{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:WFPQODD4FUEO54FUQ3ZQ2DDFYI","short_pith_number":"pith:WFPQODD4","canonical_record":{"source":{"id":"1706.06972","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T15:50:12Z","cross_cats_sorted":[],"title_canon_sha256":"4df030bcdd3371ce5b690b8dce8e7d03f8de54f2b01daf98615c698405343d27","abstract_canon_sha256":"c5eb619b6276d92fbe49bf1868170c51b5cd3183bbc8583c1d6b16c542c0ca34"},"schema_version":"1.0"},"canonical_sha256":"b15f070c7c2d08eef0b486f30d0c65c20b504ff93355324ab2d715484e7c0f7c","source":{"kind":"arxiv","id":"1706.06972","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.06972","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"arxiv_version","alias_value":"1706.06972v3","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06972","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"pith_short_12","alias_value":"WFPQODD4FUEO","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WFPQODD4FUEO54FU","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WFPQODD4","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:WFPQODD4FUEO54FUQ3ZQ2DDFYI","target":"record","payload":{"canonical_record":{"source":{"id":"1706.06972","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T15:50:12Z","cross_cats_sorted":[],"title_canon_sha256":"4df030bcdd3371ce5b690b8dce8e7d03f8de54f2b01daf98615c698405343d27","abstract_canon_sha256":"c5eb619b6276d92fbe49bf1868170c51b5cd3183bbc8583c1d6b16c542c0ca34"},"schema_version":"1.0"},"canonical_sha256":"b15f070c7c2d08eef0b486f30d0c65c20b504ff93355324ab2d715484e7c0f7c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:20.317027Z","signature_b64":"bYr2DaWIy7Tk7v73OTfYBv5Igf/KN7BRIiUdxmGPAaBVs9J5pnIeu5ukaUcJShRk7smq9QDsEpQLnCm80QODAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b15f070c7c2d08eef0b486f30d0c65c20b504ff93355324ab2d715484e7c0f7c","last_reissued_at":"2026-05-18T00:09:20.316456Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:20.316456Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.06972","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bXySF30zByT3530Q/3+Ik6YiqbHdGYbtJL6hbIpLG0p0THL1bHosTXyS+ZMXobg5D5rDIR5dH/8gdSDye/mWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T20:02:36.257626Z"},"content_sha256":"ea35cac568b91e334246498cdd50f41d29cbc4a8c8b09abe4b98e426a34ff424","schema_version":"1.0","event_id":"sha256:ea35cac568b91e334246498cdd50f41d29cbc4a8c8b09abe4b98e426a34ff424"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:WFPQODD4FUEO54FUQ3ZQ2DDFYI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Online Convolutional Sparse Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"James T. Kwok, Lionel M. Ni, Quanming Yao, Yaqing Wang","submitted_at":"2017-06-21T15:50:12Z","abstract_excerpt":"Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are expensive, in terms of both space and time, on large datasets. In this paper, we alleviate these problems by using online learning. The key is a reformulation of the CSC objective so that convolution can be handled easily in the frequency domain and much smaller history matrices are needed. We use the alternating direction method of multipliers (ADMM) to solve the resulting optimization problem and the ADMM subproble"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06972","kind":"arxiv","version":3},"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":"2026-05-18T00:09:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tt031zp09dQv9KS85TSd/3MmrKezaJbf9fHqSPgKuwPauAlxK0SZ1RMKivMbtQ9m4SM8+KKIX28D4oLtUHurCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T20:02:36.257968Z"},"content_sha256":"df793ee07bb6288cbd011a61fffa1561a0df6d5dca3b7973f5ca3bbb68d711b4","schema_version":"1.0","event_id":"sha256:df793ee07bb6288cbd011a61fffa1561a0df6d5dca3b7973f5ca3bbb68d711b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/bundle.json","state_url":"https://pith.science/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/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":"2026-06-01T20:02:36Z","links":{"resolver":"https://pith.science/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI","bundle":"https://pith.science/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/bundle.json","state":"https://pith.science/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WFPQODD4FUEO54FUQ3ZQ2DDFYI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:WFPQODD4FUEO54FUQ3ZQ2DDFYI","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":"c5eb619b6276d92fbe49bf1868170c51b5cd3183bbc8583c1d6b16c542c0ca34","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T15:50:12Z","title_canon_sha256":"4df030bcdd3371ce5b690b8dce8e7d03f8de54f2b01daf98615c698405343d27"},"schema_version":"1.0","source":{"id":"1706.06972","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.06972","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"arxiv_version","alias_value":"1706.06972v3","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06972","created_at":"2026-05-18T00:09:20Z"},{"alias_kind":"pith_short_12","alias_value":"WFPQODD4FUEO","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WFPQODD4FUEO54FU","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WFPQODD4","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:df793ee07bb6288cbd011a61fffa1561a0df6d5dca3b7973f5ca3bbb68d711b4","target":"graph","created_at":"2026-05-18T00:09:20Z","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":"Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are expensive, in terms of both space and time, on large datasets. In this paper, we alleviate these problems by using online learning. The key is a reformulation of the CSC objective so that convolution can be handled easily in the frequency domain and much smaller history matrices are needed. We use the alternating direction method of multipliers (ADMM) to solve the resulting optimization problem and the ADMM subproble","authors_text":"James T. Kwok, Lionel M. Ni, Quanming Yao, Yaqing Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T15:50:12Z","title":"Scalable Online Convolutional Sparse Coding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06972","kind":"arxiv","version":3},"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:ea35cac568b91e334246498cdd50f41d29cbc4a8c8b09abe4b98e426a34ff424","target":"record","created_at":"2026-05-18T00:09:20Z","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":"c5eb619b6276d92fbe49bf1868170c51b5cd3183bbc8583c1d6b16c542c0ca34","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-21T15:50:12Z","title_canon_sha256":"4df030bcdd3371ce5b690b8dce8e7d03f8de54f2b01daf98615c698405343d27"},"schema_version":"1.0","source":{"id":"1706.06972","kind":"arxiv","version":3}},"canonical_sha256":"b15f070c7c2d08eef0b486f30d0c65c20b504ff93355324ab2d715484e7c0f7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b15f070c7c2d08eef0b486f30d0c65c20b504ff93355324ab2d715484e7c0f7c","first_computed_at":"2026-05-18T00:09:20.316456Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:20.316456Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bYr2DaWIy7Tk7v73OTfYBv5Igf/KN7BRIiUdxmGPAaBVs9J5pnIeu5ukaUcJShRk7smq9QDsEpQLnCm80QODAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:20.317027Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.06972","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea35cac568b91e334246498cdd50f41d29cbc4a8c8b09abe4b98e426a34ff424","sha256:df793ee07bb6288cbd011a61fffa1561a0df6d5dca3b7973f5ca3bbb68d711b4"],"state_sha256":"de42db570b36a4d27cc4f8614cd2999f593fd4eb238ae3d006927d106adbcc6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YM3ZDsAtc1UNP6PAtu5r+Ef5WXRVsEqs4Z6/p06G+bhYxQndpLZ0vQlrUys/c2FfFxSMIKPjdPbjBc3MdYB+AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T20:02:36.260479Z","bundle_sha256":"944c600304a57fccd5b73cf19cfb014188296195258d5b3100223d5bc5b16c77"}}