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In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning (MTL) is an effective inductive bias method to utilize latent shared features and discover the structural relationships from related tasks. Base on this concept, we propose a novel MTL model for multivariate chaotic time-series prediction, which could learn both dynamic-shared and

https://www.selleckchem.com/pr....oducts/act001-dmamcl
5% (95% CI 65.9%-85.5%) with no remarkable difference between groups (P = .67). Likewise, there was no significant difference in 3-year PFS between tumors regardless of p53 or p16 status. However, in a subgroup analysis for 17 patients with a strong expression of EpCAM, AF showed better 3-year PFS than SF (100% vs 54.5%, P = .07). From the current study, it could not be concluded that EpCAM, p16, and p53 were prognostic factors for early-stage glottic cancer after primary radiation therapy. AF might be an appropriate fractionation