tiple logistic regression models. As result of the very high correlation amongst MLHFQ global score, physical and emotional dimensions we conducted three models, one for each MLHFQ dimension. RESULTS: The mean (SD) age of patients was 76.8 (10.7), and there were a 57.6% of men. At one year we observed 114 deaths (20.5%). Charlson index was divided into three categories, 0-1 (27.9%), 2-3 (38.8%) and greater than 3 (33.3%). The three multivariate models showed that both the total score and MLHFQ subscales, physical and emotional, were significantly associated with the likelihood of 1-year mortality, with an OR (IC95%) of 1.02 (1.01-1.03), 1.05 (1.02-1.08), and 1.04 (1-1.08), respectively. Further, all the adjustment variables resulted significant in the models, showing a higher risk of mortality as the Charlson index category and age increased, and within men. CONCLUSIONS: HRQoL as measured by MLHFQ can be considered as an independent predictor of mortality at 1-year.
A learning rate scheduler is a predefined set of instructions for varying search stepsizes during model training processes. This paper introduces a new logarithmic method using harsh restarting of step sizes through stochastic gradient descent. Cyclical log annealing implements the restart pattern more aggressively to maybe allow the usage of more greedy algorithms on the online convex optimization framework. The algorithm was tested on the CIFAR-10 image datasets, and seemed to perform analogously with cosine annealing on large transformer-enhanced residual neural networks. Future experiments would involve testing the scheduler in generative adversarial networks and finding the best parameters for the scheduler with more experiments.
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