{"id":"W4220847857","doi":"10.1002/aisy.202200001","title":"Memristors with Initial Low‐Resistive State for Efficient Neuromorphic Systems","year":2022,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Key Laboratory of Advanced Functional Materials of Jiangsu Province; Collaborative Innovation Center of Suzhou Nano Science and Technology; King Abdullah University of Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; State Administration of Foreign Experts Affairs; Technology Agency of the Czech Republic; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Memristor; Initialization; Computer science; Artificial neural network; Resistive touchscreen; Process (computing); Electronic engineering; Computer architecture; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002482706,0.0003453993,0.0004383239,0.0001556712,0.0004505655,0.0000477001,0.0002825421,0.00003193275,0.00001073963],"category_scores_gemma":[0.0000305734,0.0003304879,0.00009243308,0.0003347829,0.00004613595,0.0001047122,0.00007386842,0.0003500229,0.00001541082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003853747,"about_ca_system_score_gemma":0.00003047505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001276282,"about_ca_topic_score_gemma":0.000002069861,"domain_scores_codex":[0.997936,0.0001094165,0.0005594193,0.0004512478,0.0003876246,0.0005563078],"domain_scores_gemma":[0.9989331,0.0003074422,0.0001735734,0.0003389875,0.0001059877,0.0001408935],"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":[0.0002113744,0.00003682082,0.00001123943,0.0003782669,0.00005252409,0.00005757383,0.0003197881,0.9929354,0.004579923,0.0003766028,0.0001404074,0.0009000613],"study_design_scores_gemma":[0.00131134,0.001284128,0.00001989159,0.0003949339,0.00006025859,0.0002947447,0.003122153,0.8788985,0.04427518,0.00005018462,0.06912659,0.001162108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5397571,0.002664314,0.4471125,0.000009962359,0.006613467,0.002380719,0.0002078489,0.0007741037,0.0004800212],"genre_scores_gemma":[0.9983076,0.00002395912,0.0001742448,0.00002393146,0.0001651499,0.0006859947,0.00004198289,0.000115483,0.0004616818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4585505,"threshold_uncertainty_score":0.9999147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02395605729753216,"score_gpt":0.2406948945503204,"score_spread":0.2167388372527883,"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."}}