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This systematic review aims to synthesize empirical studies investigating the relationship between father involvement and children’s school readiness in East Asia. Both “father involvement” and “school readiness” are multidimensional constructs and literature has been inconsistent in how both constructs should be measured. Due to the educational context in East Asia, “school readiness” has a different connotation in East Asia. This study plans to adopt an inclusive approach in defining father involvement and school readiness and aims to examine , in the context of East Asia, (1) the overall relationships between the two variables, (2) whether any components of father involvement are more strongly related to overall school readiness than other components, (3) whether father involvement is more strongly related to certain school readiness outcomes than others. Related individual, familial, extra-familial and cultural variables will be examined as moderators. Implications for future research, intervention and educational instructions will also be explored.

In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR). This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real datasets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks., Comment: This paper is withdrawn by the authors for some errors

In order to compensate the threshold voltage variation while obtaining the sufficient data charging time, the AMOLED compensative pixel circuit needs to be driven by multi-pulse signals which have continuous high/low level pulses. By using traditional gate driver on array (GOA) with half duty-cycle delay of clock signals, the area of shift register is doubled, increasing the bezel width and power consumptions. In this paper, novel compact integrated gate driver circuit architectures for AMOLED pixel compensation purpose are presented. Outputs with programmable pulse width and number are achieved in these gate driving circuits without consuming extra large areas. Inner feedback technique is adopted in GOA circuit for leakage current cutting of depletion mode IGZO TFTs. The simulation result shows that these circuits work successfully with maximum ±3V threshold voltage shift. These GOA circuits can be used for internal or external Vth compensation techniques of AMOLED panel.