{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:M32JJHSTFUHJXOLKVFFZKZWUGH","short_pith_number":"pith:M32JJHST","canonical_record":{"source":{"id":"1612.06543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2016-12-20T08:19:52Z","cross_cats_sorted":[],"title_canon_sha256":"dd4041d71bfd4ce7f478c1a5710bbeca880d3149880b817906806520718ad099","abstract_canon_sha256":"2a6d91c98cf34c54929394cf97908cb0e34912c3755bdc1d50f58dcf4942e9ce"},"schema_version":"1.0"},"canonical_sha256":"66f4949e532d0e9bb96aa94b9566d431e7a30a3ee0bc577f6502d38fb1c49caa","source":{"kind":"arxiv","id":"1612.06543","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.06543","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"arxiv_version","alias_value":"1612.06543v1","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.06543","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"pith_short_12","alias_value":"M32JJHSTFUHJ","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"M32JJHSTFUHJXOLK","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"M32JJHST","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:M32JJHSTFUHJXOLKVFFZKZWUGH","target":"record","payload":{"canonical_record":{"source":{"id":"1612.06543","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2016-12-20T08:19:52Z","cross_cats_sorted":[],"title_canon_sha256":"dd4041d71bfd4ce7f478c1a5710bbeca880d3149880b817906806520718ad099","abstract_canon_sha256":"2a6d91c98cf34c54929394cf97908cb0e34912c3755bdc1d50f58dcf4942e9ce"},"schema_version":"1.0"},"canonical_sha256":"66f4949e532d0e9bb96aa94b9566d431e7a30a3ee0bc577f6502d38fb1c49caa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:25.498141Z","signature_b64":"T1STSaAK/8sk9lPzCzzoHvVmsTgYEbg51AK2Urp3rY1pL2ac2StW4prYPHXBkwlEAh6jsvLWOzSN76kJ9eksAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66f4949e532d0e9bb96aa94b9566d431e7a30a3ee0bc577f6502d38fb1c49caa","last_reissued_at":"2026-05-18T00:54:25.497491Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:25.497491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.06543","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":"2026-05-18T00:54:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oScPBOLqyC7KlFx/5m1439uSf8+NPoPHoqcURjBvmPSywAIwaDxGAV1G7UFr/a4EksYrAE296YPi3kI+oUpMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:40:02.799420Z"},"content_sha256":"2557206fe77b7f3fbfbb3eb44a8d9be032b7117a42b626bd8f0eb73877ceaaea","schema_version":"1.0","event_id":"sha256:2557206fe77b7f3fbfbb3eb44a8d9be032b7117a42b626bd8f0eb73877ceaaea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:M32JJHSTFUHJXOLKVFFZKZWUGH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Wide-Slice Residual Networks for Food Recognition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Christian Micheloni, Gian Luca Foresti, Niki Martinel","submitted_at":"2016-12-20T08:19:52Z","abstract_excerpt":"Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.06543","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":"2026-05-18T00:54:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wxFca68qfhpeBeu8VjMfx374PjvoONXVsl0Bp95dOxHYX86O3OuYc54XJVPyvka/CREG/oybMdUX4licMmcoCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:40:02.799763Z"},"content_sha256":"4bbd5d7123e3756f74547dcd34d74967e2e4c93fc9184aa47016cc17b88e4862","schema_version":"1.0","event_id":"sha256:4bbd5d7123e3756f74547dcd34d74967e2e4c93fc9184aa47016cc17b88e4862"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/bundle.json","state_url":"https://pith.science/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/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":"2026-05-31T03:40:02Z","links":{"resolver":"https://pith.science/pith/M32JJHSTFUHJXOLKVFFZKZWUGH","bundle":"https://pith.science/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/bundle.json","state":"https://pith.science/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M32JJHSTFUHJXOLKVFFZKZWUGH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:M32JJHSTFUHJXOLKVFFZKZWUGH","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":"2a6d91c98cf34c54929394cf97908cb0e34912c3755bdc1d50f58dcf4942e9ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2016-12-20T08:19:52Z","title_canon_sha256":"dd4041d71bfd4ce7f478c1a5710bbeca880d3149880b817906806520718ad099"},"schema_version":"1.0","source":{"id":"1612.06543","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.06543","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"arxiv_version","alias_value":"1612.06543v1","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.06543","created_at":"2026-05-18T00:54:25Z"},{"alias_kind":"pith_short_12","alias_value":"M32JJHSTFUHJ","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"M32JJHSTFUHJXOLK","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"M32JJHST","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:4bbd5d7123e3756f74547dcd34d74967e2e4c93fc9184aa47016cc17b88e4862","target":"graph","created_at":"2026-05-18T00:54:25Z","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":"Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defin","authors_text":"Christian Micheloni, Gian Luca Foresti, Niki Martinel","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2016-12-20T08:19:52Z","title":"Wide-Slice Residual Networks for Food Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.06543","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":"sha256:2557206fe77b7f3fbfbb3eb44a8d9be032b7117a42b626bd8f0eb73877ceaaea","target":"record","created_at":"2026-05-18T00:54:25Z","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":"2a6d91c98cf34c54929394cf97908cb0e34912c3755bdc1d50f58dcf4942e9ce","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2016-12-20T08:19:52Z","title_canon_sha256":"dd4041d71bfd4ce7f478c1a5710bbeca880d3149880b817906806520718ad099"},"schema_version":"1.0","source":{"id":"1612.06543","kind":"arxiv","version":1}},"canonical_sha256":"66f4949e532d0e9bb96aa94b9566d431e7a30a3ee0bc577f6502d38fb1c49caa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"66f4949e532d0e9bb96aa94b9566d431e7a30a3ee0bc577f6502d38fb1c49caa","first_computed_at":"2026-05-18T00:54:25.497491Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:54:25.497491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T1STSaAK/8sk9lPzCzzoHvVmsTgYEbg51AK2Urp3rY1pL2ac2StW4prYPHXBkwlEAh6jsvLWOzSN76kJ9eksAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:54:25.498141Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.06543","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2557206fe77b7f3fbfbb3eb44a8d9be032b7117a42b626bd8f0eb73877ceaaea","sha256:4bbd5d7123e3756f74547dcd34d74967e2e4c93fc9184aa47016cc17b88e4862"],"state_sha256":"f16e011dbb4b04fe129051a52321adf345697b44220b8a1b16fe147b75b2b323"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dKf3vZ6QjWwU8cbF30rIhIIiWTOiXMiSVfsiYM2eea7hiG5wqt0hLro3IMgpI73HMXXWSNnkMI+jvNmpvnanCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T03:40:02.801606Z","bundle_sha256":"e4f805684dc89248f3251cd0647f04ad2c0e159134fdc2af526e8835bf99c64c"}}