{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:5JISDMYBTO7TSR3V5U2ZXSX3EN","short_pith_number":"pith:5JISDMYB","canonical_record":{"source":{"id":"1509.08387","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-28T16:48:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8c6891cdc5242939ca7f7f6deb34455feb6328d1171174acf9e796f59a464f5c","abstract_canon_sha256":"e7402ce56532f622472a81150c700cff7e9c1446fab4935cff155e371c6a406c"},"schema_version":"1.0"},"canonical_sha256":"ea5121b3019bbf394775ed359bcafb23629d16d84502fccd41e6beb5eea33145","source":{"kind":"arxiv","id":"1509.08387","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.08387","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"arxiv_version","alias_value":"1509.08387v2","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.08387","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"pith_short_12","alias_value":"5JISDMYBTO7T","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5JISDMYBTO7TSR3V","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5JISDMYB","created_at":"2026-05-18T12:29:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:5JISDMYBTO7TSR3V5U2ZXSX3EN","target":"record","payload":{"canonical_record":{"source":{"id":"1509.08387","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-28T16:48:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8c6891cdc5242939ca7f7f6deb34455feb6328d1171174acf9e796f59a464f5c","abstract_canon_sha256":"e7402ce56532f622472a81150c700cff7e9c1446fab4935cff155e371c6a406c"},"schema_version":"1.0"},"canonical_sha256":"ea5121b3019bbf394775ed359bcafb23629d16d84502fccd41e6beb5eea33145","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:35.896089Z","signature_b64":"vQXd/MdEFB26yY+fREQvs9kWEX5b2oAR90qQ6V08k88Hf7jg9weYnib7mNJB/Bs9hxWtm7hubquknmJehQpfBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea5121b3019bbf394775ed359bcafb23629d16d84502fccd41e6beb5eea33145","last_reissued_at":"2026-05-18T00:50:35.895374Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:35.895374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.08387","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:50:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wQRdS4rRrLoMUx4qZ27GQgLfDXow2+EmCHiPYNHTVp/LIlqxll1Ps4X7kKYUbZ52boedLQLtUmN04fyPkybBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:25:02.474943Z"},"content_sha256":"d267e118027db632d848099f29a6040c30d8e5c655d52956679c92f0fe1b2772","schema_version":"1.0","event_id":"sha256:d267e118027db632d848099f29a6040c30d8e5c655d52956679c92f0fe1b2772"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:5JISDMYBTO7TSR3V5U2ZXSX3EN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distance-Penalized Active Learning Using Quantile Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Brandon Wong, Branko Kerkez, Donald Scavia, John Lipor, Laura Balzano","submitted_at":"2015-09-28T16:48:39Z","abstract_excerpt":"Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\\mathbb{R}^{d}$ with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and the distance traveled during estimation. This is motivated by our work studying regions of low oxygen concentration in the Great Lakes. We show that for one-dimensional threshold classifiers, a tradeoff between the number of samples taken and distance traveled can be achieved usin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.08387","kind":"arxiv","version":2},"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":"2026-05-18T00:50:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G7nbdIir40mSkfhOICaT0MUoGa4/mDoKpuNtZDJIhXsKdKNMdCUvpY7SqOg277yTO71Rr73Ex/qJ6XWejRFJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:25:02.475627Z"},"content_sha256":"a6821c81f556d193fe294c5204392edec2b4755b4226cf014791776fd833fe54","schema_version":"1.0","event_id":"sha256:a6821c81f556d193fe294c5204392edec2b4755b4226cf014791776fd833fe54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/bundle.json","state_url":"https://pith.science/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/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":"2026-05-31T15:25:02Z","links":{"resolver":"https://pith.science/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN","bundle":"https://pith.science/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/bundle.json","state":"https://pith.science/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5JISDMYBTO7TSR3V5U2ZXSX3EN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:5JISDMYBTO7TSR3V5U2ZXSX3EN","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":"e7402ce56532f622472a81150c700cff7e9c1446fab4935cff155e371c6a406c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-28T16:48:39Z","title_canon_sha256":"8c6891cdc5242939ca7f7f6deb34455feb6328d1171174acf9e796f59a464f5c"},"schema_version":"1.0","source":{"id":"1509.08387","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.08387","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"arxiv_version","alias_value":"1509.08387v2","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.08387","created_at":"2026-05-18T00:50:35Z"},{"alias_kind":"pith_short_12","alias_value":"5JISDMYBTO7T","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_16","alias_value":"5JISDMYBTO7TSR3V","created_at":"2026-05-18T12:29:05Z"},{"alias_kind":"pith_short_8","alias_value":"5JISDMYB","created_at":"2026-05-18T12:29:05Z"}],"graph_snapshots":[{"event_id":"sha256:a6821c81f556d193fe294c5204392edec2b4755b4226cf014791776fd833fe54","target":"graph","created_at":"2026-05-18T00:50:35Z","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":"Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\\mathbb{R}^{d}$ with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and the distance traveled during estimation. This is motivated by our work studying regions of low oxygen concentration in the Great Lakes. We show that for one-dimensional threshold classifiers, a tradeoff between the number of samples taken and distance traveled can be achieved usin","authors_text":"Brandon Wong, Branko Kerkez, Donald Scavia, John Lipor, Laura Balzano","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-28T16:48:39Z","title":"Distance-Penalized Active Learning Using Quantile Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.08387","kind":"arxiv","version":2},"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:d267e118027db632d848099f29a6040c30d8e5c655d52956679c92f0fe1b2772","target":"record","created_at":"2026-05-18T00:50:35Z","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":"e7402ce56532f622472a81150c700cff7e9c1446fab4935cff155e371c6a406c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-28T16:48:39Z","title_canon_sha256":"8c6891cdc5242939ca7f7f6deb34455feb6328d1171174acf9e796f59a464f5c"},"schema_version":"1.0","source":{"id":"1509.08387","kind":"arxiv","version":2}},"canonical_sha256":"ea5121b3019bbf394775ed359bcafb23629d16d84502fccd41e6beb5eea33145","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea5121b3019bbf394775ed359bcafb23629d16d84502fccd41e6beb5eea33145","first_computed_at":"2026-05-18T00:50:35.895374Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:35.895374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vQXd/MdEFB26yY+fREQvs9kWEX5b2oAR90qQ6V08k88Hf7jg9weYnib7mNJB/Bs9hxWtm7hubquknmJehQpfBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:35.896089Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.08387","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d267e118027db632d848099f29a6040c30d8e5c655d52956679c92f0fe1b2772","sha256:a6821c81f556d193fe294c5204392edec2b4755b4226cf014791776fd833fe54"],"state_sha256":"1b85e040925c3027908f89d0b5c6fd1f30cefa382b94f2dbe206d0412ce6615d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CTMyIYhW9GtoYp71j7DfI7mIX0xQX+MOzULwJIVAK4NzshJHjFOFw2MtWpYoS/tGaVdY08UqHLgKOaOPiv4QDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T15:25:02.479586Z","bundle_sha256":"b84b19a8102f1f384bc4c2b9daf22c65c2aa5d44bc14c9a33c81da5cb384098f"}}