{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7F7HGTWTLVRTKSOR25MPVLEYVO","short_pith_number":"pith:7F7HGTWT","canonical_record":{"source":{"id":"1708.04682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-12T00:25:10Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"e4a328bf1030ca630eb53c6506dc697a34452fd29661330d904199557080b644","abstract_canon_sha256":"cfd503996083b1ad36416682c373b5f263b3538ab225971b6fa8921e3d6b9ab3"},"schema_version":"1.0"},"canonical_sha256":"f97e734ed35d633549d1d758faac98ab855dd4d4144464f9c6dd9fab97a965f2","source":{"kind":"arxiv","id":"1708.04682","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04682","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04682v1","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04682","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"7F7HGTWTLVRT","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7F7HGTWTLVRTKSOR","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7F7HGTWT","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7F7HGTWTLVRTKSOR25MPVLEYVO","target":"record","payload":{"canonical_record":{"source":{"id":"1708.04682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-12T00:25:10Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"e4a328bf1030ca630eb53c6506dc697a34452fd29661330d904199557080b644","abstract_canon_sha256":"cfd503996083b1ad36416682c373b5f263b3538ab225971b6fa8921e3d6b9ab3"},"schema_version":"1.0"},"canonical_sha256":"f97e734ed35d633549d1d758faac98ab855dd4d4144464f9c6dd9fab97a965f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:11.496968Z","signature_b64":"4JGJ9+2Bi123r3bQ6ep97aV34GGhQ5ZUY3j8APtwnHn09Obujkfc9ozqgST9bmC8Qsq9wHyL+9CgFBEO6+1rCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f97e734ed35d633549d1d758faac98ab855dd4d4144464f9c6dd9fab97a965f2","last_reissued_at":"2026-05-18T00:21:11.496566Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:11.496566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.04682","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":"2026-05-18T00:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MrG6jNIhLNt2RrLqwaVJwOtUvCS5fh71IunkLg2rl4v2KxblAV8xRHQY9h/Ho932W1ugg55zvSW8RmvoLlITBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:07:47.485909Z"},"content_sha256":"5e6d508c197d8fc8aa3353fdebcaa0dd576c994e61c7ae5d79f3814bfb456b23","schema_version":"1.0","event_id":"sha256:5e6d508c197d8fc8aa3353fdebcaa0dd576c994e61c7ae5d79f3814bfb456b23"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7F7HGTWTLVRTKSOR25MPVLEYVO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning for Passive Synthetic Aperture Radar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.CV","authors_text":"Bariscan Yonel, Birsen Yaz{\\i}c{\\i}, Eric Mason","submitted_at":"2017-08-12T00:25:10Z","abstract_excerpt":"We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image recon- struction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods. We further adapt the RNN architecture to image reconstruction problems by transforming the net"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04682","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":""},"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":"2026-05-18T00:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0jqwygq9CehlNzR0JKxYfUoQvuDb1F4aSFwHPBPZ3FOiDnidmeULDDlc8KiiLrLGA2OLnykqK/RRzN9KmJMDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:07:47.486259Z"},"content_sha256":"046fc74952acbd2ce2d20e858fa112214d6ade55034d43aad8e3dd124b926b59","schema_version":"1.0","event_id":"sha256:046fc74952acbd2ce2d20e858fa112214d6ade55034d43aad8e3dd124b926b59"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/bundle.json","state_url":"https://pith.science/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/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":"2026-05-30T13:07:47Z","links":{"resolver":"https://pith.science/pith/7F7HGTWTLVRTKSOR25MPVLEYVO","bundle":"https://pith.science/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/bundle.json","state":"https://pith.science/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7F7HGTWTLVRTKSOR25MPVLEYVO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7F7HGTWTLVRTKSOR25MPVLEYVO","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":"cfd503996083b1ad36416682c373b5f263b3538ab225971b6fa8921e3d6b9ab3","cross_cats_sorted":["cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-12T00:25:10Z","title_canon_sha256":"e4a328bf1030ca630eb53c6506dc697a34452fd29661330d904199557080b644"},"schema_version":"1.0","source":{"id":"1708.04682","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04682","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04682v1","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04682","created_at":"2026-05-18T00:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"7F7HGTWTLVRT","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7F7HGTWTLVRTKSOR","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7F7HGTWT","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:046fc74952acbd2ce2d20e858fa112214d6ade55034d43aad8e3dd124b926b59","target":"graph","created_at":"2026-05-18T00:21:11Z","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"},"paper":{"abstract_excerpt":"We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image recon- struction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods. We further adapt the RNN architecture to image reconstruction problems by transforming the net","authors_text":"Bariscan Yonel, Birsen Yaz{\\i}c{\\i}, Eric Mason","cross_cats":["cs.CE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-12T00:25:10Z","title":"Deep Learning for Passive Synthetic Aperture Radar"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04682","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":"sha256:5e6d508c197d8fc8aa3353fdebcaa0dd576c994e61c7ae5d79f3814bfb456b23","target":"record","created_at":"2026-05-18T00:21:11Z","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":"cfd503996083b1ad36416682c373b5f263b3538ab225971b6fa8921e3d6b9ab3","cross_cats_sorted":["cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-12T00:25:10Z","title_canon_sha256":"e4a328bf1030ca630eb53c6506dc697a34452fd29661330d904199557080b644"},"schema_version":"1.0","source":{"id":"1708.04682","kind":"arxiv","version":1}},"canonical_sha256":"f97e734ed35d633549d1d758faac98ab855dd4d4144464f9c6dd9fab97a965f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f97e734ed35d633549d1d758faac98ab855dd4d4144464f9c6dd9fab97a965f2","first_computed_at":"2026-05-18T00:21:11.496566Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:11.496566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4JGJ9+2Bi123r3bQ6ep97aV34GGhQ5ZUY3j8APtwnHn09Obujkfc9ozqgST9bmC8Qsq9wHyL+9CgFBEO6+1rCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:11.496968Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.04682","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e6d508c197d8fc8aa3353fdebcaa0dd576c994e61c7ae5d79f3814bfb456b23","sha256:046fc74952acbd2ce2d20e858fa112214d6ade55034d43aad8e3dd124b926b59"],"state_sha256":"e16f4c0f125293a68731cce34fb12ef02cb8e7401d7980f1f5171f8818afb293"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OHviVuPJBKQJ24M153YLQs32NR0rsLsUQomPA5kovZtLTyzv6OkjlA0YjKyJH2p+wr9G9lu19lpSM/X57j7XAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T13:07:47.488637Z","bundle_sha256":"9e83678231cb6c7a31a48d14266a1b8dd969233b4276efa195978d9ecc223aa5"}}