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This study examined the impact of research and development (R&D) investment behaviour on the corporate performance of the Taiwanese semiconductor industry, which faced the economic downturn caused by the global financial crisis of 2008, for the period 2005–2016. A dynamic panel data model was used to empirically analyse the impact of R&D intensity on business performance. A generalised method of moments estimator was adopted to avoid endogeneity problems caused by adding dynamics to the model. Further, the model was used to explore the impact of the lag effect of R&D investments on business performance. It was found that significant R&D investments in a given period may reduce business performance in the same period and continue to influence it in the next few periods, thus indicating the presence of a positive and lagged effect of R&D investments in the high-tech industry. Firm size was also found to be positively correlated with business performance, that is, the larger the firm size, the greater is the use of resources for R&D, which, in turn, leads to more sophisticated technologies and profitable outcomes, forming a positive cycle. This indicates that R&D expenditures affect firms’sustainable management.

This study examined the impact of research and development (R&D) investment behaviour on the corporate performance of the Taiwanese semiconductor industry, which faced the economic downturn caused by the global financial crisis of 2008, for the period 2005–2016. A dynamic panel data model was used to empirically analyse the impact of R&D intensity on business performance. A generalised method of moments estimator was adopted to avoid endogeneity problems caused by adding dynamics to the model. Further, the model was used to explore the impact of the lag effect of R&D investments on business performance. It was found that significant R&D investments in a given period may reduce business performance in the same period and continue to influence it in the next few periods, thus indicating the presence of a positive and lagged effect of R&D investments in the high-tech industry. Firm size was also found to be positively correlated with business performance, that is, the larger the firm size, the greater is the use of resources for R&D, which, in turn, leads to more sophisticated technologies and profitable outcomes, forming a positive cycle. This indicates that R&D expenditures affect firms’sustainable management.

Text recognition in natural scene is a challenging problem due to the many factors affecting text appearance. In this paper, we presents a method that directly transcribes scene text images to text without needing of sophisticated character segmentation. We leverage recent advances of deep neural networks to model the appearance of scene text images with temporal dynamics. Specifically, we integrates convolutional neural network (CNN) and recurrent neural network (RNN) which is motivated by observing the complementary modeling capabilities of the two models. The main contribution of this work is investigating how temporal memory helps in an segmentation free fashion for this specific problem. By using long short-term memory (LSTM) blocks as hidden units, our model can retain long-term memory compared with HMMs which only maintain short-term state dependences. We conduct experiments on Street View House Number dataset containing highly variable number images. The results demonstrate the superiority of the proposed method over traditional HMM based methods., Comment: 6 pages, 2 figures, 2 tables

The ore-forming process was investigated of the large deposit, based on the regional Au metallogenic background and the geological characteristics of the mineralisation. The results indicate that Devonian and Carboniferous carbonaceous fine clastic and carbonate formation with relatively high Au background constituted one of the ore resources, that the Indosinian and Yanshanian granitic and syenitic porphyries and relevant dykes provided the heat source and deep ore resource for the mineralisation and that the ring structure formed by EW-, NE- and NW-trending faults eventually led to the formation of the ore resources in the region. Sb and Hg deposits occur outwards around the Au deposit, showing metallogenic zonation and suggesting an epithermal ore-forming association., The ore-forming process was investigated of the large deposit, based on the regional Au metallogenic background and the geological characteristics of the mineralisation. The results indicate that Devonian and Carboniferous carbonaceous fine clastic and carbonate formation with relatively high Au background constituted one of the ore resources, that the Indosinian and Yanshanian granitic and syenitic porphyries and relevant dykes provided the heat source and deep ore resource for the mineralisation and that the ring structure formed by EW-, NE- and NW-trending faults eventually led to the formation of the ore resources in the region. Sb and Hg deposits occur outwards around the Au deposit, showing metallogenic zonation and suggesting an epithermal ore-forming association.