{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6JDA3TTES5AWG6YDVU5YZFRXQU","short_pith_number":"pith:6JDA3TTE","canonical_record":{"source":{"id":"1711.08160","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-22T07:44:20Z","cross_cats_sorted":[],"title_canon_sha256":"ae33ba2d77fde4ed95ba135129b1828a4f8278b19471994898a96b10dfd67788","abstract_canon_sha256":"612c0b30906d368fe78c66acca52afed0931fe8b582963441a39fa666ecb8287"},"schema_version":"1.0"},"canonical_sha256":"f2460dce649741637b03ad3b8c9637852ba1ee308d0359ca7e9c88daef66904a","source":{"kind":"arxiv","id":"1711.08160","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08160","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08160v2","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08160","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"6JDA3TTES5AW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6JDA3TTES5AWG6YD","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6JDA3TTE","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6JDA3TTES5AWG6YDVU5YZFRXQU","target":"record","payload":{"canonical_record":{"source":{"id":"1711.08160","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-22T07:44:20Z","cross_cats_sorted":[],"title_canon_sha256":"ae33ba2d77fde4ed95ba135129b1828a4f8278b19471994898a96b10dfd67788","abstract_canon_sha256":"612c0b30906d368fe78c66acca52afed0931fe8b582963441a39fa666ecb8287"},"schema_version":"1.0"},"canonical_sha256":"f2460dce649741637b03ad3b8c9637852ba1ee308d0359ca7e9c88daef66904a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:32.706756Z","signature_b64":"ZAiv1G8zSYjrTeFScj0BGQwS3AlLauTJJJlCIxzYxUeKQrDUmN3CoqmyU9M/2CqGMzcOVPVBYDdRZ001l+ZfAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2460dce649741637b03ad3b8c9637852ba1ee308d0359ca7e9c88daef66904a","last_reissued_at":"2026-05-18T00:12:32.705878Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:32.705878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.08160","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":"2026-05-18T00:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F4p9tBxyHPuQTG9XGQVMBZxFuPMrQfputoxNIT1kuiai9VOXeFhwHw91Dee4BsT3z3r4WyDxdgbjR1CW+FdfCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:54:59.124293Z"},"content_sha256":"b843bb17ea00845ae090fa17235f06d641dd0a9060eea6ee8c573f1f1ccd1b80","schema_version":"1.0","event_id":"sha256:b843bb17ea00845ae090fa17235f06d641dd0a9060eea6ee8c573f1f1ccd1b80"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6JDA3TTES5AWG6YDVU5YZFRXQU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Alex Tank, Ali Shojaie, Emily B. Fox, Ian Cover, Nicholas J. Foti","submitted_at":"2017-11-22T07:44:20Z","abstract_excerpt":"While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear Granger causality detection using multilayer perceptrons where the input to the network is the past time lags of all series and the output is the future value of a single series. A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero. For estimation, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08160","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":"2026-05-18T00:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4JZpnwXNjFGPu6xTUl0L/yXHK7ha313hsqxRDT34GoZnILIK/5RYO+7pV25sJtyiPW6BZUvChn9GTcQ8xsaeDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:54:59.124866Z"},"content_sha256":"1bac66a5bfa93d5d592026651d5b2b2f427842f11eb28cdee053c96a064fd4f0","schema_version":"1.0","event_id":"sha256:1bac66a5bfa93d5d592026651d5b2b2f427842f11eb28cdee053c96a064fd4f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/bundle.json","state_url":"https://pith.science/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/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":"2026-05-27T07:54:59Z","links":{"resolver":"https://pith.science/pith/6JDA3TTES5AWG6YDVU5YZFRXQU","bundle":"https://pith.science/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/bundle.json","state":"https://pith.science/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6JDA3TTES5AWG6YDVU5YZFRXQU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6JDA3TTES5AWG6YDVU5YZFRXQU","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":"612c0b30906d368fe78c66acca52afed0931fe8b582963441a39fa666ecb8287","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-22T07:44:20Z","title_canon_sha256":"ae33ba2d77fde4ed95ba135129b1828a4f8278b19471994898a96b10dfd67788"},"schema_version":"1.0","source":{"id":"1711.08160","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08160","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08160v2","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08160","created_at":"2026-05-18T00:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"6JDA3TTES5AW","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6JDA3TTES5AWG6YD","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6JDA3TTE","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:1bac66a5bfa93d5d592026651d5b2b2f427842f11eb28cdee053c96a064fd4f0","target":"graph","created_at":"2026-05-18T00:12:32Z","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":"While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear Granger causality detection using multilayer perceptrons where the input to the network is the past time lags of all series and the output is the future value of a single series. A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero. For estimation, we ","authors_text":"Alex Tank, Ali Shojaie, Emily B. Fox, Ian Cover, Nicholas J. Foti","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-22T07:44:20Z","title":"An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08160","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:b843bb17ea00845ae090fa17235f06d641dd0a9060eea6ee8c573f1f1ccd1b80","target":"record","created_at":"2026-05-18T00:12:32Z","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":"612c0b30906d368fe78c66acca52afed0931fe8b582963441a39fa666ecb8287","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-22T07:44:20Z","title_canon_sha256":"ae33ba2d77fde4ed95ba135129b1828a4f8278b19471994898a96b10dfd67788"},"schema_version":"1.0","source":{"id":"1711.08160","kind":"arxiv","version":2}},"canonical_sha256":"f2460dce649741637b03ad3b8c9637852ba1ee308d0359ca7e9c88daef66904a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2460dce649741637b03ad3b8c9637852ba1ee308d0359ca7e9c88daef66904a","first_computed_at":"2026-05-18T00:12:32.705878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:32.705878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZAiv1G8zSYjrTeFScj0BGQwS3AlLauTJJJlCIxzYxUeKQrDUmN3CoqmyU9M/2CqGMzcOVPVBYDdRZ001l+ZfAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:32.706756Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.08160","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b843bb17ea00845ae090fa17235f06d641dd0a9060eea6ee8c573f1f1ccd1b80","sha256:1bac66a5bfa93d5d592026651d5b2b2f427842f11eb28cdee053c96a064fd4f0"],"state_sha256":"72fe36ab4f044d5fabdc57ee74e170c95f509621830adae176281fd3c172690c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gmSd+reH9HooFvOyMuaBQsjQC/CEkTfrz9DVWogTN3eAUj/HQ9DBJ646HU1lVwwRPdHjHmdMlzk305CRSqRtBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T07:54:59.128332Z","bundle_sha256":"78e787fd56ad45cd5d485a0459e9078d862c25899dec077ce2ed7e93c8c94cd0"}}