Component Trade and China?s Global Economic Integration

China?s engagement in the so-called international fragmentation of production ? namely ?cross-border dispersion of component production/assembly within vertically integrated manufacturing industries? ? has become an increasingly important form of its economic integration into the regional as well as the global economy. The paper presents the recent trend of trade in parts and components between China and its main trading partners. Applying an adjusted gravity modelling method, the paper explores how China?s pattern of trade in parts and components is being determined. The paper found that China?s rapid economic growth, increasing market size and economies of scale, foreign direct investment and infrastructure development including transportation and telecommunications are important factors in explaining China?s rapid increase of bilateral trade in parts and components with its trading partners. The paper also found that the spatial distance and transportation costs have significant negative impacts on China?s trade of parts and components suggesting that the reduction in transportation costs by technological innovation and investment could enhance trade in parts and components, and thereby deepen the process of international specialization involving China and its main trading partners. The paper argues that given the prospects of the rapid growth of the Chinese economy, its current and planned massive investments in R&D and in infrastructure, its continual policies in attracting FDI and its rapid move towards liberalizing its services sectors including its financial sectors, the scope for China and its trading partners to benefit from the process of international fragmentation of production is tremendous.component trade, international fragmentation of production, gravity model

Semantic Search and Visual Exploration of Computational Notebooks

Code search is an important and frequent activity for developers using computational notebooks (e.g., Jupyter). The flexibility of notebooks brings challenges for effective code search, where classic search interfaces for traditional software code may be limited. In this thesis, we propose, NBSearch, a novel system that supports semantic code search in notebook collections and interactive visual exploration of search results. NBSearch leverages advanced machine learning models to enable natural language search queries and intuitive visualizations to present complicated intra- and inter-notebook relationships in the returned results. We developed NBSearch through an iterative participatory design process with two experts from a large software company. We evaluated the models with a series of experiments and the whole system with a controlled user study. The results indicate the feasibility of our analytical pipeline and the effectiveness of NBSearch to support code search in large notebook collections. As one important aspect of the future directions, the search quality of NBSearch was further improved by incorporating the impact of markdowns in notebooks, and its performance was evaluated by comparing to the original implementation

Research Paper No. 2008/101 Component Trade and China’s Global Economic Integration

China’s engagement in the so-called international fragmentation of production – namely ‘cross-border dispersion of component production/assembly within vertically integrated manufacturing industries ’ – has become an increasingly important form of its economic integration into the regional as well as the global economy. The paper presents the recent trend of trade in parts and components between China and its main trading partners. Applying an adjusted gravity modelling method, the paper explores how China’s pattern of trade in parts and components is being determined. The paper found that China’s rapid economic growth, increasing market size and economies of scale, foreign direct investment and infrastructure development including transportation and telecommunications are important factors in explaining China’s rapid increase of bilateral trade in parts and components with its trading partners. The paper also found that the spatial distance and transportation costs have significant negative impacts on China’s trade of parts and components suggesting that the reduction in transportation costs by technological innovation and investment could enhance trade in parts and components, and thereby deepen the process of international specialization involving China and its main trading partners. The paper argues that given the prospects of the rapid growth of the Chinese economy, its current and planned massive investments in R&D and in infrastructure, its continual policies in attracting FDI and its rapid move towards liberalizing its services sectors including its financial sectors, the scope for China and its trading partners to benefit from the process of international fragmentation of production is tremendous

Experimental Study on the Corrosion of Buried Directly Heating Supply Pipeline Based on the BOTDA (R) Technique

A novel Brillouin Optical Time Domain Analysis sensors, BOTDA(R), has been developed, which can monitor steel corrosion in concrete structures and various insulation structures. The application of distributed BOTDA(R) technology in heating supply pipe corrosion detection is discussed in this paper. According to the characteristics of derictly buried heating supply pipe, we have designed the optical fiber corrosion sensors for the prefabricated pipe, and the sensors were arranged in the junction of pipe section. Accelerated corrosion tests were used to demo the acid soil environment. Quantitative evaluation was derived formula for steel pipe corrosion

Reconstructive Neuron Pruning for Backdoor Defense

Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing defense methods have demonstrated promising results, it is still not clear how to effectively remove backdoor-associated neurons in backdoored DNNs. In this paper, we propose a novel defense called \emph{Reconstructive Neuron Pruning} (RNP) to expose and prune backdoor neurons via an unlearning and then recovering process. Specifically, RNP first unlearns the neurons by maximizing the model's error on a small subset of clean samples and then recovers the neurons by minimizing the model's error on the same data. In RNP, unlearning is operated at the neuron level while recovering is operated at the filter level, forming an asymmetric reconstructive learning procedure. We show that such an asymmetric process on only a few clean samples can effectively expose and prune the backdoor neurons implanted by a wide range of attacks, achieving a new state-of-the-art defense performance. Moreover, the unlearned model at the intermediate step of our RNP can be directly used to improve other backdoor defense tasks including backdoor removal, trigger recovery, backdoor label detection, and backdoor sample detection. Code is available at \url{https://github.com/bboylyg/RNP}.Comment: Accepted by ICML2