{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ORQGF4PRSBSXNFRPRPFDBT2VSB","short_pith_number":"pith:ORQGF4PR","canonical_record":{"source":{"id":"2306.09789","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T11:59:18Z","cross_cats_sorted":[],"title_canon_sha256":"d84a89ca8d9a2dc79d57451339d7c77d6502c2d34b194bc30de420420932b0e8","abstract_canon_sha256":"1cc755a46bc2cd7b62a8ce9ea6f6bcb5280c40429edd0ab093d1f492834cb21f"},"schema_version":"1.0"},"canonical_sha256":"746062f1f1906576962f8bca30cf559043ab2d4436f982b832c8913c2c319bd2","source":{"kind":"arxiv","id":"2306.09789","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09789","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09789v1","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09789","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_12","alias_value":"ORQGF4PRSBSX","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_16","alias_value":"ORQGF4PRSBSXNFRP","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_8","alias_value":"ORQGF4PR","created_at":"2026-07-05T06:21:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ORQGF4PRSBSXNFRPRPFDBT2VSB","target":"record","payload":{"canonical_record":{"source":{"id":"2306.09789","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T11:59:18Z","cross_cats_sorted":[],"title_canon_sha256":"d84a89ca8d9a2dc79d57451339d7c77d6502c2d34b194bc30de420420932b0e8","abstract_canon_sha256":"1cc755a46bc2cd7b62a8ce9ea6f6bcb5280c40429edd0ab093d1f492834cb21f"},"schema_version":"1.0"},"canonical_sha256":"746062f1f1906576962f8bca30cf559043ab2d4436f982b832c8913c2c319bd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:21:23.950244Z","signature_b64":"D3oRhHnxXlS3i8STHY/anUHgx0HAPL8XtXGVaELREVmzoYGooYLdcY9dIVv4ZWNwuzkoZVdzxBAMiKt7Us1WCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"746062f1f1906576962f8bca30cf559043ab2d4436f982b832c8913c2c319bd2","last_reissued_at":"2026-07-05T06:21:23.949809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:21:23.949809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.09789","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":"2026-07-05T06:21:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AF7nfIHtUnkHz99oD3oKh0SrjTvhl7tBVsEMh5JT7CYa9zf8T2RoOt+JIP5nGRwIethydoxEU27uHF3iluOHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T08:05:07.635336Z"},"content_sha256":"dd65909c1e96a6e86fd6d8810fec4ad97e134ff6455baa10e4750958ef9688da","schema_version":"1.0","event_id":"sha256:dd65909c1e96a6e86fd6d8810fec4ad97e134ff6455baa10e4750958ef9688da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ORQGF4PRSBSXNFRPRPFDBT2VSB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alessio Burrello, Daniele Jahier Pagliari, Enrico Macii, Francesco Daghero, Massimo Poncino, Paolo Montuschi","submitted_at":"2023-06-16T11:59:18Z","abstract_excerpt":"With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes. Decision tree ensembles, such as Random Forests (RFs) and Gradient Boosting (GBTs), are particularly suited for this task, given their relatively low complexity compared to other alternatives. However, their inference time and energy costs are still significant for edge hardware. Given that said costs grow linearly with the ensemble size, this paper proposes the use of dynamic ensembles, that adjust the number of e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09789","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/2306.09789/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:21:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zDrZlI6tpb3uPVt0aoWhgd4BzHs4JmeZlcb2JGgsX0d6McC4lAekwFSAoz6gd3jiwyKwdjYishdLBNfgEwsqCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T08:05:07.635723Z"},"content_sha256":"a0a21a43c318ee4e947d105a1324976840f378b1b1caf0dcd9491d943992e0ba","schema_version":"1.0","event_id":"sha256:a0a21a43c318ee4e947d105a1324976840f378b1b1caf0dcd9491d943992e0ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/bundle.json","state_url":"https://pith.science/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/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":"2026-07-09T08:05:07Z","links":{"resolver":"https://pith.science/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB","bundle":"https://pith.science/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/bundle.json","state":"https://pith.science/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ORQGF4PRSBSXNFRPRPFDBT2VSB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ORQGF4PRSBSXNFRPRPFDBT2VSB","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":"1cc755a46bc2cd7b62a8ce9ea6f6bcb5280c40429edd0ab093d1f492834cb21f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T11:59:18Z","title_canon_sha256":"d84a89ca8d9a2dc79d57451339d7c77d6502c2d34b194bc30de420420932b0e8"},"schema_version":"1.0","source":{"id":"2306.09789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09789","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09789v1","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09789","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_12","alias_value":"ORQGF4PRSBSX","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_16","alias_value":"ORQGF4PRSBSXNFRP","created_at":"2026-07-05T06:21:23Z"},{"alias_kind":"pith_short_8","alias_value":"ORQGF4PR","created_at":"2026-07-05T06:21:23Z"}],"graph_snapshots":[{"event_id":"sha256:a0a21a43c318ee4e947d105a1324976840f378b1b1caf0dcd9491d943992e0ba","target":"graph","created_at":"2026-07-05T06:21:23Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.09789/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes. Decision tree ensembles, such as Random Forests (RFs) and Gradient Boosting (GBTs), are particularly suited for this task, given their relatively low complexity compared to other alternatives. However, their inference time and energy costs are still significant for edge hardware. Given that said costs grow linearly with the ensemble size, this paper proposes the use of dynamic ensembles, that adjust the number of e","authors_text":"Alessio Burrello, Daniele Jahier Pagliari, Enrico Macii, Francesco Daghero, Massimo Poncino, Paolo Montuschi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T11:59:18Z","title":"Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09789","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:dd65909c1e96a6e86fd6d8810fec4ad97e134ff6455baa10e4750958ef9688da","target":"record","created_at":"2026-07-05T06:21:23Z","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":"1cc755a46bc2cd7b62a8ce9ea6f6bcb5280c40429edd0ab093d1f492834cb21f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-16T11:59:18Z","title_canon_sha256":"d84a89ca8d9a2dc79d57451339d7c77d6502c2d34b194bc30de420420932b0e8"},"schema_version":"1.0","source":{"id":"2306.09789","kind":"arxiv","version":1}},"canonical_sha256":"746062f1f1906576962f8bca30cf559043ab2d4436f982b832c8913c2c319bd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"746062f1f1906576962f8bca30cf559043ab2d4436f982b832c8913c2c319bd2","first_computed_at":"2026-07-05T06:21:23.949809Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:21:23.949809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"D3oRhHnxXlS3i8STHY/anUHgx0HAPL8XtXGVaELREVmzoYGooYLdcY9dIVv4ZWNwuzkoZVdzxBAMiKt7Us1WCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:21:23.950244Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.09789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd65909c1e96a6e86fd6d8810fec4ad97e134ff6455baa10e4750958ef9688da","sha256:a0a21a43c318ee4e947d105a1324976840f378b1b1caf0dcd9491d943992e0ba"],"state_sha256":"3a112a8d2d058c4df4d51f09b276830e38e5821e9b6c9a9bbe0db0053cb99f1f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HqwPgYtrzj2pWNmrdHQfE1oaE91AUrFwCmadzRIdUjcyEv2Eg6NHR5F7F9RjXiIGsi9Aj+YqpDRYpQr4ZWZfDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T08:05:07.638163Z","bundle_sha256":"f0cae0017f8931285a537dead84ac9104d4199054f99b6217e27c777e4aabda4"}}