{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:6WUYTFGZSQFHVXKK5AZ2YNLS6S","short_pith_number":"pith:6WUYTFGZ","canonical_record":{"source":{"id":"2002.07203","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-17T19:06:05Z","cross_cats_sorted":[],"title_canon_sha256":"400b53db62f643a4b3343e974072454fa0022ea56d517f990fcf0d771ffb71d5","abstract_canon_sha256":"ee24603f01f783ec61b3c1f53e92dc0d10d4eea2218a7df8f5cdf339a37e19ad"},"schema_version":"1.0"},"canonical_sha256":"f5a98994d9940a7add4ae833ac3572f48fcf4d4c6a4d11304a24c24808848925","source":{"kind":"arxiv","id":"2002.07203","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.07203","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"2002.07203v1","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.07203","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"6WUYTFGZSQFH","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_16","alias_value":"6WUYTFGZSQFHVXKK","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_8","alias_value":"6WUYTFGZ","created_at":"2026-07-05T00:41:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:6WUYTFGZSQFHVXKK5AZ2YNLS6S","target":"record","payload":{"canonical_record":{"source":{"id":"2002.07203","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-17T19:06:05Z","cross_cats_sorted":[],"title_canon_sha256":"400b53db62f643a4b3343e974072454fa0022ea56d517f990fcf0d771ffb71d5","abstract_canon_sha256":"ee24603f01f783ec61b3c1f53e92dc0d10d4eea2218a7df8f5cdf339a37e19ad"},"schema_version":"1.0"},"canonical_sha256":"f5a98994d9940a7add4ae833ac3572f48fcf4d4c6a4d11304a24c24808848925","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:14.461784Z","signature_b64":"26ehJmDF8t4rqzvHCsrqsUCJXjC908jOdK7IvZsCAdxQZ1mH5xTQbwV9H5h/Ipecm0fUl0q6rcCQZENUPDRaBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5a98994d9940a7add4ae833ac3572f48fcf4d4c6a4d11304a24c24808848925","last_reissued_at":"2026-07-05T00:41:14.461383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:14.461383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.07203","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":"2026-07-05T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"snH6fTGs/KrkwJRkxzKvI3FZJznGXnmFrvDYrpXMKmoc+5VeOHq2DDV/LNZTeY7g6J3DPyXwk8LCaF3lIkFOCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:06:57.257070Z"},"content_sha256":"e3c5cde2e6eb7caa8b1ad380a4732d982169c44229c7be8fc6686196dcc2e4c6","schema_version":"1.0","event_id":"sha256:e3c5cde2e6eb7caa8b1ad380a4732d982169c44229c7be8fc6686196dcc2e4c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:6WUYTFGZSQFHVXKK5AZ2YNLS6S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multilinear Compressive Learning with Prior Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandros Iosifidis, Dat Thanh Tran, Moncef Gabbouj","submitted_at":"2020-02-17T19:06:05Z","abstract_excerpt":"The recently proposed Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system that takes into account the multidimensional structure of the signals when designing the sensing and feature synthesis components. The key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. Thus, the ability to find such a discriminative tensor subspace and optimize the system to project the signals onto that data manifold play"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07203","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/2002.07203/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":"2026-07-05T00:41:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zpS1guUxPhOEU0Toi0VJZGXS+GxzKUqEMgISLglGB2xG0HHn362TBzcwjj0QAb802mxkffqiGM2+9YssygyyAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:06:57.257453Z"},"content_sha256":"02b073683356b09b0e21126cb54ac4077131e51bfa5a29fee48d9a724d902cb4","schema_version":"1.0","event_id":"sha256:02b073683356b09b0e21126cb54ac4077131e51bfa5a29fee48d9a724d902cb4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/bundle.json","state_url":"https://pith.science/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/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":"2026-07-07T08:06:57Z","links":{"resolver":"https://pith.science/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S","bundle":"https://pith.science/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/bundle.json","state":"https://pith.science/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6WUYTFGZSQFHVXKK5AZ2YNLS6S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6WUYTFGZSQFHVXKK5AZ2YNLS6S","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":"ee24603f01f783ec61b3c1f53e92dc0d10d4eea2218a7df8f5cdf339a37e19ad","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-17T19:06:05Z","title_canon_sha256":"400b53db62f643a4b3343e974072454fa0022ea56d517f990fcf0d771ffb71d5"},"schema_version":"1.0","source":{"id":"2002.07203","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.07203","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"arxiv_version","alias_value":"2002.07203v1","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.07203","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_12","alias_value":"6WUYTFGZSQFH","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_16","alias_value":"6WUYTFGZSQFHVXKK","created_at":"2026-07-05T00:41:14Z"},{"alias_kind":"pith_short_8","alias_value":"6WUYTFGZ","created_at":"2026-07-05T00:41:14Z"}],"graph_snapshots":[{"event_id":"sha256:02b073683356b09b0e21126cb54ac4077131e51bfa5a29fee48d9a724d902cb4","target":"graph","created_at":"2026-07-05T00:41:14Z","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/2002.07203/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The recently proposed Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system that takes into account the multidimensional structure of the signals when designing the sensing and feature synthesis components. The key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. Thus, the ability to find such a discriminative tensor subspace and optimize the system to project the signals onto that data manifold play","authors_text":"Alexandros Iosifidis, Dat Thanh Tran, Moncef Gabbouj","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-17T19:06:05Z","title":"Multilinear Compressive Learning with Prior Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.07203","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:e3c5cde2e6eb7caa8b1ad380a4732d982169c44229c7be8fc6686196dcc2e4c6","target":"record","created_at":"2026-07-05T00:41:14Z","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":"ee24603f01f783ec61b3c1f53e92dc0d10d4eea2218a7df8f5cdf339a37e19ad","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-02-17T19:06:05Z","title_canon_sha256":"400b53db62f643a4b3343e974072454fa0022ea56d517f990fcf0d771ffb71d5"},"schema_version":"1.0","source":{"id":"2002.07203","kind":"arxiv","version":1}},"canonical_sha256":"f5a98994d9940a7add4ae833ac3572f48fcf4d4c6a4d11304a24c24808848925","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5a98994d9940a7add4ae833ac3572f48fcf4d4c6a4d11304a24c24808848925","first_computed_at":"2026-07-05T00:41:14.461383Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:41:14.461383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"26ehJmDF8t4rqzvHCsrqsUCJXjC908jOdK7IvZsCAdxQZ1mH5xTQbwV9H5h/Ipecm0fUl0q6rcCQZENUPDRaBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:41:14.461784Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.07203","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e3c5cde2e6eb7caa8b1ad380a4732d982169c44229c7be8fc6686196dcc2e4c6","sha256:02b073683356b09b0e21126cb54ac4077131e51bfa5a29fee48d9a724d902cb4"],"state_sha256":"acba2d6c57806b203f91e811ab6efee2edd088502cfd9d619826db24a6f341a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CSyldGx4NoSMhLzFDbugyCso+t7lCCVdkgVto1gASjy5evNRC4sE/9VvgZC1SH/sgYYr7pS2afjel6mA2TKVAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:06:57.259389Z","bundle_sha256":"e458dac9a22e08020a0fbe82b57dab77e408ea63e7df0a80512dc0c792a0ed06"}}