c23b5ee755ed1beb67410b80e30dab6987f36b75dc380c3f876b785c6e5"}}