Challenge of inclusive growth : global capitalization : institutional cost transferring and the economic of being poor = 包容性增長的挑戰: 全球資本化: 制度成本轉嫁與貧困經濟學

My empirical research started with China’s experiment of rural reform during the 1980s-90s. According to the principle of experiment, only through a process of incessant falsification can we approach the truth. In the process my preliminary conclusion was the theory of institutional costs. However in later comparative studies on different countries, I found that institutional cost was not my innovation. Instead my theoretical innovation should be the discovery that the vested interest groups which appropriate the returns of an institutional transition constantly transfer the institutional costs to disadvantaged groups located at a less privileged position in the institutional structure. Therefore I revise my theory as the theory of transfer of institutional costs

Session 1 : Political and economic alternative paradigms : Strategic transformation of ecological civilization and rural revitalization

On Day 1 (13 June 2018), in the session of “Political and Economic Alternative Paradigms”, WEN Tiejun (Renmin University of China) delivered a lecture on Strategic Transformation of Ecological Civilization and Rural Revitalization. The video is produced by Global University for Sustainability, 2018

主权外部性与币缘战略 : 关于发展陷阱的案例研究 = Sovereignty externalities and currency-strategy development trap case studies

本报告以全球化危机向发展中国家转嫁为据，提出两个论点。其一，大多数发展中国家因“成本转嫁”而沉落于发展主义陷阱的根本教训，在于其在向殖民主义宗主国争取国家“合法”独立的交易中，已经形成了失去资源和经济主权的负外部性。其二，当前金融资本时代强权政治控制的币缘战略不同于传统的产业资本时代的地缘战略，并且在以国家为单位的全球竞争框架中，客观地形成了金融主导国家币权为核心的“币权三角”；由此衍生出国家竞争的微笑曲线，乃是制造业国家承担金融化制度成本的内在机制。 这些理论创新，可以解释包括中国在内的一般发展中国家的困境成因。 本文把通过非暴力形式的政治谈判这种“交易”来形成发展中国家独立主权进程中与生俱来的制度性缺陷，称为“主权外部性”；把金融资本阶段凭借政治强权维护世界储备货币地位、并据此向能源和食物市场释放过剩流动性，借以把巨额债务转化为他国通胀危机的“巧实力”运作，称为“币缘战略”。 这两者都是发展中国家难以改出发展陷阱的主要原因，也是金融全球化阶段内在形成国家竞争的微笑曲线的前提条件。 Based on an understanding that the crises of core nations are being transferred to developing countries and thus globalized, two perspectives are highlighted here: first, the development trap most developing countries are caught in due to “cost transfer” by advanced countries can be attributed to the negative externalities of having lost part of their resources and economic sovereignty in the transaction with their former suzerains for gaining “legitimate” independence; second, the currency-strategy of political dominance by the super power in contemporary financial capitalism is different from traditional geo-strategy in the age of industrial capitalism. In the framework of global competition among nation-states, the currency-hegemony triangle is taking shape, which is pivoted on the currency-hegemony of the dominant financial country. We further elaborate the International Competition Smiling Curve to illustrate how manufacturing countries bear the international institutional costs of global financialization. These theoretical innovations can help to elaborate the cause of the predicament developing countries are facing nowadays

Images Speak in Images: A Generalist Painter for In-Context Visual Learning

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing. In addition, Painter significantly outperforms recent generalist models on several challenging tasks.Comment: Accepted to CVPR 2023. Code and model is available at: https://github.com/baaivision/Painte

Single underwater image enhancement with a new optical model

As light is attenuated when disseminating in water, the clarity of images or videos captured under water is usually degraded to varying degrees. By exploring the difference in light attenuation between in atmosphere and in water, we derive a new underwater optical model to describe the formation of an underwater image in the true physical process, and then propose an effective enhancement algorithm with the derived optical model to improve the perception of underwater images or video frames. In our algorithm, a new underwater dark channel is derived to estimate the scattering rate, and an effective method is also presented to estimate the background light in the underwater optical model. Experimental results show that our algorithm can well handle underwater images, especially for deep-sea images and those captured from turbid waters.Engineering, Electrical & ElectronicEICPCI-S(ISTP)

Robust video fingerprinting based on visual attention regions

This paper presents a robust video fingerprinting based on visual attention regions. Video fingerprints, which are a set of short feature vectors, are unique to video clips and used for video identification. The performance of video fingerprinting is usually measured in terms of robustness and accuracy of identification. In our proposed approach, we extract video fingerprints using visual attention regions which remain the same for the perceptually same scenes with different types of distortions and different for different scenes. The experimental results show that the proposed video fingerprinting is effective for constructing video fingerprints that are robust against various content-preserving distortions and accurate in identifying different video clips. ?2009 IEEE.EI

Multi-polarity text segmentation using graph theory

Text segmentation, or named text binarization, is usually an essential step for text information extraction from images and videos. However, most existing text segmentation methods have difficulties in extracting multi-polarity texts, where multi-polarity texts mean those texts with multiple colors or intensities in the same line. In this paper, we propose a novel algorithm for multi-polarity text segmentation based on graph theory. By representing a text image with an undirected weighted graph and partitioning it iteratively, multi-polarity text image can be effectively split into several single-polarity text images. As a result, these text images are then segmented by single-polarity text segmentation algorithms. Experiments on thousands of multi-polarity text images show that our algorithm can effectively segment multi-polarity texts. ? 2008 IEEE.EI