{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YUKBCQRWQGCDNLF735TBAANHXA","short_pith_number":"pith:YUKBCQRW","canonical_record":{"source":{"id":"2606.06723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-04T21:14:25Z","cross_cats_sorted":[],"title_canon_sha256":"ecd1e17e92ff33a10a5c1c0812072bb10a5c4cf9c6c1f6b03ff3fa21ed10e62f","abstract_canon_sha256":"d7fb4e5a9321119072af584cacd3f7929ed49ed0fe9df6c1295c88020aa8c126"},"schema_version":"1.0"},"canonical_sha256":"c514114236818436acbfdf661001a7b8388f285106f29acf5e20ec586ec6138b","source":{"kind":"arxiv","id":"2606.06723","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.06723","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2606.06723v1","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06723","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"YUKBCQRWQGCD","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"YUKBCQRWQGCDNLF7","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"YUKBCQRW","created_at":"2026-06-08T01:04:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YUKBCQRWQGCDNLF735TBAANHXA","target":"record","payload":{"canonical_record":{"source":{"id":"2606.06723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-04T21:14:25Z","cross_cats_sorted":[],"title_canon_sha256":"ecd1e17e92ff33a10a5c1c0812072bb10a5c4cf9c6c1f6b03ff3fa21ed10e62f","abstract_canon_sha256":"d7fb4e5a9321119072af584cacd3f7929ed49ed0fe9df6c1295c88020aa8c126"},"schema_version":"1.0"},"canonical_sha256":"c514114236818436acbfdf661001a7b8388f285106f29acf5e20ec586ec6138b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:24.658963Z","signature_b64":"yK6ATkmSV4robIzgQp3fib/qu6iTG2gilKaTn6CjGG+udq3Ei2sPpPD2Frt787QDB56g9mkU0JP0RZijB1m7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c514114236818436acbfdf661001a7b8388f285106f29acf5e20ec586ec6138b","last_reissued_at":"2026-06-08T01:04:24.658200Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:24.658200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.06723","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":"2026-06-08T01:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZY0dc6k/vrs3zeVvPTAiThIM3XpteD8lVmnzkulRpTiNfZGJ3CedQ09orrqI3jO6OlatKd9biC1zkFAaiDRtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:10:26.723520Z"},"content_sha256":"fd2ec61c7a2131db7a775bf542eb6f5f607957596d06ed021961bf3fb62e6d16","schema_version":"1.0","event_id":"sha256:fd2ec61c7a2131db7a775bf542eb6f5f607957596d06ed021961bf3fb62e6d16"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YUKBCQRWQGCDNLF735TBAANHXA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning Based Sparse Array Design with Pre-Steering for Adaptive Beamforming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ian Straub, Syed A Hamza","submitted_at":"2026-06-04T21:14:25Z","abstract_excerpt":"This paper investigates the use of convolutional neural networks (CNNs) for learning sparse array configurations that achieve near-optimal beamforming under varying source and interference angles. Unlike conventional or convex optimization based algorithms, the proposed deep learning approach enables rapid reconfiguration of sparse arrays in highly dynamic propagation environments. The paper considers a single desired source and a single interference signal at arbitrary angles, analyzing scenarios with both fixed and varying desired source directions. To avoid retraining for each possible sour"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06723","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/2606.06723/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":"2026-06-08T01:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dhsRLdpYmlqb7w0g7PE0gEH0M4cWjcmhQJ8SqOxV4MaZy2+kZA4rV29IpF1F4i6nYH4SsP+OvR5iYVTQek1VCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:10:26.724284Z"},"content_sha256":"14e61cb5168d305e8f87cb362cd4cd749d711fe221667a157e915e5a1a08fc00","schema_version":"1.0","event_id":"sha256:14e61cb5168d305e8f87cb362cd4cd749d711fe221667a157e915e5a1a08fc00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YUKBCQRWQGCDNLF735TBAANHXA/bundle.json","state_url":"https://pith.science/pith/YUKBCQRWQGCDNLF735TBAANHXA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YUKBCQRWQGCDNLF735TBAANHXA/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":"2026-08-03T16:10:26Z","links":{"resolver":"https://pith.science/pith/YUKBCQRWQGCDNLF735TBAANHXA","bundle":"https://pith.science/pith/YUKBCQRWQGCDNLF735TBAANHXA/bundle.json","state":"https://pith.science/pith/YUKBCQRWQGCDNLF735TBAANHXA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YUKBCQRWQGCDNLF735TBAANHXA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YUKBCQRWQGCDNLF735TBAANHXA","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":"d7fb4e5a9321119072af584cacd3f7929ed49ed0fe9df6c1295c88020aa8c126","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-04T21:14:25Z","title_canon_sha256":"ecd1e17e92ff33a10a5c1c0812072bb10a5c4cf9c6c1f6b03ff3fa21ed10e62f"},"schema_version":"1.0","source":{"id":"2606.06723","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.06723","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2606.06723v1","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06723","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"YUKBCQRWQGCD","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"YUKBCQRWQGCDNLF7","created_at":"2026-06-08T01:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"YUKBCQRW","created_at":"2026-06-08T01:04:24Z"}],"graph_snapshots":[{"event_id":"sha256:14e61cb5168d305e8f87cb362cd4cd749d711fe221667a157e915e5a1a08fc00","target":"graph","created_at":"2026-06-08T01:04:24Z","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/2606.06723/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper investigates the use of convolutional neural networks (CNNs) for learning sparse array configurations that achieve near-optimal beamforming under varying source and interference angles. Unlike conventional or convex optimization based algorithms, the proposed deep learning approach enables rapid reconfiguration of sparse arrays in highly dynamic propagation environments. The paper considers a single desired source and a single interference signal at arbitrary angles, analyzing scenarios with both fixed and varying desired source directions. To avoid retraining for each possible sour","authors_text":"Ian Straub, Syed A Hamza","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-04T21:14:25Z","title":"Deep Learning Based Sparse Array Design with Pre-Steering for Adaptive Beamforming"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06723","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:fd2ec61c7a2131db7a775bf542eb6f5f607957596d06ed021961bf3fb62e6d16","target":"record","created_at":"2026-06-08T01:04:24Z","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":"d7fb4e5a9321119072af584cacd3f7929ed49ed0fe9df6c1295c88020aa8c126","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-06-04T21:14:25Z","title_canon_sha256":"ecd1e17e92ff33a10a5c1c0812072bb10a5c4cf9c6c1f6b03ff3fa21ed10e62f"},"schema_version":"1.0","source":{"id":"2606.06723","kind":"arxiv","version":1}},"canonical_sha256":"c514114236818436acbfdf661001a7b8388f285106f29acf5e20ec586ec6138b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c514114236818436acbfdf661001a7b8388f285106f29acf5e20ec586ec6138b","first_computed_at":"2026-06-08T01:04:24.658200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:24.658200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yK6ATkmSV4robIzgQp3fib/qu6iTG2gilKaTn6CjGG+udq3Ei2sPpPD2Frt787QDB56g9mkU0JP0RZijB1m7BA==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:24.658963Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.06723","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd2ec61c7a2131db7a775bf542eb6f5f607957596d06ed021961bf3fb62e6d16","sha256:14e61cb5168d305e8f87cb362cd4cd749d711fe221667a157e915e5a1a08fc00"],"state_sha256":"18753bbfeaa46c3c60789cf2f98ebf9d7673e11ca990b771fac893a666ff908e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/G0P0yitw3R4jpGnMp7tX38lJlGCnvci51z2cZbTQZ4M6+JMKKwqS2B/8HsgxHE/eoZXkFqeb6nGiR4g1fL1Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:10:26.729890Z","bundle_sha256":"ffee572f3034d96b4c45b27341dc35dd19bed2a82402cd03c8bd62ebcc5016d7"}}