{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IU5YBDUPGQ7HK3RZN2OGUNHUP3","short_pith_number":"pith:IU5YBDUP","canonical_record":{"source":{"id":"1810.08229","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T18:37:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"928dfc46a1b5d92584e6f2d9140fdbb182c077eb1a06fcad80d8dd38b526cef0","abstract_canon_sha256":"7b2392901de0b3f003018e79767042a083a8f2f60d742e2e7fe5f783e533f1ad"},"schema_version":"1.0"},"canonical_sha256":"453b808e8f343e756e396e9c6a34f47eeec95b1806af6efe4f673037fd1872ae","source":{"kind":"arxiv","id":"1810.08229","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08229","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08229v2","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08229","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"IU5YBDUPGQ7H","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IU5YBDUPGQ7HK3RZ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IU5YBDUP","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IU5YBDUPGQ7HK3RZN2OGUNHUP3","target":"record","payload":{"canonical_record":{"source":{"id":"1810.08229","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T18:37:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"928dfc46a1b5d92584e6f2d9140fdbb182c077eb1a06fcad80d8dd38b526cef0","abstract_canon_sha256":"7b2392901de0b3f003018e79767042a083a8f2f60d742e2e7fe5f783e533f1ad"},"schema_version":"1.0"},"canonical_sha256":"453b808e8f343e756e396e9c6a34f47eeec95b1806af6efe4f673037fd1872ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:58.006866Z","signature_b64":"UcWugGBChqP+yuqHQJNRXWnlR1Va7LYC19heTnlA26FtXvBo1bV8adzRT1kMy3RsZmje1zisV3ZsNReYPYspAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"453b808e8f343e756e396e9c6a34f47eeec95b1806af6efe4f673037fd1872ae","last_reissued_at":"2026-05-17T23:48:58.006194Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:58.006194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.08229","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wbMW5gr5J3nUbPsgvoU3uLwdFf1HDz22PM4QdH/XjZyZIxPJHQoG1ruw1TtPMiuHFSHBRTWQaAVfiYwzfVUzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:19:20.176756Z"},"content_sha256":"62445970e953a3cf16d3bd0510fa96a489f5bce2117b14c4d0830688a9841eb0","schema_version":"1.0","event_id":"sha256:62445970e953a3cf16d3bd0510fa96a489f5bce2117b14c4d0830688a9841eb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IU5YBDUPGQ7HK3RZN2OGUNHUP3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MRI Reconstruction via Cascaded Channel-wise Attention Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dimitris Metaxas, Dong Yang, Hui Qu, Jingru Yi, Pengxiang Wu, Qiaoying Huang","submitted_at":"2018-10-18T18:37:37Z","abstract_excerpt":"We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dominate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise features equally, which results in degraded representation ability of the neu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08229","kind":"arxiv","version":2},"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":"2026-05-17T23:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6Ky2/FO3oswxQ5KaPrOeuZ9cpUDrqzEBy2L2APtWRf0jZQ2hAOyQa7sqfnInIcuuK31YuiKoMa/lDt10VtjkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:19:20.177228Z"},"content_sha256":"3415cd04671f31a38a93fd8897ecd6fad1e9fd2eaff18a28d571ded7c9b2c2e7","schema_version":"1.0","event_id":"sha256:3415cd04671f31a38a93fd8897ecd6fad1e9fd2eaff18a28d571ded7c9b2c2e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/bundle.json","state_url":"https://pith.science/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/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":"2026-05-30T16:19:20Z","links":{"resolver":"https://pith.science/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3","bundle":"https://pith.science/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/bundle.json","state":"https://pith.science/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IU5YBDUPGQ7HK3RZN2OGUNHUP3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IU5YBDUPGQ7HK3RZN2OGUNHUP3","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":"7b2392901de0b3f003018e79767042a083a8f2f60d742e2e7fe5f783e533f1ad","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T18:37:37Z","title_canon_sha256":"928dfc46a1b5d92584e6f2d9140fdbb182c077eb1a06fcad80d8dd38b526cef0"},"schema_version":"1.0","source":{"id":"1810.08229","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08229","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08229v2","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08229","created_at":"2026-05-17T23:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"IU5YBDUPGQ7H","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IU5YBDUPGQ7HK3RZ","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IU5YBDUP","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:3415cd04671f31a38a93fd8897ecd6fad1e9fd2eaff18a28d571ded7c9b2c2e7","target":"graph","created_at":"2026-05-17T23:48:58Z","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 consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dominate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise features equally, which results in degraded representation ability of the neu","authors_text":"Dimitris Metaxas, Dong Yang, Hui Qu, Jingru Yi, Pengxiang Wu, Qiaoying Huang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T18:37:37Z","title":"MRI Reconstruction via Cascaded Channel-wise Attention Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08229","kind":"arxiv","version":2},"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":"sha256:62445970e953a3cf16d3bd0510fa96a489f5bce2117b14c4d0830688a9841eb0","target":"record","created_at":"2026-05-17T23:48:58Z","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":"7b2392901de0b3f003018e79767042a083a8f2f60d742e2e7fe5f783e533f1ad","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T18:37:37Z","title_canon_sha256":"928dfc46a1b5d92584e6f2d9140fdbb182c077eb1a06fcad80d8dd38b526cef0"},"schema_version":"1.0","source":{"id":"1810.08229","kind":"arxiv","version":2}},"canonical_sha256":"453b808e8f343e756e396e9c6a34f47eeec95b1806af6efe4f673037fd1872ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"453b808e8f343e756e396e9c6a34f47eeec95b1806af6efe4f673037fd1872ae","first_computed_at":"2026-05-17T23:48:58.006194Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:58.006194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UcWugGBChqP+yuqHQJNRXWnlR1Va7LYC19heTnlA26FtXvBo1bV8adzRT1kMy3RsZmje1zisV3ZsNReYPYspAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:58.006866Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.08229","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62445970e953a3cf16d3bd0510fa96a489f5bce2117b14c4d0830688a9841eb0","sha256:3415cd04671f31a38a93fd8897ecd6fad1e9fd2eaff18a28d571ded7c9b2c2e7"],"state_sha256":"9bf3a441c1512fcbe55af81bf618810c5f01fd4c46ed213035fdebc88c96d826"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"64K2tI4UkmTdWrpN4Ixr8fr5BcJ36sSSaddoZY48BGPjtGYr02yD5iV8jqVfFDUOq0hqJfnaxV36F1iBZi9mAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T16:19:20.180397Z","bundle_sha256":"453d97b80bc682ae64f7c6a55d2fc8ee532dda0fec63c829c770544017d7fae9"}}