{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZWGCFCUPWKE3UT5S3BH6QAZNY2","short_pith_number":"pith:ZWGCFCUP","schema_version":"1.0","canonical_sha256":"cd8c228a8fb289ba4fb2d84fe8032dc69ac6a1cc555b091acbee8a49c6408337","source":{"kind":"arxiv","id":"1810.09409","version":2},"attestation_state":"computed","paper":{"title":"Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akos Pasztor, Andreas Vieli, Jan Beutel, J\\'erome Faillettaz, Lothar Thiele, Matthias Meyer, Reto Da Forno, Samuel Weber, Timo Farei-Campagna, Tonio Gsell","submitted_at":"2018-10-22T17:24:31Z","abstract_excerpt":"In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, thresho"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1810.09409","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-22T17:24:31Z","cross_cats_sorted":["cs.NI","stat.ML"],"title_canon_sha256":"5cb943057ea0bf3d3f1cf0137ada3342567f4c4f32004b45d954a643490b1c9b","abstract_canon_sha256":"bb71b2349ed37fead926d728cfc5386e3e33bfcee95677ce969206e41d1d2000"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:21.896706Z","signature_b64":"PkwpL5jeuvjfn28xT6ZXefr75cS/ODJe32UEpEsl6e1l0k32VS3PSEoJJ81/8DT0PQoPAbr29IP/rcUqULKFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd8c228a8fb289ba4fb2d84fe8032dc69ac6a1cc555b091acbee8a49c6408337","last_reissued_at":"2026-05-17T23:52:21.895927Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:21.895927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Akos Pasztor, Andreas Vieli, Jan Beutel, J\\'erome Faillettaz, Lothar Thiele, Matthias Meyer, Reto Da Forno, Samuel Weber, Timo Farei-Campagna, Tonio Gsell","submitted_at":"2018-10-22T17:24:31Z","abstract_excerpt":"In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, thresho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09409","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1810.09409","created_at":"2026-05-17T23:52:21.896060+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.09409v2","created_at":"2026-05-17T23:52:21.896060+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09409","created_at":"2026-05-17T23:52:21.896060+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWGCFCUPWKE3","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWGCFCUPWKE3UT5S","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWGCFCUP","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2","json":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2.json","graph_json":"https://pith.science/api/pith-number/ZWGCFCUPWKE3UT5S3BH6QAZNY2/graph.json","events_json":"https://pith.science/api/pith-number/ZWGCFCUPWKE3UT5S3BH6QAZNY2/events.json","paper":"https://pith.science/paper/ZWGCFCUP"},"agent_actions":{"view_html":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2","download_json":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2.json","view_paper":"https://pith.science/paper/ZWGCFCUP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.09409&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWGCFCUPWKE3UT5S3BH6QAZNY2/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWGCFCUPWKE3UT5S3BH6QAZNY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2/action/storage_attestation","attest_author":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2/action/author_attestation","sign_citation":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2/action/citation_signature","submit_replication":"https://pith.science/pith/ZWGCFCUPWKE3UT5S3BH6QAZNY2/action/replication_record"}},"created_at":"2026-05-17T23:52:21.896060+00:00","updated_at":"2026-05-17T23:52:21.896060+00:00"}