{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TDLKQVAO53GKPZZRT6OARCCPXT","short_pith_number":"pith:TDLKQVAO","schema_version":"1.0","canonical_sha256":"98d6a8540eeecca7e7319f9c08884fbcd03dc1bb3049de04b5cdabd87f98b412","source":{"kind":"arxiv","id":"2411.04992","version":1},"attestation_state":"computed","paper":{"title":"Which bits went where? Past and future transfer entropy decomposition with the information bottleneck","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Dani S. Bassett, Kieran A. Murphy, Zhuowen Yin","submitted_at":"2024-11-07T18:57:24Z","abstract_excerpt":"Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the o"},"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":"2411.04992","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-07T18:57:24Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"844946e42128c7ce4a774224e7f5fcd09f8d16840dbc121b9e8b79849abeee6d","abstract_canon_sha256":"86bb6ef624fc2e468f764bd20dcf2fdc41bfd71136df798fafa9deac1c5c123d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:37.330115Z","signature_b64":"rxNhmkIeqj9xhVfGb/tEJb+GvvGfg/NcfsixaDp7bcTK7Hqy4mE/e9je6qNrhH3Yy+pNzT6Ri78lfCkaFWS+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98d6a8540eeecca7e7319f9c08884fbcd03dc1bb3049de04b5cdabd87f98b412","last_reissued_at":"2026-07-05T09:32:37.329610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:37.329610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Which bits went where? Past and future transfer entropy decomposition with the information bottleneck","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Dani S. Bassett, Kieran A. Murphy, Zhuowen Yin","submitted_at":"2024-11-07T18:57:24Z","abstract_excerpt":"Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04992","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.04992/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2411.04992","created_at":"2026-07-05T09:32:37.329672+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04992v1","created_at":"2026-07-05T09:32:37.329672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04992","created_at":"2026-07-05T09:32:37.329672+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDLKQVAO53GK","created_at":"2026-07-05T09:32:37.329672+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDLKQVAO53GKPZZR","created_at":"2026-07-05T09:32:37.329672+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDLKQVAO","created_at":"2026-07-05T09:32:37.329672+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08177","citing_title":"Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT","json":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT.json","graph_json":"https://pith.science/api/pith-number/TDLKQVAO53GKPZZRT6OARCCPXT/graph.json","events_json":"https://pith.science/api/pith-number/TDLKQVAO53GKPZZRT6OARCCPXT/events.json","paper":"https://pith.science/paper/TDLKQVAO"},"agent_actions":{"view_html":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT","download_json":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT.json","view_paper":"https://pith.science/paper/TDLKQVAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04992&json=true","fetch_graph":"https://pith.science/api/pith-number/TDLKQVAO53GKPZZRT6OARCCPXT/graph.json","fetch_events":"https://pith.science/api/pith-number/TDLKQVAO53GKPZZRT6OARCCPXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT/action/storage_attestation","attest_author":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT/action/author_attestation","sign_citation":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT/action/citation_signature","submit_replication":"https://pith.science/pith/TDLKQVAO53GKPZZRT6OARCCPXT/action/replication_record"}},"created_at":"2026-07-05T09:32:37.329672+00:00","updated_at":"2026-07-05T09:32:37.329672+00:00"}