{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:XFPZ5XB6VMPGPNKB3AXXYOOZT2","short_pith_number":"pith:XFPZ5XB6","canonical_record":{"source":{"id":"1810.07960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T09:21:44Z","cross_cats_sorted":[],"title_canon_sha256":"fa1dfafa00b05f06a513d38fb6c48b30b26e9bc7b768f7d5b9037682fefadf57","abstract_canon_sha256":"7ef434be513219454b51b07dbfbf5f6ede6b7a371cce36845cf294837606a959"},"schema_version":"1.0"},"canonical_sha256":"b95f9edc3eab1e67b541d82f7c39d99e9b830cff6375ed3d0b06d97a370853bf","source":{"kind":"arxiv","id":"1810.07960","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.07960","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"arxiv_version","alias_value":"1810.07960v1","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07960","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"pith_short_12","alias_value":"XFPZ5XB6VMPG","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XFPZ5XB6VMPGPNKB","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XFPZ5XB6","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:XFPZ5XB6VMPGPNKB3AXXYOOZT2","target":"record","payload":{"canonical_record":{"source":{"id":"1810.07960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T09:21:44Z","cross_cats_sorted":[],"title_canon_sha256":"fa1dfafa00b05f06a513d38fb6c48b30b26e9bc7b768f7d5b9037682fefadf57","abstract_canon_sha256":"7ef434be513219454b51b07dbfbf5f6ede6b7a371cce36845cf294837606a959"},"schema_version":"1.0"},"canonical_sha256":"b95f9edc3eab1e67b541d82f7c39d99e9b830cff6375ed3d0b06d97a370853bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:58.069573Z","signature_b64":"wXCQqUC9YSzHFVjZU1MtQbApRdNHbkbQ8/HKr1aLjVh4eIbFUFlNJLIbyDq7TJqkyJIzavo3yUd/lwn5S/8uDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b95f9edc3eab1e67b541d82f7c39d99e9b830cff6375ed3d0b06d97a370853bf","last_reissued_at":"2026-05-17T23:39:58.069085Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:58.069085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.07960","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":"2026-05-17T23:39:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1y/MRaWNfXnmtvaZeBRBJrAY5ofo6TQUQBGl3LQZow7lbL4e25urwrhb4RUStQyvpIIKUN2dvcK6ld4qQVhICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:57:20.838664Z"},"content_sha256":"8e221b4309e7a03f5c3f5a9ca7a16d483dfb698f6f96a69e4e3fd9f8f35e5cf0","schema_version":"1.0","event_id":"sha256:8e221b4309e7a03f5c3f5a9ca7a16d483dfb698f6f96a69e4e3fd9f8f35e5cf0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:XFPZ5XB6VMPGPNKB3AXXYOOZT2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bolun Zheng, Rui Sun, Xiang Tian, Yaowu Chen","submitted_at":"2018-10-18T09:21:44Z","abstract_excerpt":"Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07960","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":"2026-05-17T23:39:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QCNHMyVMLSCqvBAtFNjygoV4UNha2NSaHITxD9K0g1ajusAMOq4Wl39QHLAX4mopOnxqulKsAEChdYcCiCvrCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:57:20.839419Z"},"content_sha256":"9b1bc8d6655c7b91bc836e293cd5972fa9c2b126693d587b54521402e8e2c7fa","schema_version":"1.0","event_id":"sha256:9b1bc8d6655c7b91bc836e293cd5972fa9c2b126693d587b54521402e8e2c7fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/bundle.json","state_url":"https://pith.science/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/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":"2026-05-27T11:57:20Z","links":{"resolver":"https://pith.science/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2","bundle":"https://pith.science/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/bundle.json","state":"https://pith.science/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XFPZ5XB6VMPGPNKB3AXXYOOZT2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:XFPZ5XB6VMPGPNKB3AXXYOOZT2","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":"7ef434be513219454b51b07dbfbf5f6ede6b7a371cce36845cf294837606a959","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T09:21:44Z","title_canon_sha256":"fa1dfafa00b05f06a513d38fb6c48b30b26e9bc7b768f7d5b9037682fefadf57"},"schema_version":"1.0","source":{"id":"1810.07960","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.07960","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"arxiv_version","alias_value":"1810.07960v1","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07960","created_at":"2026-05-17T23:39:58Z"},{"alias_kind":"pith_short_12","alias_value":"XFPZ5XB6VMPG","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XFPZ5XB6VMPGPNKB","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XFPZ5XB6","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:9b1bc8d6655c7b91bc836e293cd5972fa9c2b126693d587b54521402e8e2c7fa","target":"graph","created_at":"2026-05-17T23:39:58Z","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":"Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an","authors_text":"Bolun Zheng, Rui Sun, Xiang Tian, Yaowu Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T09:21:44Z","title":"S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07960","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":"sha256:8e221b4309e7a03f5c3f5a9ca7a16d483dfb698f6f96a69e4e3fd9f8f35e5cf0","target":"record","created_at":"2026-05-17T23:39:58Z","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":"7ef434be513219454b51b07dbfbf5f6ede6b7a371cce36845cf294837606a959","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-18T09:21:44Z","title_canon_sha256":"fa1dfafa00b05f06a513d38fb6c48b30b26e9bc7b768f7d5b9037682fefadf57"},"schema_version":"1.0","source":{"id":"1810.07960","kind":"arxiv","version":1}},"canonical_sha256":"b95f9edc3eab1e67b541d82f7c39d99e9b830cff6375ed3d0b06d97a370853bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b95f9edc3eab1e67b541d82f7c39d99e9b830cff6375ed3d0b06d97a370853bf","first_computed_at":"2026-05-17T23:39:58.069085Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:58.069085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wXCQqUC9YSzHFVjZU1MtQbApRdNHbkbQ8/HKr1aLjVh4eIbFUFlNJLIbyDq7TJqkyJIzavo3yUd/lwn5S/8uDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:58.069573Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.07960","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e221b4309e7a03f5c3f5a9ca7a16d483dfb698f6f96a69e4e3fd9f8f35e5cf0","sha256:9b1bc8d6655c7b91bc836e293cd5972fa9c2b126693d587b54521402e8e2c7fa"],"state_sha256":"4633ed98353afffe10645546bae413ed9f626e3e37fb8df3fbf6b138ec2ad5a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"egHsmz3TqnR09Y0UqbtCRdNQZ4EDQ0EPuc4GqZsPM0cFnBLppk6NLdRSL8XzPOIQ7Ktf8Tz48BQw/dEUBnvACw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:57:20.843161Z","bundle_sha256":"1cf9079c448e6c3e91fd125051acd2e78ad1b9654374f902139a0b8f9cf6be44"}}