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Although studies have explored the effect of CEOs’ characteristics on product innovation, family backgrounds have received little attention. This study endeavoured to fill this gap by investigating how a CEO’s only-child background impacts product innovation. Integrating the imprinting theory with the upper echelon work, we contend that only-child CEOs have lower motivation to pursue product innovation, as a result of the imprinting from distinctive parenting patterns and the lack of peer competition in their early family life. Based on the Survey Data of ESIEC (Enterprise Survey for Innovation and Entrepreneurship in China), we test our hypotheses using a sample of 639 Chinese small and medium-sized enterprises (SMEs). The empirical results support the negative relationship between CEOs’ only-child imprinting and product innovation. Moreover, we further examine the moderating effects that CEOs’ family financing and CEOs’ faith in effort exerted on the strength of the only-child imprinting. This study extends our understanding of the non-ignorable effect of only-child background on an individual’s later strategic decision, which is necessary and timely given the speedy growing proportion of only children around the world because of falling fertility rates. [ABSTRACT FROM AUTHOR]

Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in least mean square (LMS) regression of traditional neural network NUC methods, which is realized by employing a total variation penalty in the cost function and redesigning the processing architecture. Moreover, an adaptive gated learning rate is presented to further reduce the ghosting artifacts and guarantee fast convergence. The performance of the proposed algorithm is comprehensively investigated with artificially corrupted test sequences and real infrared image sequences, respectively. Experimental results show that the proposed algorithm can effectively accelerate the convergence speed, suppress ghosting artifacts, and promote correction precision. [ABSTRACT FROM AUTHOR]