{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AANYLGAP6PSZUVVZUAJVLME6CY","short_pith_number":"pith:AANYLGAP","canonical_record":{"source":{"id":"1905.04392","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T22:22:04Z","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"ed7bdcce2dc5d87af05d4dd04db73e46a338ca1d232d2606e3d3c870c588fbdd","abstract_canon_sha256":"57157e1a05c4435dd1ce61941bf462c68a9d9e142a2b2fff3206f4c89222d2f3"},"schema_version":"1.0"},"canonical_sha256":"001b85980ff3e59a56b9a01355b09e16165ba9f071c3e7aff719943dca15584e","source":{"kind":"arxiv","id":"1905.04392","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04392","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04392v1","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04392","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"AANYLGAP6PSZ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AANYLGAP6PSZUVVZ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AANYLGAP","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AANYLGAP6PSZUVVZUAJVLME6CY","target":"record","payload":{"canonical_record":{"source":{"id":"1905.04392","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T22:22:04Z","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"ed7bdcce2dc5d87af05d4dd04db73e46a338ca1d232d2606e3d3c870c588fbdd","abstract_canon_sha256":"57157e1a05c4435dd1ce61941bf462c68a9d9e142a2b2fff3206f4c89222d2f3"},"schema_version":"1.0"},"canonical_sha256":"001b85980ff3e59a56b9a01355b09e16165ba9f071c3e7aff719943dca15584e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:25.084151Z","signature_b64":"EH8peXzFM/UyqJZw6cTaVyLBeNo79A0uG5h3P9aWUNXrdR6auGjK8Lyq641RLmp+kX68xDjBM8tifgAY9GQjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"001b85980ff3e59a56b9a01355b09e16165ba9f071c3e7aff719943dca15584e","last_reissued_at":"2026-05-17T23:46:25.083489Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:25.083489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.04392","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":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T+jQaaVsBgUU8VMM21FuqGC4Zokt/M3kXrcdX4/+P+IksZpAMzj5WJe5sliBCexsa3LOF1Zh5ydQ9XAxOXhkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:40:06.736501Z"},"content_sha256":"c0e258de674da04e09ae71b2d61118f8a29270cfbd025367d164a892e85d3d79","schema_version":"1.0","event_id":"sha256:c0e258de674da04e09ae71b2d61118f8a29270cfbd025367d164a892e85d3d79"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AANYLGAP6PSZUVVZUAJVLME6CY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","cs.NE"],"primary_cat":"eess.SP","authors_text":"Ismail Alkhouri, Mohsen Joneidi, Nazanin Rahnavard","submitted_at":"2019-05-10T22:22:04Z","abstract_excerpt":"A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum usage is a highly correlated time series. Moreover, there is a correlation for occupancy of spectrum between different frequency channels. Therefore, revealing all these correlations using learning and prediction of one-dimensional time series is not a trivial task. In this paper, we introduce a new framework for representing the spectrum measurements in a tensor format. Next, a time-series prediction method based on CANDECOMP"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04392","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":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UhqNGJJA21e0CcGDm8I7cI4rVSJhEyPzmAiquSWMLemzX2TcWG/N03+sdSWR+Wvcp3+/vs8gxQ8VAodB6c9TAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:40:06.737238Z"},"content_sha256":"fd502b86fcc9f2c36a6cc9947ecb67cb410e0e02d3209c9efdf1d167a8edc2e5","schema_version":"1.0","event_id":"sha256:fd502b86fcc9f2c36a6cc9947ecb67cb410e0e02d3209c9efdf1d167a8edc2e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AANYLGAP6PSZUVVZUAJVLME6CY/bundle.json","state_url":"https://pith.science/pith/AANYLGAP6PSZUVVZUAJVLME6CY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AANYLGAP6PSZUVVZUAJVLME6CY/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":"2026-05-26T02:40:06Z","links":{"resolver":"https://pith.science/pith/AANYLGAP6PSZUVVZUAJVLME6CY","bundle":"https://pith.science/pith/AANYLGAP6PSZUVVZUAJVLME6CY/bundle.json","state":"https://pith.science/pith/AANYLGAP6PSZUVVZUAJVLME6CY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AANYLGAP6PSZUVVZUAJVLME6CY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AANYLGAP6PSZUVVZUAJVLME6CY","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":"57157e1a05c4435dd1ce61941bf462c68a9d9e142a2b2fff3206f4c89222d2f3","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T22:22:04Z","title_canon_sha256":"ed7bdcce2dc5d87af05d4dd04db73e46a338ca1d232d2606e3d3c870c588fbdd"},"schema_version":"1.0","source":{"id":"1905.04392","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04392","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04392v1","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04392","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"AANYLGAP6PSZ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AANYLGAP6PSZUVVZ","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AANYLGAP","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:fd502b86fcc9f2c36a6cc9947ecb67cb410e0e02d3209c9efdf1d167a8edc2e5","target":"graph","created_at":"2026-05-17T23:46:25Z","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":"A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum usage is a highly correlated time series. Moreover, there is a correlation for occupancy of spectrum between different frequency channels. Therefore, revealing all these correlations using learning and prediction of one-dimensional time series is not a trivial task. In this paper, we introduce a new framework for representing the spectrum measurements in a tensor format. Next, a time-series prediction method based on CANDECOMP","authors_text":"Ismail Alkhouri, Mohsen Joneidi, Nazanin Rahnavard","cross_cats":["cs.CV","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T22:22:04Z","title":"Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04392","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:c0e258de674da04e09ae71b2d61118f8a29270cfbd025367d164a892e85d3d79","target":"record","created_at":"2026-05-17T23:46:25Z","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":"57157e1a05c4435dd1ce61941bf462c68a9d9e142a2b2fff3206f4c89222d2f3","cross_cats_sorted":["cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-05-10T22:22:04Z","title_canon_sha256":"ed7bdcce2dc5d87af05d4dd04db73e46a338ca1d232d2606e3d3c870c588fbdd"},"schema_version":"1.0","source":{"id":"1905.04392","kind":"arxiv","version":1}},"canonical_sha256":"001b85980ff3e59a56b9a01355b09e16165ba9f071c3e7aff719943dca15584e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"001b85980ff3e59a56b9a01355b09e16165ba9f071c3e7aff719943dca15584e","first_computed_at":"2026-05-17T23:46:25.083489Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:25.083489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EH8peXzFM/UyqJZw6cTaVyLBeNo79A0uG5h3P9aWUNXrdR6auGjK8Lyq641RLmp+kX68xDjBM8tifgAY9GQjAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:25.084151Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.04392","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0e258de674da04e09ae71b2d61118f8a29270cfbd025367d164a892e85d3d79","sha256:fd502b86fcc9f2c36a6cc9947ecb67cb410e0e02d3209c9efdf1d167a8edc2e5"],"state_sha256":"deb8a8ec7146c49468eeef10b79c33210f319b9155ab624ee1f8fd98d87b2fa7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D6LkyqQGEpe8LPZVh2JaAPxmG8itYL0D0+1y8Pu/GdsCxICHoPcVxh3FukP7T0Sc0bPXRzoHthPH4Oz7VNpbAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:40:06.740954Z","bundle_sha256":"2e59fa4cb7e3f30b05123cadc72f3ed3e2284851038a867914b41541e7d8bf2c"}}