{"id":"W3091835776","doi":"10.1109/tsp.2020.3029499","title":"Distributed Coding of Quantized Random Projections","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada; Mitsubishi Electric Research Laboratories","keywords":"Computer science; Decoding methods; Algorithm; Distributed source coding; Coding (social sciences); Source code; Signal processing; Rate–distortion theory; Computational complexity theory; Multispectral image; Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Mathematics; Variable-length code; Data compression; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005234479,0.0001495877,0.0002373104,0.00009235751,0.0001404837,0.00003765531,0.0000985609,0.00006944911,0.00003654336],"category_scores_gemma":[0.00000347727,0.0001495731,0.00009998415,0.000438675,0.00004733694,0.0001507841,6.746009e-7,0.0002441452,0.00000602659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002528272,"about_ca_system_score_gemma":0.00003002562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006064544,"about_ca_topic_score_gemma":0.000001643371,"domain_scores_codex":[0.9992275,0.00002511138,0.0002667289,0.0001603298,0.0001561796,0.0001642041],"domain_scores_gemma":[0.9996585,0.00005680999,0.00004937269,0.00008650443,0.00008022398,0.00006864042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003233066,0.00009699968,0.000007416479,0.0002393073,0.0001108526,0.000009429342,0.0008839124,0.4064361,0.5282367,0.00002156723,0.0004953996,0.063139],"study_design_scores_gemma":[0.0005426391,0.00005629373,0.000004565491,0.0001563851,0.00004443471,0.000005180947,0.00008995117,0.422148,0.5765732,0.00004269813,0.0002027989,0.0001338431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01449624,0.0001021535,0.98357,0.00008841352,0.00007556199,0.0001719652,0.0000291033,0.0009753644,0.0004911612],"genre_scores_gemma":[0.9976401,0.00002956283,0.002179998,0.00005135654,0.00003970753,0.00001820144,0.000003298785,0.0000312861,0.000006434762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9831439,"threshold_uncertainty_score":0.6099415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532206475296501,"score_gpt":0.2500441048274705,"score_spread":0.2147220400745055,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}