Trade Liberalization and Trade Performance of Environmental Goods: Evidence from Asia-Pacific Economic Cooperation Members

In this article, we study the impact of trade liberalization, including reductions in both tariff and nontariff trade barriers, on environmental goods (EGs) exports. Using bilateral trade data from 20 Asia-Pacific Economic Cooperation members, we find that tariff reduction in an exporting country has a larger positive impact on its exports of EGs than tariff reduction in an importing country. Our results also show that a lower nontariff barrier in an importing country increases its imports of EGs. A considerable amount of heterogeneity also exists in subsample results based on countries’ income levels

Data Detection and Code Channel Allocation for Frequency-Domain Spread ACO-OFDM Systems Over Indoor Diffuse Wireless Channels

Future optical wireless communication systems promise to provide high-speed data transmission in indoor diffuse environments. This paper considers frequency-domain spread asymmetrically clipped optical orthogonal frequency-division multiplexing (ACOOFDM) systems in indoor diffuse channels and aims to develop efficient data detection and code channel allocation schemes. By exploiting the frequency-domain spread concept, a linear multi-code detection scheme is proposed to maximize the signal to interference plus noise ratio (SINR) at the receiver. The achieved SINR and bit error ratio (BER) performance are analyzed. A computationally efficient code channel allocation algorithm is proposed to improve the BER performance of the frequency-domain spread ACO-OFDM system. Numerical results show that the frequency-domain spread ACO-OFDM system outperforms conventional ACO-OFDM systems in indoor diffuse channels. Moreover, the proposed linear multi-code detection and code channel allocation algorithm can improve the performance of optical peak-to-average power ratio (PAPR

Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset