{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TY5DA25K5T5OCQLNBRCEJBSQWZ","short_pith_number":"pith:TY5DA25K","canonical_record":{"source":{"id":"2505.05520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T04:25:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"787c8c56cc6e397a311d461ba509672ad57e7fa620bc06c615c55cb03aaf5b90","abstract_canon_sha256":"0221b3ffa12e6994de243a6b0c797c4df011b7c2205ef138aa30304783582c76"},"schema_version":"1.0"},"canonical_sha256":"9e3a306baaecfae1416d0c44448650b648c2a25399551b540b9b08e6a56ce403","source":{"kind":"arxiv","id":"2505.05520","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.05520","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"arxiv_version","alias_value":"2505.05520v1","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05520","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_12","alias_value":"TY5DA25K5T5O","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_16","alias_value":"TY5DA25K5T5OCQLN","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_8","alias_value":"TY5DA25K","created_at":"2026-07-05T11:00:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TY5DA25K5T5OCQLNBRCEJBSQWZ","target":"record","payload":{"canonical_record":{"source":{"id":"2505.05520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T04:25:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"787c8c56cc6e397a311d461ba509672ad57e7fa620bc06c615c55cb03aaf5b90","abstract_canon_sha256":"0221b3ffa12e6994de243a6b0c797c4df011b7c2205ef138aa30304783582c76"},"schema_version":"1.0"},"canonical_sha256":"9e3a306baaecfae1416d0c44448650b648c2a25399551b540b9b08e6a56ce403","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:00:27.343472Z","signature_b64":"u6S4D5jBSZZKmg23cpYkQ99TULBCvtVlCZ6Zjf2a3lxC9mZS3LLQPJMVyhdrsv6BuBIP6QxBZq8OEPmkC0frAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e3a306baaecfae1416d0c44448650b648c2a25399551b540b9b08e6a56ce403","last_reissued_at":"2026-07-05T11:00:27.342854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:00:27.342854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.05520","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":"2026-07-05T11:00:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IbnfRJTGHNHzXpOse82x1iwhG/EffSuAHx7QYUCw6c6dKMLMW7yzN+kilm+iV4rYoLeiF3VVhV/K/YO7YbsfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T13:07:21.720302Z"},"content_sha256":"a14de6ff3a538a161891381a3495ca2a1bf197ea1769968b031fa6a65f4adb77","schema_version":"1.0","event_id":"sha256:a14de6ff3a538a161891381a3495ca2a1bf197ea1769968b031fa6a65f4adb77"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TY5DA25K5T5OCQLNBRCEJBSQWZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chengwei Ye, Huanzhen Zhang, Kangsheng Wang, Linuo Xu, Shuyan Liu, Yufei Lin","submitted_at":"2025-05-08T04:25:22Z","abstract_excerpt":"Gliomas are aggressive brain tumors that pose serious health risks. Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation, limiting real-time use on mobile medical devices. We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction. To improve interpretability and mimic the human visual system, we apply Gabor filters at multiple scales. Our method achieves high segmentation accuracy with fewer parameters and faster computation. Extensive exper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05520","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/2505.05520/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":"2026-07-05T11:00:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nchh/g44l3OOyxAxJyk6zxmpAiD9tIF/h85gYOkopzN97XDHHlFiB24KwikWtP40Ij7+LhZSkqzQVr1970riBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T13:07:21.720700Z"},"content_sha256":"09e043234fcbb4219bdcfa391c1cd3336c4f2a3b823e1b01f1b7b6ded679e342","schema_version":"1.0","event_id":"sha256:09e043234fcbb4219bdcfa391c1cd3336c4f2a3b823e1b01f1b7b6ded679e342"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/bundle.json","state_url":"https://pith.science/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/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":"2026-07-10T13:07:21Z","links":{"resolver":"https://pith.science/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ","bundle":"https://pith.science/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/bundle.json","state":"https://pith.science/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TY5DA25K5T5OCQLNBRCEJBSQWZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TY5DA25K5T5OCQLNBRCEJBSQWZ","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":"0221b3ffa12e6994de243a6b0c797c4df011b7c2205ef138aa30304783582c76","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T04:25:22Z","title_canon_sha256":"787c8c56cc6e397a311d461ba509672ad57e7fa620bc06c615c55cb03aaf5b90"},"schema_version":"1.0","source":{"id":"2505.05520","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.05520","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"arxiv_version","alias_value":"2505.05520v1","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.05520","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_12","alias_value":"TY5DA25K5T5O","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_16","alias_value":"TY5DA25K5T5OCQLN","created_at":"2026-07-05T11:00:27Z"},{"alias_kind":"pith_short_8","alias_value":"TY5DA25K","created_at":"2026-07-05T11:00:27Z"}],"graph_snapshots":[{"event_id":"sha256:09e043234fcbb4219bdcfa391c1cd3336c4f2a3b823e1b01f1b7b6ded679e342","target":"graph","created_at":"2026-07-05T11:00:27Z","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/2505.05520/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gliomas are aggressive brain tumors that pose serious health risks. Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation, limiting real-time use on mobile medical devices. We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction. To improve interpretability and mimic the human visual system, we apply Gabor filters at multiple scales. Our method achieves high segmentation accuracy with fewer parameters and faster computation. Extensive exper","authors_text":"Chengwei Ye, Huanzhen Zhang, Kangsheng Wang, Linuo Xu, Shuyan Liu, Yufei Lin","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T04:25:22Z","title":"GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.05520","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:a14de6ff3a538a161891381a3495ca2a1bf197ea1769968b031fa6a65f4adb77","target":"record","created_at":"2026-07-05T11:00:27Z","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":"0221b3ffa12e6994de243a6b0c797c4df011b7c2205ef138aa30304783582c76","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-08T04:25:22Z","title_canon_sha256":"787c8c56cc6e397a311d461ba509672ad57e7fa620bc06c615c55cb03aaf5b90"},"schema_version":"1.0","source":{"id":"2505.05520","kind":"arxiv","version":1}},"canonical_sha256":"9e3a306baaecfae1416d0c44448650b648c2a25399551b540b9b08e6a56ce403","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e3a306baaecfae1416d0c44448650b648c2a25399551b540b9b08e6a56ce403","first_computed_at":"2026-07-05T11:00:27.342854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:00:27.342854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u6S4D5jBSZZKmg23cpYkQ99TULBCvtVlCZ6Zjf2a3lxC9mZS3LLQPJMVyhdrsv6BuBIP6QxBZq8OEPmkC0frAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:00:27.343472Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.05520","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a14de6ff3a538a161891381a3495ca2a1bf197ea1769968b031fa6a65f4adb77","sha256:09e043234fcbb4219bdcfa391c1cd3336c4f2a3b823e1b01f1b7b6ded679e342"],"state_sha256":"c66e89f04d41527c885ac3a06361fa5b8edd83115ed7ea3c103dc86b2b8ab77a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cc3qI71blPgSvsUi13Ho81E6w0s47e7M9jFV6AAZAx/Tys15x0rBi6sLrbK9X+QOYlf3fo0sBfWkAPm4Me9TCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T13:07:21.722629Z","bundle_sha256":"e729827f3733e113920550c29dac0987a1e3a8f4eb1061ea3dd9ee0309826157"}}