China’s WTO Accession and Regional Economies

Along with the rapid economic growth since China undertook economic reform in 1978, the income gap among Chinese regions has widened. Using CERD, a computable general equilibrium model of the Chinese economy with regional details, this paper investigates the impact of China’s accession to the World Trade Organisation on regional development and finds that, although all regions will gain from the accession, the trend of a widening gap among regions will be reinforced rather than eased. Specifically, the eastern coastal region gains more than the inland regions. The result is robust no matter whether the change in trade balance is left free or fixed, although the scenario with zero change in the trade balance generates a lower overall welfare gain and an even worse regional disparity. A retreat from WTO commitments in tariff cuts in agriculture reduces welfare gains, but could to some degree ameliorate the worsening inequality between rural and urban households and between coastal and inland regions. However, this analysis incorporates only WTO commitments on tariff cuts and does not include commitments on non-tariff barriers. Moreover, it does not model other domestic reforms that may be adopted to offset the adjustment costs of the WTO commitments.International Relations/Trade,

Earmarking of pollution charges and the sub-optimality of the Pigouvian tax

One approach to internalising a negative externality of economic activity is to impose a Pigouvian tax equal to the marginal cost of the externality. However, this approach overlooks the possibility that the tax revenue can be earmarked to correct the externality directly, i.e. financing the environmental protection projects. It is found that a pure Pigouvian tax is usually not an optimal policy. This issue is examined in both partial and general equilibrium, static and dynamic settings. Certain conditions for justifying a pure Pigouvian tax or a fully earmarked tax scheme are developed.Environmental Economics and Policy,

Shelf-life Extension of Leafy Vegetables: Evaluating the Impacts

Crop Production/Industries,

Financial sentiment analysis using FinBERT with application in predicting stock movement

We apply sentiment analysis in financial context using FinBERT, and build a deep neural network model based on LSTM to predict the movement of financial market movement. We apply this model on stock news dataset, and compare its effectiveness to BERT, LSTM and classical ARIMA model. We find that sentiment is an effective factor in predicting market movement. We also propose several method to improve the model.Comment: CS224U projec

What Does the Lewis Turning Point Mean for China? A Computable General Equilibrium Analysis

We apply a computable general equilibrium framework to assess likely impacts of the Lewis turning point on China and the rest of the world. Modeling results suggest that China will probably transition from an abnormal economy to a normal economy with somewhat lower growth but higher inflation, which requires significant revision to the macroeconomic policy framework. China would lose competitiveness in laborintensive activities, its current account surplus should fall but overinvestment risk could rise. These changes in China should help improve other counties current accounts and boost lowcost countries production. The Lewis turning point, however, does not provide automatic solutions to some of the key challenges, such as service sector development and innovation capability. China will need to make serious policy efforts to avoid the socalled middle income trap.Lewis turning point, labor shortage, general equilibrium analysis, normal economy, middleâ€ÂÂincome trap

A Survey on Physical Adversarial Attack in Computer Vision

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions

A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's production. Although some traditional visible copyright techniques are available, they would introduce undesired traces and result in a poor user experience. In this paper, we propose a novel plug-and-play invisible copyright protection method based on defensive perturbation for DNN-based applications (i.e., style transfer). Rather than apply the perturbation to attack the DNNs model, we explore the potential utilization of perturbation in copyright protection. Specifically, we project the copyright information to the defensive perturbation with the designed copyright encoder, which is added to the image to be protected. Then, we extract the copyright information from the encoded copyrighted image with the devised copyright decoder. Furthermore, we use a robustness module to strengthen the decoding capability of the decoder toward images with various distortions (e.g., JPEG compression), which may be occurred when the user posts the image on social media. To ensure the image quality of encoded images and decoded copyright images, a loss function was elaborately devised. Objective and subjective experiment results demonstrate the effectiveness of the proposed method. We have also conducted physical world tests on social media (i.e., Wechat and Twitter) by posting encoded copyright images. The results show that the copyright information in the encoded image saved from social media can still be correctly extracted.Comment: 9 pages, 7 figure

Generalized point configurations in Fqd{\mathbb F}_q^d

In this paper, we generalize \cite{IosevichParshall}, \cite{LongPaths} and \cite{cycles} by allowing the \emph{distance} between two points in a finite field vector space to be defined by a general non-degenerate bilinear form or quadratic form. We prove the same bounds on the sizes of large subsets of \F_q^d for them to contain distance graphs with a given maximal vertex degree, under the more general notion of distance. We also prove the same results for embedding paths, trees and cycles in the general setting.Comment: arXiv admin note: text overlap with arXiv:1802.0646

Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense