Notes

Ultrafast along with High-Yield Polaronic Exciton Dissociation throughout Two-Dimensional Perovskites.
Catch-up patients had significantly larger waist circumference compared to non-catch-up group (median 55cm [RIC52-58] versus median 49.5cm, [RIC46-52] p<0.001).
and
were methylated in all samples. IRS2 was methylated in 60% of SGA patients without difference between groups (p=0.520).

There is no association between IRS2 methylation and catch-up growth among SGA patients.
and
were methylated in all SGA patients. Gene methylation may be implicated in metabolic disease later in life. More studies should be performed to confirm this hypothesis.
There is no association between IRS2 methylation and catch-up growth among SGA patients. LEP and GLP2R were methylated in all SGA patients. Gene methylation may be implicated in metabolic disease later in life. More studies should be performed to confirm this hypothesis.This article tackles the problem of multilabel learning with missing labels. For this problem, it is widely accepted that label correlations can be used to recover the ground-truth label matrix. Most of the existing approaches impose the low-rank assumption on the observed label matrix to exploit label correlations by decomposing it into two matrices, which describe the latent factors of instances and labels, respectively. The quality of these latent factors highly influences the recovery of ground-truth labels and the construction of the multilabel classification model. In this article, we propose recovering the ground-truth label matrix by regularized matrix factorization. Specifically, the latent factors of instances are regularized by the local topological structure derived from the feature space, which can be further used to induce an effective multilabel model. Moreover, the latent factors of labels and the label correlations are mutually adapted via label manifold regularization. In this way, the recovery of the ground-truth label matrix and the construction of the multilabel classification model are optimized jointly and can benefit from the regularized matrix factorization. click here Extensive experimental studies show that the proposed approach significantly outperforms the state-of-the-art algorithms on both full-label and missing-label data.This article focuses on the distributed maximum correntropy filtering issue for general stochastic nonlinear systems subject to deception attacks. The considered nonlinear functions consist of a determined one and a stochastic one, and the stochastic signals sent by deception attacks with identified statistic characteristics could be non-Gaussian. The corresponding calculation formulas of both the filter gains and the upper bound of the filter error covariance are proposed by means of the Taylor series expansion and the fixed-point iterative update rule, where the weighted maximum correntropy criterion is utilized to take the place of traditional minimum covariance indexes. Such an upper bound is only dependent on the local information, neighbor information, and the identified statistics of deception attacks and, therefore, the developed filtering scheme realizes the requirement of distributed calculation. Furthermore, a simplified version is obtained by removing weights in the correntropy criterion. Finally, an illustrative example is given to verify the effectiveness of developed distributed maximum correntropy filtering subject to deception attacks.This article investigates the asymptotic tracking control problem for full-state-constrained nonlinear systems with unknown time-varying powers. By introducing a nonlinear state-dependent transformation, a continuous bounded scalar function, and lower and higher powers into adding a power integrator control design, full-state constraints are skillfully handled without imposing frequently used feasibility conditions in traditional barrier Lyapunov function-based methods, and an asymptotic tracking control design is provided. It is proved that all the closed-loop signals are bounded, full-state constraints are not transgressed, and the asymptotic tracking is achieved.Music information retrieval is of great interest in audio signal processing. However, relatively little attention has been paid to the playing techniques of musical instruments. This work proposes an automatic system for classifying guitar playing techniques (GPTs). Automatic classification for GPTs is challenging because some playing techniques differ only slightly from others. This work presents a new framework for GPT classification it uses a new feature extraction method based on spectral-temporal receptive fields (STRFs) to extract features from guitar sounds. This work applies a supervised deep learning approach to classify GPTs. Specifically, a new deep learning model, called the hierarchical cascade deep belief network (HCDBN), is proposed to perform automatic GPT classification. Several simulations were performed and the datasets of 1) data on onsets of signals; 2) complete audio signals; and 3) audio signals in a real-world environment are adopted to compare the performance. The proposed system improves upon the F-score by approximately 11.47% in setup 1) and yields an F-score of 96.82% in setup 2). The results in setup 3) demonstrate that the proposed system also works well in a real-world environment. These results show that the proposed system is robust and has very high accuracy in automatic GPT classification.During the last two decades, the notion of multiobjective optimization (MOO) has been successfully adopted to solve the nonconvex constrained optimization problems (COPs) in their most general forms. However, such works mainly utilized the Pareto dominance-based MOO framework while the other successful MOO frameworks, such as the reference vector (RV) and the decomposition-based ones, have not drawn sufficient attention from the COP researchers. In this article, we utilize the concepts of the RV-based MOO to design a ranking strategy for the solutions of a COP. We first transform the COP into a biobjective optimization problem (BOP) and then solve it by using the covariance matrix adaptation evolution strategy (CMA-ES), which is arguably one of the most competitive evolutionary algorithms of current interest. We propose an RV-based ranking strategy to calculate the mean and update the covariance matrix in CMA-ES. Besides, the RV is explicitly tuned during the optimization process based on the characteristics of COPs in a RV-based MOO framework.
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