{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3OXMYQABXGIQ6IFF2CTQIHJP3T","short_pith_number":"pith:3OXMYQAB","canonical_record":{"source":{"id":"1706.01208","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-06-05T06:24:05Z","cross_cats_sorted":[],"title_canon_sha256":"3ccdef19e35401616f699281d875b4c8ecd9e843ca237edae9d5702b21053278","abstract_canon_sha256":"b45b32d7d5d0fb77160b1b0ba2199419de3c36d6a9728d096c4ab6267b8c044d"},"schema_version":"1.0"},"canonical_sha256":"dbaecc4001b9910f20a5d0a7041d2fdceabc6dee79720613137058767c478824","source":{"kind":"arxiv","id":"1706.01208","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.01208","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"arxiv_version","alias_value":"1706.01208v1","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01208","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"pith_short_12","alias_value":"3OXMYQABXGIQ","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3OXMYQABXGIQ6IFF","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3OXMYQAB","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3OXMYQABXGIQ6IFF2CTQIHJP3T","target":"record","payload":{"canonical_record":{"source":{"id":"1706.01208","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-06-05T06:24:05Z","cross_cats_sorted":[],"title_canon_sha256":"3ccdef19e35401616f699281d875b4c8ecd9e843ca237edae9d5702b21053278","abstract_canon_sha256":"b45b32d7d5d0fb77160b1b0ba2199419de3c36d6a9728d096c4ab6267b8c044d"},"schema_version":"1.0"},"canonical_sha256":"dbaecc4001b9910f20a5d0a7041d2fdceabc6dee79720613137058767c478824","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:04.196227Z","signature_b64":"f1mTsAsebneOboop8jYk1CFRKSAJPX7SRKiLeQUicwdmpznj8XPhK9hUMoQ4QDf90pFtsNtOkim77UaRj8x0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbaecc4001b9910f20a5d0a7041d2fdceabc6dee79720613137058767c478824","last_reissued_at":"2026-05-18T00:43:04.195509Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:04.195509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.01208","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":"2026-05-18T00:43:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PfBsdduVhMjAplFAGj5vyrmHoRCVUBnAtDcv0Tx2YcUo67KZ213QR28aQZREBRMT4cqPF3XlgBF5+GMXFeDkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:55:15.318219Z"},"content_sha256":"76c9ea1691d15e6b0329ced5010b7ad6ccf4f3a6de58d0a8a0f96e71e2d38dc4","schema_version":"1.0","event_id":"sha256:76c9ea1691d15e6b0329ced5010b7ad6ccf4f3a6de58d0a8a0f96e71e2d38dc4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3OXMYQABXGIQ6IFF2CTQIHJP3T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Connelly Barnes, Yuting Yang","submitted_at":"2017-06-05T06:24:05Z","abstract_excerpt":"This paper introduces a general method to approximate the convolution of an arbitrary program with a Gaussian kernel. This process has the effect of smoothing out a program. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. Our approach breaks the input program into parts and relates the statistics of the different parts, under the smoothing process. We give several approximations that can be used for the different parts of the program. These include the approximation of Dorn et al., a novel adaptive Gaussian approximat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01208","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":"2026-05-18T00:43:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v7QygzSpAeWyWrFIdzzOgiyEXsf3AC4Ha35gWALqQa2KR4I0BFYYnxYuyeXo0vSwO4Tw/TB0UhMEzUALGWkJBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:55:15.318835Z"},"content_sha256":"f5210c7c9692e0fc079ab2e7b7dabb01df2657fda63dfee2abea6e5a5c449c80","schema_version":"1.0","event_id":"sha256:f5210c7c9692e0fc079ab2e7b7dabb01df2657fda63dfee2abea6e5a5c449c80"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/bundle.json","state_url":"https://pith.science/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/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":"2026-05-26T16:55:15Z","links":{"resolver":"https://pith.science/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T","bundle":"https://pith.science/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/bundle.json","state":"https://pith.science/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3OXMYQABXGIQ6IFF2CTQIHJP3T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3OXMYQABXGIQ6IFF2CTQIHJP3T","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":"b45b32d7d5d0fb77160b1b0ba2199419de3c36d6a9728d096c4ab6267b8c044d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-06-05T06:24:05Z","title_canon_sha256":"3ccdef19e35401616f699281d875b4c8ecd9e843ca237edae9d5702b21053278"},"schema_version":"1.0","source":{"id":"1706.01208","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.01208","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"arxiv_version","alias_value":"1706.01208v1","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01208","created_at":"2026-05-18T00:43:04Z"},{"alias_kind":"pith_short_12","alias_value":"3OXMYQABXGIQ","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3OXMYQABXGIQ6IFF","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3OXMYQAB","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:f5210c7c9692e0fc079ab2e7b7dabb01df2657fda63dfee2abea6e5a5c449c80","target":"graph","created_at":"2026-05-18T00:43:04Z","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":"This paper introduces a general method to approximate the convolution of an arbitrary program with a Gaussian kernel. This process has the effect of smoothing out a program. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. Our approach breaks the input program into parts and relates the statistics of the different parts, under the smoothing process. We give several approximations that can be used for the different parts of the program. These include the approximation of Dorn et al., a novel adaptive Gaussian approximat","authors_text":"Connelly Barnes, Yuting Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-06-05T06:24:05Z","title":"Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01208","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":"sha256:76c9ea1691d15e6b0329ced5010b7ad6ccf4f3a6de58d0a8a0f96e71e2d38dc4","target":"record","created_at":"2026-05-18T00:43:04Z","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":"b45b32d7d5d0fb77160b1b0ba2199419de3c36d6a9728d096c4ab6267b8c044d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2017-06-05T06:24:05Z","title_canon_sha256":"3ccdef19e35401616f699281d875b4c8ecd9e843ca237edae9d5702b21053278"},"schema_version":"1.0","source":{"id":"1706.01208","kind":"arxiv","version":1}},"canonical_sha256":"dbaecc4001b9910f20a5d0a7041d2fdceabc6dee79720613137058767c478824","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbaecc4001b9910f20a5d0a7041d2fdceabc6dee79720613137058767c478824","first_computed_at":"2026-05-18T00:43:04.195509Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:04.195509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f1mTsAsebneOboop8jYk1CFRKSAJPX7SRKiLeQUicwdmpznj8XPhK9hUMoQ4QDf90pFtsNtOkim77UaRj8x0Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:04.196227Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.01208","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76c9ea1691d15e6b0329ced5010b7ad6ccf4f3a6de58d0a8a0f96e71e2d38dc4","sha256:f5210c7c9692e0fc079ab2e7b7dabb01df2657fda63dfee2abea6e5a5c449c80"],"state_sha256":"bd62fe7a64360f1fb0c3dd906b0fd56b5acfe4cfff344119dc658e0727a21f5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WN8qnP0n6IhTwR5PqDhtuV82PEV+FmG7ApzhOeemPWFzE7Yr4iaDRb+wdYWWEvPCamJisP9TU7Ac2vwoR0KtAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:55:15.321698Z","bundle_sha256":"a120b15cf916fe34fcb8a030eec5aea5f1d9c3785b74e46b06e2d83ad02d83d3"}}