Risk Communication Mechanisms in China Coping with the Risk of Digital Transformation of Society

In the process of promoting the digital transformation of the society, digital platform companies will transform the previously uncontrollable uncertain damage into controllable uncertain damage by reasonable risk decisions. but at the same time, unreasonable risk decisions will cause new uncertain damage and it is the main source of risk in the digital society. How to motivate multiple risk stakeholders such as government, digital platform companies and the public to jointly make reasonable risk decisions and practices is the dilemma of risk management in digital society. China has opened up the governance of digital platform companies to the government and the public through a dual cycle system of risk decision-making. These Institutional innovations are aimed at transforming in-company business decisions into public decisions negotiated by multiple risk stakeholders through constructing risk communication mechanisms, thereby enhancing the transparency, democracy and accountability of risk decisions. However, there are many problems in the construction of specific communication mechanisms, which hinder the regional development of digital economy in Asia. China should learn from other’s experience and promote the convergence of risk communication mechanisms by more concrete measures

FX Resilience around the World: Fighting Volatile Cross-Border Capital Flows

We show that capital flow (CF) volatility exerts an adverse effect on exchange rate (FX) volatility, regardless of whether capital controls have been put in place. However, this effect can be significantly moderated by certain macroeconomic fundamentals that reflect trade openness, foreign assets holdings, monetary policy easing, fiscal sustainability, and financial development. Passing the threshold levels of these macroeconomic fundamentals, the adverse effect of CF volatility may be negligible. We further construct an intuitive FX resilience measure, which provides an assessment of the strength of a country's exchange rates

A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.Comment: Accepted by CVPRW 202