{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:D5DROHWA5YVUVX7AJRANS3SNY2","short_pith_number":"pith:D5DROHWA","canonical_record":{"source":{"id":"1602.04358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-02-13T17:25:03Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"b05dcf72c8a2a0644738d519e064fa47929698cfec33ed4ece90348972099eb9","abstract_canon_sha256":"0a2edf0795fe9563af963f7d7e76d4a57d92e9a5f1ce1d131277ac35b72dc64b"},"schema_version":"1.0"},"canonical_sha256":"1f47171ec0ee2b4adfe04c40d96e4dc68ee4cf6b1928cf9f51dd23c74027de7d","source":{"kind":"arxiv","id":"1602.04358","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.04358","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"arxiv_version","alias_value":"1602.04358v1","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04358","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"pith_short_12","alias_value":"D5DROHWA5YVU","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"D5DROHWA5YVUVX7A","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"D5DROHWA","created_at":"2026-05-18T12:30:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:D5DROHWA5YVUVX7AJRANS3SNY2","target":"record","payload":{"canonical_record":{"source":{"id":"1602.04358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-02-13T17:25:03Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"b05dcf72c8a2a0644738d519e064fa47929698cfec33ed4ece90348972099eb9","abstract_canon_sha256":"0a2edf0795fe9563af963f7d7e76d4a57d92e9a5f1ce1d131277ac35b72dc64b"},"schema_version":"1.0"},"canonical_sha256":"1f47171ec0ee2b4adfe04c40d96e4dc68ee4cf6b1928cf9f51dd23c74027de7d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:51.116783Z","signature_b64":"gUv4dSgBsX6jvs0eiRdSlBhe2IGW+l+mBOmxEHCmcujFQsUmejCWpL75FxelsMOuhI+6Li3bTUhj3rPHD95+Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f47171ec0ee2b4adfe04c40d96e4dc68ee4cf6b1928cf9f51dd23c74027de7d","last_reissued_at":"2026-05-18T01:20:51.116226Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:51.116226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.04358","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":"2026-05-18T01:20:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zfrRHKRY/uGnN8EQwmW9dJQIt7rkk/nLAt5DekPOTTY/cI4z368FvIgENI2Q4PxLnmO5yGHqCOwGKpIbYCkoCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:06:29.872952Z"},"content_sha256":"f49872d93fe7e366c705cce2eded39c00ef63df4298a016cf3a13e7b459f1df9","schema_version":"1.0","event_id":"sha256:f49872d93fe7e366c705cce2eded39c00ef63df4298a016cf3a13e7b459f1df9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:D5DROHWA5YVUVX7AJRANS3SNY2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine olfaction using time scattering of sensor multiresolution graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","stat.ML"],"primary_cat":"cs.AI","authors_text":"Leonid Gugel, Shai Dekel, Yoel Shkolnisky","submitted_at":"2016-02-13T17:25:03Z","abstract_excerpt":"In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors. The algorithm then applies scattering networks to the time series graphs to create the feature space. We demonstrate our method on a machine olfaction problem, where one needs to classify the gas type and the location where it originates from data sampled by an array of sensors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04358","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":"2026-05-18T01:20:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YnrSKipE/4nCZNDE7nf/jHpaSSctt1Xx4DXu+Mx0EbgC7Q+wTwFSXnvKXLRDCTapMdcQeh0KWFFch3ENRwNaBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:06:29.873653Z"},"content_sha256":"4eed768eabfa0a06025d7830777319b73a617b9a2dcf6647feb8aace9b11b245","schema_version":"1.0","event_id":"sha256:4eed768eabfa0a06025d7830777319b73a617b9a2dcf6647feb8aace9b11b245"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D5DROHWA5YVUVX7AJRANS3SNY2/bundle.json","state_url":"https://pith.science/pith/D5DROHWA5YVUVX7AJRANS3SNY2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D5DROHWA5YVUVX7AJRANS3SNY2/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":"2026-05-30T11:06:29Z","links":{"resolver":"https://pith.science/pith/D5DROHWA5YVUVX7AJRANS3SNY2","bundle":"https://pith.science/pith/D5DROHWA5YVUVX7AJRANS3SNY2/bundle.json","state":"https://pith.science/pith/D5DROHWA5YVUVX7AJRANS3SNY2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D5DROHWA5YVUVX7AJRANS3SNY2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:D5DROHWA5YVUVX7AJRANS3SNY2","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":"0a2edf0795fe9563af963f7d7e76d4a57d92e9a5f1ce1d131277ac35b72dc64b","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-02-13T17:25:03Z","title_canon_sha256":"b05dcf72c8a2a0644738d519e064fa47929698cfec33ed4ece90348972099eb9"},"schema_version":"1.0","source":{"id":"1602.04358","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.04358","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"arxiv_version","alias_value":"1602.04358v1","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04358","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"pith_short_12","alias_value":"D5DROHWA5YVU","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_16","alias_value":"D5DROHWA5YVUVX7A","created_at":"2026-05-18T12:30:09Z"},{"alias_kind":"pith_short_8","alias_value":"D5DROHWA","created_at":"2026-05-18T12:30:09Z"}],"graph_snapshots":[{"event_id":"sha256:4eed768eabfa0a06025d7830777319b73a617b9a2dcf6647feb8aace9b11b245","target":"graph","created_at":"2026-05-18T01:20:51Z","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":"In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors. The algorithm then applies scattering networks to the time series graphs to create the feature space. We demonstrate our method on a machine olfaction problem, where one needs to classify the gas type and the location where it originates from data sampled by an array of sensors","authors_text":"Leonid Gugel, Shai Dekel, Yoel Shkolnisky","cross_cats":["cs.DS","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-02-13T17:25:03Z","title":"Machine olfaction using time scattering of sensor multiresolution graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04358","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:f49872d93fe7e366c705cce2eded39c00ef63df4298a016cf3a13e7b459f1df9","target":"record","created_at":"2026-05-18T01:20:51Z","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":"0a2edf0795fe9563af963f7d7e76d4a57d92e9a5f1ce1d131277ac35b72dc64b","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-02-13T17:25:03Z","title_canon_sha256":"b05dcf72c8a2a0644738d519e064fa47929698cfec33ed4ece90348972099eb9"},"schema_version":"1.0","source":{"id":"1602.04358","kind":"arxiv","version":1}},"canonical_sha256":"1f47171ec0ee2b4adfe04c40d96e4dc68ee4cf6b1928cf9f51dd23c74027de7d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f47171ec0ee2b4adfe04c40d96e4dc68ee4cf6b1928cf9f51dd23c74027de7d","first_computed_at":"2026-05-18T01:20:51.116226Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:51.116226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gUv4dSgBsX6jvs0eiRdSlBhe2IGW+l+mBOmxEHCmcujFQsUmejCWpL75FxelsMOuhI+6Li3bTUhj3rPHD95+Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:51.116783Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.04358","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f49872d93fe7e366c705cce2eded39c00ef63df4298a016cf3a13e7b459f1df9","sha256:4eed768eabfa0a06025d7830777319b73a617b9a2dcf6647feb8aace9b11b245"],"state_sha256":"47c0f0e18e9e27a76c010deaaf73a6dac18723972a934071a395e813d4f4a33b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AWpMZT+mDSYCr+E2wxYL6hycZ1r49vRchZD/R8FXnxYN3moHC0aeYOX0OAXNNkP09Hey3Ne37xhrp8ZzwF1NDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T11:06:29.877536Z","bundle_sha256":"dcb96dd1540f24fa7ea69c81ea2ea827e021f274fc76d48e433071a006ad7ea0"}}