{"id":"W4404307335","doi":"10.1109/lra.2024.3497717","title":"Differentiable-Optimization Based Neural Policy for Occlusion-Aware Target Tracking","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Eesti Teadusagentuur","keywords":"Differentiable function; Computer science; Occlusion; Tracking (education); Artificial neural network; Artificial intelligence; Medicine; Psychology; Mathematics; Internal medicine","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":[0.0001558823,0.0001668649,0.0001436112,0.000248368,0.0002834666,0.0008196349,0.0002474537,0.00007392014,0.000009295508],"category_scores_gemma":[0.00002628415,0.0001521576,0.00007451925,0.0003669894,0.00003038096,0.0005516703,0.0000481877,0.0001172525,0.00000427148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003885096,"about_ca_system_score_gemma":0.00004147806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007975982,"about_ca_topic_score_gemma":6.46995e-7,"domain_scores_codex":[0.9988257,0.00004537348,0.0002646801,0.000384899,0.0002085731,0.0002707893],"domain_scores_gemma":[0.9993073,0.0002338731,0.00006822544,0.0002525215,0.00005644446,0.00008158574],"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":[0.000002065608,0.00001391376,0.00004686276,0.00007823029,0.000009111724,0.000006707691,0.0001023504,0.9846222,0.0004877241,0.003826221,0.006357968,0.004446603],"study_design_scores_gemma":[0.0002129074,0.00002234937,0.0001861298,0.00009282549,0.00001171477,0.00001069729,0.000003614593,0.9978344,0.0002475463,0.0003190347,0.0008739983,0.0001847183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002193643,0.00008604614,0.9792705,0.01633971,0.001263864,0.0001979874,0.000026493,0.0006082258,0.00001351155],"genre_scores_gemma":[0.7450747,0.00001600002,0.2512694,0.002994773,0.0004424628,0.00001295056,0.000128672,0.00002640598,0.00003455479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7428811,"threshold_uncertainty_score":0.7903758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424928169205644,"score_gpt":0.2518760457864968,"score_spread":0.2376267640944404,"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."}}