Well-posedness and exponential stability of the inhomogeneous anisotropic incompressible Navier-Stokes equation with far-field vacuum in two-dimensional whole space

In this paper, we investigate the well-posedness theory and exponential stability for the inhomogeneous incompressible Navier-Stokes equation with only horizontal dissipative structure. Due to the lack of the vertical dissipative term and appearance of vacuum, it is a highly challenging tricky problem for us to study the well-posedness, stability and large-time behavior problems in two-dimensional whole space. The local-in-time well-posedness theory is successfully established at first because we develop some good estimates for the density and vorticity to control the nonlinear term. Finally, these good estimates of density and vorticity help us to establish the global-in-time well-posedness and exponential stability if the initial velocity is suitable small

Wireless Deep Speech Semantic Transmission

In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as deep speech semantic transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we build an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semantic features of different importance can be allocated with different coding rates reasonably, which maximizes the system coding gain. Furthermore, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism such that a single model can be applied over various channel states. The end-to-end optimization of our model leads to a flexible rate-distortion (RD) trade-off, supporting versatile wireless speech semantic transmission. Experimental results verify that our DSST system clearly outperforms current engineered speech transmission systems on both objective and subjective metrics. Compared with existing neural speech semantic transmission methods, our model saves up to 75% of channel bandwidth costs when achieving the same quality. An intuitive comparison of audio demos can be found at https://ximoo123.github.io/DSST

Can environmental supervision improve air quality? Quasi-experimental evidence from China

Environmental supervision is significantly disrupted by local economic development and typically characterized by a lack of independence in China. This paper investigates the impacts and mechanisms of the vertical management reform of environmental protection department in China on urban air quality. We construct a principal–agent model suitable for explaining the interactions between the central and local governments and elaborate the intrinsic mechanism of EVM on strengthening environmental supervision. Using manually collected data, we conduct EVM as a quasi-experiment and construct a time-varying difference-in-difference (DID) model. Our empirical results show that the EVM significantly strengthens regional environmental supervision and reduces urban air pollution, bringing abatement in the PM2.5 concentration. The mechanism shows that EVM increases enterprises’ green innovation and attracts new entrants, further promoting industrial upgrading. Our study provides a new perspective on environmental governance and urban air quality in emerging countries such as China