{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:YMLUNK4MIKFMJX2HRZ5U7POHHS","short_pith_number":"pith:YMLUNK4M","canonical_record":{"source":{"id":"1407.5807","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-07-22T10:12:51Z","cross_cats_sorted":["cs.SY","stat.ML"],"title_canon_sha256":"efb0510002e52e3c81277c7558fa772265808809750788c93b302069968ab6a9","abstract_canon_sha256":"de1bb10f7860f709d42195f56524bd8277b090bd3d675a2fd01b5f134b4f4815"},"schema_version":"1.0"},"canonical_sha256":"c31746ab8c428ac4df478e7b4fbdc73cb88e7989d1905a2e2eed8f3f6b786731","source":{"kind":"arxiv","id":"1407.5807","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.5807","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"arxiv_version","alias_value":"1407.5807v1","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.5807","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"pith_short_12","alias_value":"YMLUNK4MIKFM","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"YMLUNK4MIKFMJX2H","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"YMLUNK4M","created_at":"2026-05-18T12:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:YMLUNK4MIKFMJX2HRZ5U7POHHS","target":"record","payload":{"canonical_record":{"source":{"id":"1407.5807","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-07-22T10:12:51Z","cross_cats_sorted":["cs.SY","stat.ML"],"title_canon_sha256":"efb0510002e52e3c81277c7558fa772265808809750788c93b302069968ab6a9","abstract_canon_sha256":"de1bb10f7860f709d42195f56524bd8277b090bd3d675a2fd01b5f134b4f4815"},"schema_version":"1.0"},"canonical_sha256":"c31746ab8c428ac4df478e7b4fbdc73cb88e7989d1905a2e2eed8f3f6b786731","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:47:06.601360Z","signature_b64":"rXr9eZtzRBzus8nv4u99+BydCggoSGKoY4od1v0Kx0bqn87/B+m9xeQgS0F4jpqrBOmHHlhl6sOAS5t1yMu+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c31746ab8c428ac4df478e7b4fbdc73cb88e7989d1905a2e2eed8f3f6b786731","last_reissued_at":"2026-05-18T02:47:06.600949Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:47:06.600949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1407.5807","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":"2026-05-18T02:47:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r+w75zAmr1ehqb9K1Xl0v6sKkKipNYbr1jaNiiL5nCTHaco2cxEudlSX4eVxW1tjRCVP3zyt+0II+lliZmoqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:26:41.467842Z"},"content_sha256":"7e6d5454c1dae77ff7690db5bafaf554da842ffffc4c26f5460343bdcb6c6395","schema_version":"1.0","event_id":"sha256:7e6d5454c1dae77ff7690db5bafaf554da842ffffc4c26f5460343bdcb6c6395"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:YMLUNK4MIKFMJX2HRZ5U7POHHS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-agents adaptive estimation and coverage control using Gaussian regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","stat.ML"],"primary_cat":"cs.MA","authors_text":"Andrea Carron, Gianluigi Pillonetto, Luca Schenato, Marco Todescato, Ruggero Carli","submitted_at":"2014-07-22T10:12:51Z","abstract_excerpt":"We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements. Hence, estimation and coverage needs to be performed at the same time. We cast the problem in a Bayesian regression framework, where the sensory function is seen as a Gaussian random field. Then, we design a set of control inputs which try to well balance coverage and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.5807","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":"2026-05-18T02:47:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5MMBJsaLgakR7zq/PhSeqqTlxP5zfdzBnlkeY5Q5sWQiv1L7t768BaB/h9f77NbOzaiW2WZKznELXTkR0L0KBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:26:41.468485Z"},"content_sha256":"cd67284332ba05895a87d7e4fdcb49b1b4d8f0b6023fe10f990de7bf46edf465","schema_version":"1.0","event_id":"sha256:cd67284332ba05895a87d7e4fdcb49b1b4d8f0b6023fe10f990de7bf46edf465"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/bundle.json","state_url":"https://pith.science/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/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":"2026-05-25T21:26:41Z","links":{"resolver":"https://pith.science/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS","bundle":"https://pith.science/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/bundle.json","state":"https://pith.science/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YMLUNK4MIKFMJX2HRZ5U7POHHS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:YMLUNK4MIKFMJX2HRZ5U7POHHS","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":"de1bb10f7860f709d42195f56524bd8277b090bd3d675a2fd01b5f134b4f4815","cross_cats_sorted":["cs.SY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-07-22T10:12:51Z","title_canon_sha256":"efb0510002e52e3c81277c7558fa772265808809750788c93b302069968ab6a9"},"schema_version":"1.0","source":{"id":"1407.5807","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.5807","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"arxiv_version","alias_value":"1407.5807v1","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.5807","created_at":"2026-05-18T02:47:06Z"},{"alias_kind":"pith_short_12","alias_value":"YMLUNK4MIKFM","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"YMLUNK4MIKFMJX2H","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"YMLUNK4M","created_at":"2026-05-18T12:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:cd67284332ba05895a87d7e4fdcb49b1b4d8f0b6023fe10f990de7bf46edf465","target":"graph","created_at":"2026-05-18T02:47:06Z","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":"We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements. Hence, estimation and coverage needs to be performed at the same time. We cast the problem in a Bayesian regression framework, where the sensory function is seen as a Gaussian random field. Then, we design a set of control inputs which try to well balance coverage and","authors_text":"Andrea Carron, Gianluigi Pillonetto, Luca Schenato, Marco Todescato, Ruggero Carli","cross_cats":["cs.SY","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-07-22T10:12:51Z","title":"Multi-agents adaptive estimation and coverage control using Gaussian regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.5807","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":"sha256:7e6d5454c1dae77ff7690db5bafaf554da842ffffc4c26f5460343bdcb6c6395","target":"record","created_at":"2026-05-18T02:47:06Z","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":"de1bb10f7860f709d42195f56524bd8277b090bd3d675a2fd01b5f134b4f4815","cross_cats_sorted":["cs.SY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-07-22T10:12:51Z","title_canon_sha256":"efb0510002e52e3c81277c7558fa772265808809750788c93b302069968ab6a9"},"schema_version":"1.0","source":{"id":"1407.5807","kind":"arxiv","version":1}},"canonical_sha256":"c31746ab8c428ac4df478e7b4fbdc73cb88e7989d1905a2e2eed8f3f6b786731","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c31746ab8c428ac4df478e7b4fbdc73cb88e7989d1905a2e2eed8f3f6b786731","first_computed_at":"2026-05-18T02:47:06.600949Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:47:06.600949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rXr9eZtzRBzus8nv4u99+BydCggoSGKoY4od1v0Kx0bqn87/B+m9xeQgS0F4jpqrBOmHHlhl6sOAS5t1yMu+DA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:47:06.601360Z","signed_message":"canonical_sha256_bytes"},"source_id":"1407.5807","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e6d5454c1dae77ff7690db5bafaf554da842ffffc4c26f5460343bdcb6c6395","sha256:cd67284332ba05895a87d7e4fdcb49b1b4d8f0b6023fe10f990de7bf46edf465"],"state_sha256":"cc983d1ba26bf5cc186513a1eb5ed710589f79fd7eb5d45990c652874dacec84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZP9OpAie6YULnREmhRxtKzJZBkiy6V/15X2D3JhEGOpgBJ7PpNHEBvHISpvSKoA/OzVO8QfCSD69F8I878hDAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:26:41.472058Z","bundle_sha256":"afd253ce72988d1b1c2a522b0bc9c0489242c713713a4f7f398105fa5bbc15a3"}}