The Reasons for and the Impact of Antidumping Protection: The Case of People's Republic of China

Over the past few decades, the liberalization in international trade has progressed at a rapid speed. While tariffs and quotas have been and continue to be reduced, another type of trade barrier, antidumping, is being used more and more frequently as a measure of protection. This paper focuses on the case of China, explores the characteristics, the reasons for and implications of antidumping. China is the largest targeting economy of antidumping (AD) trade disputes and there is evidence that China is more susceptible to antidumping than other economies, even after controlling for factors such as the non-market economy (NME) status. Our paper analyzes the reasons for China being so broadly and intensively targeted. In particular, the domestic characteristics of exports structure and industrial structures are examined. Our analysis also reveals that foreign direct investment (FDI) may be a significant factor explaining AD cases against China. There is also evidence that low concentration ratios in Chinese industries have contributed to the competitive price and low profit margins. An earlier draft of this paper was presented at the APEC Capacity-Building Workshop on Quantification of NTMs and Trade Facilitation, October 8-10, 2003, in Bangkok, Thailand.

A Sketch-Based Educational System for Learning Chinese Handwriting

Learning Chinese as a Second Language (CSL) is a difficult task for students in English-speaking countries due to the large symbol set and complicated writing techniques. Traditional classroom methods of teaching Chinese handwriting have major drawbacks due to human experts’ bias and the lack of assessment on writing techniques. In this work, we propose a sketch-based educational system to help CSL students learn Chinese handwriting faster and better in a novel way. Our system allows students to draw freehand symbols to answer questions, and uses sketch recognition and AI techniques to recognize, assess, and provide feedback in real time. Results have shown that the system reaches a recognition accuracy of 86% on novice learners’ inputs, higher than 95% detection rate for mistakes in writing techniques, and 80.3% F-measure on the classification between expert and novice handwriting inputs

Economic Effects of Liberalization: The Case of China's Accession to the World Trade Organization

Many developing economies have joined or applied to join the WTO as part of their process of transformation to market-oriented economies. Accession to the WTO involves provisions to liberalize capital markets and to significantly reduce domestic industrial subsidies to the, usually large, state-owned sector. Therefore, any welfare gains derived from such policies are to be considered as part of the welfare gains of trade liberalization. In this paper we develop a dynamic applied general equilibrium model to quantitatively assess the welfare benefits of capital market liberalization and domestic industrial policy reform, and we apply it to the case of China's accession to the WTO. We find that most of China's benefits of accessing the WTO are derived from the reduction of the state-owned sector driven by the reform in domestic policy required by the treaty. The highest welfare benefits occur when both domestic policy reform and capital market liberalization are jointly implemented. Welfare is enhanced by early opening of the capital markets

Does College Education Promote Entrepreneurship in China?

Abstract(#br)There is no consensus on the impact of education on entrepreneurial choice in both theory and empirics. China’s Higher Education Expansion (HEE) policy initiated in 1999 provides us a unique opportunity to identity the causal relationship between college education and entrepreneurship by exploiting the Fuzzy Regression Discontinuity Design (FRDD) approach. In this paper, we use the China Household Income Project (CHIP) 2013 database, finding that China’s HEE policy significantly increases the probability of obtaining college education by 12%. There is suggestive evidence that college education decreases overall and self-employed - type of entrepreneurial choices, but increases boss-type activities; none of the coefficients are precisely estimated, though. Our results are..

Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach

The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs garners significant attention due to its effectiveness in mitigating computing and energy requirements. However, achieving energy-efficient split inference in UAVs remains complex considering of various crucial parameters such as energy level and delay constraints, especially involving multiple tasks. In this paper, we present a two-timescale approach for energy minimization in split inference, where discrete and continuous variables are segregated into two timescales to reduce the size of action space and computational complexity. This segregation enables the utilization of tiny reinforcement learning (TRL) for selecting discrete transmission modes for sequential tasks. Moreover, optimization programming (OP) is embedded between TRL's output and reward function to optimize the continuous transmit power. Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that energy consumption monotonically decreases with increasing transmission time. The replacement significantly reduces the feasible region and enables a fast solution according to the closed-form expression for optimal transmit power. Simulation results show that the proposed algorithm can achieve a higher probability of successful task completion with lower energy consumption

Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19

We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions

Photoinduced coupled twisted intramolecular charge transfer and excited-state proton transfer via intermolecular hydrogen bonding: a DFT/TD-DFT study

We discuss theoretically the geometric and electronic structure properties of the thiazolidinedione derivative A and its hydrogen-bonded complex in dimethylformamide (DMF) solution in the S0 and S1 states. To gain insight into the photoinduced coupled excited-state proton transfer (ESPT) and twisted intramolecular charge transfer (TICT) associated with intermolecular hydrogen bonding, the potential energy profiles are provided along the Osingle bondH bond and the twisted angle. It is predicted that TICT in S1 can facilitate ESPT initiated by intermolecular hydrogen-bond strengthening in the S1 state. The coupling of ESPT and TICT is energetically preferable