{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KR6D6OG3R3SYG5QTDSMNCSQ265","short_pith_number":"pith:KR6D6OG3","canonical_record":{"source":{"id":"2411.10128","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-15T12:01:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0d5c451da68368187cdd3e4eb3fcb57803dbafd082e9177447b6c38344aac465","abstract_canon_sha256":"af5f2828b62e918c34e2c31fed6915a1a5526929594c679f8ba044f9dde6cdca"},"schema_version":"1.0"},"canonical_sha256":"547c3f38db8ee58376131c98d14a1af77ec17b4db23677d3bd60c514487a63c5","source":{"kind":"arxiv","id":"2411.10128","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.10128","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"2411.10128v1","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10128","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"KR6D6OG3R3SY","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_16","alias_value":"KR6D6OG3R3SYG5QT","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_8","alias_value":"KR6D6OG3","created_at":"2026-07-05T09:36:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KR6D6OG3R3SYG5QTDSMNCSQ265","target":"record","payload":{"canonical_record":{"source":{"id":"2411.10128","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-15T12:01:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0d5c451da68368187cdd3e4eb3fcb57803dbafd082e9177447b6c38344aac465","abstract_canon_sha256":"af5f2828b62e918c34e2c31fed6915a1a5526929594c679f8ba044f9dde6cdca"},"schema_version":"1.0"},"canonical_sha256":"547c3f38db8ee58376131c98d14a1af77ec17b4db23677d3bd60c514487a63c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:00.089062Z","signature_b64":"eNpvl4qIDoMDU6pHulheR6KqHiNklU0k1OyjFHVGqi4y8LskFyqO0Cx1d8sU0SfUqWccOqyBGvCTW+cUVxF7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"547c3f38db8ee58376131c98d14a1af77ec17b4db23677d3bd60c514487a63c5","last_reissued_at":"2026-07-05T09:36:00.088620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:00.088620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.10128","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":"2026-07-05T09:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1LKiwXo+s48ZQ3tLDWtR243OO/5Ls5DED1zepckLd2NQG5bDRIvvhd4gJbkZxlsvI+iaxvu1BRtS96DA6gWKDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:59:21.673050Z"},"content_sha256":"810eeeae5786b75fe4b772c84ee9dd211736f74d7e6b5ad5eb3b72bd6cacaa14","schema_version":"1.0","event_id":"sha256:810eeeae5786b75fe4b772c84ee9dd211736f74d7e6b5ad5eb3b72bd6cacaa14"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KR6D6OG3R3SYG5QTDSMNCSQ265","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Kushal Bose, Sagar Ghosh, Swagatam Das","submitted_at":"2024-11-15T12:01:03Z","abstract_excerpt":"The emergence of Deep Convolutional Neural Networks (DCNNs) has been a pervasive tool for accomplishing widespread applications in computer vision. Despite its potential capability to capture intricate patterns inside the data, the underlying embedding space remains Euclidean and primarily pursues contractive convolution. Several instances can serve as a precedent for the exacerbating performance of DCNNs. The recent advancement of neural networks in the hyperbolic spaces gained traction, incentivizing the development of convolutional deep neural networks in the hyperbolic space. In this work,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10128","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/2411.10128/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":"2026-07-05T09:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7wTfTI+KogW4B9hKDo5EN/NyE/JFuhZuP0LzpXD6MCnQxKdrR/bPKWIFVo+0rTzVU7bWdKF1L8BasA/gqZggAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:59:21.673702Z"},"content_sha256":"af0d86fe3b9ba18daade43bd4872902a356a88af93379783eedad773e4f92a33","schema_version":"1.0","event_id":"sha256:af0d86fe3b9ba18daade43bd4872902a356a88af93379783eedad773e4f92a33"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/bundle.json","state_url":"https://pith.science/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/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":"2026-08-09T07:59:21Z","links":{"resolver":"https://pith.science/pith/KR6D6OG3R3SYG5QTDSMNCSQ265","bundle":"https://pith.science/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/bundle.json","state":"https://pith.science/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KR6D6OG3R3SYG5QTDSMNCSQ265/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KR6D6OG3R3SYG5QTDSMNCSQ265","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":"af5f2828b62e918c34e2c31fed6915a1a5526929594c679f8ba044f9dde6cdca","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-15T12:01:03Z","title_canon_sha256":"0d5c451da68368187cdd3e4eb3fcb57803dbafd082e9177447b6c38344aac465"},"schema_version":"1.0","source":{"id":"2411.10128","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.10128","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"2411.10128v1","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10128","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"KR6D6OG3R3SY","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_16","alias_value":"KR6D6OG3R3SYG5QT","created_at":"2026-07-05T09:36:00Z"},{"alias_kind":"pith_short_8","alias_value":"KR6D6OG3","created_at":"2026-07-05T09:36:00Z"}],"graph_snapshots":[{"event_id":"sha256:af0d86fe3b9ba18daade43bd4872902a356a88af93379783eedad773e4f92a33","target":"graph","created_at":"2026-07-05T09:36:00Z","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/2411.10128/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The emergence of Deep Convolutional Neural Networks (DCNNs) has been a pervasive tool for accomplishing widespread applications in computer vision. Despite its potential capability to capture intricate patterns inside the data, the underlying embedding space remains Euclidean and primarily pursues contractive convolution. Several instances can serve as a precedent for the exacerbating performance of DCNNs. The recent advancement of neural networks in the hyperbolic spaces gained traction, incentivizing the development of convolutional deep neural networks in the hyperbolic space. In this work,","authors_text":"Kushal Bose, Sagar Ghosh, Swagatam Das","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-15T12:01:03Z","title":"On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10128","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":"sha256:810eeeae5786b75fe4b772c84ee9dd211736f74d7e6b5ad5eb3b72bd6cacaa14","target":"record","created_at":"2026-07-05T09:36:00Z","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":"af5f2828b62e918c34e2c31fed6915a1a5526929594c679f8ba044f9dde6cdca","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-15T12:01:03Z","title_canon_sha256":"0d5c451da68368187cdd3e4eb3fcb57803dbafd082e9177447b6c38344aac465"},"schema_version":"1.0","source":{"id":"2411.10128","kind":"arxiv","version":1}},"canonical_sha256":"547c3f38db8ee58376131c98d14a1af77ec17b4db23677d3bd60c514487a63c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"547c3f38db8ee58376131c98d14a1af77ec17b4db23677d3bd60c514487a63c5","first_computed_at":"2026-07-05T09:36:00.088620Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:00.088620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eNpvl4qIDoMDU6pHulheR6KqHiNklU0k1OyjFHVGqi4y8LskFyqO0Cx1d8sU0SfUqWccOqyBGvCTW+cUVxF7Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:00.089062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.10128","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:810eeeae5786b75fe4b772c84ee9dd211736f74d7e6b5ad5eb3b72bd6cacaa14","sha256:af0d86fe3b9ba18daade43bd4872902a356a88af93379783eedad773e4f92a33"],"state_sha256":"18e51be99197d465614c12109474c2f937cd2e34d901ec905669f639998b3ea3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yxB5l2z1mMd9vvvTuJLN5ueB4X6JH8QqPMy3HMlk/tCD4c+kuVYP/3NCPlDnI9ZUI+WvWF2nwkrJ8uzqTDNgCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:59:21.681102Z","bundle_sha256":"ac04fcdb889230bd116f0789d8dd4c204621ebde1e0adf13d1635b6fed020748"}}