{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:Q4R26VA27JLYUX2LFSF25MZLB2","short_pith_number":"pith:Q4R26VA2","canonical_record":{"source":{"id":"1112.5716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-12-24T11:21:26Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"49d977fb46d094c582344ee13ef234e5c0b383d08250c5ee6a0b1ebe95895125","abstract_canon_sha256":"39fb82c0d9b2d57a40eb3ac34e44bc39776d6790ff314198d815fb878e66938a"},"schema_version":"1.0"},"canonical_sha256":"8723af541afa578a5f4b2c8baeb32b0eb7fb87e8d4c485ae7f9f3d39d09e3879","source":{"kind":"arxiv","id":"1112.5716","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1112.5716","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1112.5716v1","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1112.5716","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"Q4R26VA27JLY","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q4R26VA27JLYUX2L","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q4R26VA2","created_at":"2026-05-18T12:26:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:Q4R26VA27JLYUX2LFSF25MZLB2","target":"record","payload":{"canonical_record":{"source":{"id":"1112.5716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-12-24T11:21:26Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"49d977fb46d094c582344ee13ef234e5c0b383d08250c5ee6a0b1ebe95895125","abstract_canon_sha256":"39fb82c0d9b2d57a40eb3ac34e44bc39776d6790ff314198d815fb878e66938a"},"schema_version":"1.0"},"canonical_sha256":"8723af541afa578a5f4b2c8baeb32b0eb7fb87e8d4c485ae7f9f3d39d09e3879","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:59:00.748545Z","signature_b64":"dBTDnue3xViYgGCQdWQkr/8eaLutu10ku5A30sJ1ph3b2nYlkvxMLjkRuzdJyDbjO0cuHbiOPQSnSASRPuGSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8723af541afa578a5f4b2c8baeb32b0eb7fb87e8d4c485ae7f9f3d39d09e3879","last_reissued_at":"2026-05-18T01:59:00.747841Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:59:00.747841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1112.5716","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":"2026-05-18T01:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ijBvaTlx4U4AX8MfQYh7Ho8JOoi+D5X0leeEVnvNqTe0Q2SumaFC7Q4QnhkxffElT9tZ7Oz9CdgRKProXWLtBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T11:29:06.750807Z"},"content_sha256":"25f47ac280749af8e71180e27795f45220cdf09294b4ad246b2cd74d3b315d2a","schema_version":"1.0","event_id":"sha256:25f47ac280749af8e71180e27795f45220cdf09294b4ad246b2cd74d3b315d2a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:Q4R26VA27JLYUX2LFSF25MZLB2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Sparsity-Aware Adaptive Algorithm for Distributed Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Konstantinos Slavakis, Sergios Theodoridis, Symeon Chouvardas, Yannis Kopsinis","submitted_at":"2011-12-24T11:21:26Z","abstract_excerpt":"In this paper, a sparsity-aware adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.5716","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":"2026-05-18T01:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WrR0nDm1KFLwiZ6Q7IFRg1VXMEA1kBM06CB9+kwEHt5SCaWtZaUk4y6AN2hWhRV1iy+S0WpTqzHIKZ9qWiufBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T11:29:06.751154Z"},"content_sha256":"2c7a0331093c009fc3e4e436293e12ff3e40658285431cc6cf2ab2a33e5a97be","schema_version":"1.0","event_id":"sha256:2c7a0331093c009fc3e4e436293e12ff3e40658285431cc6cf2ab2a33e5a97be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q4R26VA27JLYUX2LFSF25MZLB2/bundle.json","state_url":"https://pith.science/pith/Q4R26VA27JLYUX2LFSF25MZLB2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q4R26VA27JLYUX2LFSF25MZLB2/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":"2026-06-03T11:29:06Z","links":{"resolver":"https://pith.science/pith/Q4R26VA27JLYUX2LFSF25MZLB2","bundle":"https://pith.science/pith/Q4R26VA27JLYUX2LFSF25MZLB2/bundle.json","state":"https://pith.science/pith/Q4R26VA27JLYUX2LFSF25MZLB2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q4R26VA27JLYUX2LFSF25MZLB2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:Q4R26VA27JLYUX2LFSF25MZLB2","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":"39fb82c0d9b2d57a40eb3ac34e44bc39776d6790ff314198d815fb878e66938a","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-12-24T11:21:26Z","title_canon_sha256":"49d977fb46d094c582344ee13ef234e5c0b383d08250c5ee6a0b1ebe95895125"},"schema_version":"1.0","source":{"id":"1112.5716","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1112.5716","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1112.5716v1","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1112.5716","created_at":"2026-05-18T01:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"Q4R26VA27JLY","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q4R26VA27JLYUX2L","created_at":"2026-05-18T12:26:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q4R26VA2","created_at":"2026-05-18T12:26:39Z"}],"graph_snapshots":[{"event_id":"sha256:2c7a0331093c009fc3e4e436293e12ff3e40658285431cc6cf2ab2a33e5a97be","target":"graph","created_at":"2026-05-18T01:59:00Z","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":"In this paper, a sparsity-aware adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto th","authors_text":"Konstantinos Slavakis, Sergios Theodoridis, Symeon Chouvardas, Yannis Kopsinis","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-12-24T11:21:26Z","title":"A Sparsity-Aware Adaptive Algorithm for Distributed Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.5716","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":"sha256:25f47ac280749af8e71180e27795f45220cdf09294b4ad246b2cd74d3b315d2a","target":"record","created_at":"2026-05-18T01:59:00Z","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":"39fb82c0d9b2d57a40eb3ac34e44bc39776d6790ff314198d815fb878e66938a","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-12-24T11:21:26Z","title_canon_sha256":"49d977fb46d094c582344ee13ef234e5c0b383d08250c5ee6a0b1ebe95895125"},"schema_version":"1.0","source":{"id":"1112.5716","kind":"arxiv","version":1}},"canonical_sha256":"8723af541afa578a5f4b2c8baeb32b0eb7fb87e8d4c485ae7f9f3d39d09e3879","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8723af541afa578a5f4b2c8baeb32b0eb7fb87e8d4c485ae7f9f3d39d09e3879","first_computed_at":"2026-05-18T01:59:00.747841Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:59:00.747841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dBTDnue3xViYgGCQdWQkr/8eaLutu10ku5A30sJ1ph3b2nYlkvxMLjkRuzdJyDbjO0cuHbiOPQSnSASRPuGSCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:59:00.748545Z","signed_message":"canonical_sha256_bytes"},"source_id":"1112.5716","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25f47ac280749af8e71180e27795f45220cdf09294b4ad246b2cd74d3b315d2a","sha256:2c7a0331093c009fc3e4e436293e12ff3e40658285431cc6cf2ab2a33e5a97be"],"state_sha256":"cf3c720c88b0ffee450b722e0a3f39c5379dd171ad48e366c3c3d384a25cdcdf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QPjHPc0YrAEcEd7rob3vrRN14lPcBa/59TdOVxEKMxqvdB1KPffzEewF/opyfDHZZH8eUT0qFMeBqAOs5fBABw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T11:29:06.753183Z","bundle_sha256":"5eaf8fbd7fa747128ef7db3026b65ca0c56afc478903da706bf5717c2968490f"}}