{"id":"W4394951272","doi":"10.1109/twc.2024.3383807","title":"Multi-Task Learning Resource Allocation in Federated Integrated Sensing and Communication Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Resource allocation; Resource management (computing); Resource (disambiguation); Computer network; Distributed computing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001868653,0.0008605665,0.001268195,0.0003910638,0.0004783659,0.001051723,0.001476379,0.001304208,0.001019013],"category_scores_gemma":[0.003283635,0.0004528683,0.0004484579,0.0006598947,0.0009308662,0.001837501,0.001342781,0.001108047,0.0001507743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264769,"about_ca_system_score_gemma":0.001735374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006097388,"about_ca_topic_score_gemma":0.005735435,"domain_scores_codex":[0.9990979,0.000282007,0.00003634307,0.0002334458,0.0001576156,0.0001927114],"domain_scores_gemma":[0.9988756,0.0005835372,0.0001316915,0.0001099885,0.0002129191,0.00008632107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001054621,0.00007311755,0.0004031215,0.00004154447,0.00002738124,0.00005765119,0.00003838112,0.9580539,0.001058269,0.004917661,0.0007227343,0.03450081],"study_design_scores_gemma":[0.0000035191,0.00001082769,0.00003937105,0.000001682567,0.000002503403,0.00000525606,0.000005053344,0.9973477,0.0002023333,0.002306443,0.00007338937,0.000001914751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03785446,0.0003792004,0.9592619,0.0003335409,0.0000438142,0.00003152816,0.00004342397,0.0003613462,0.001690859],"genre_scores_gemma":[0.939485,0.0001349274,0.05804718,0.0001748723,0.00002835807,0.0000712951,0.00005755731,0.00003368558,0.001967103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006097388,"threshold_uncertainty_score":0.01212376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168014581435835,"score_gpt":0.2428917178548287,"score_spread":0.2260902597112452,"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."}}