Xinlong Li

Retinal vessel segmentation in fundus images is critical for diagnosing microvascular and ophthalmologic diseases. However, the task remains challenging due to significant vessel width variation and low vessel-to-background contrast. To address these limitations, we propose WDM-UNet, a novel spatial-wavelet dual-domain fusion architecture that integrates spatial and wavelet-domain representations to simultaneously enhance the local detail and global context. The encoder combines a Deformable Convolution Encoder (DCE), which adaptively models complex vascular structures through dynamic receptive fields, and a Wavelet Convolution Encoder (WCE), which captures the semantic and structural contexts through low-frequency components and hierarchical wavelet convolution. These features are further refined by a Gated Fusion Transformer (GFT), which employs gated attention to enhance multi-scale feature integration. In the decoder, depthwise separable convolutions are used to reduce the computational overhead without compromising the representational capacity. To preserve fine structural details and facilitate contextual information flow across layers, the model incorporates skip connections with a hierarchical fusion strategy, enabling the effective integration of shallow and deep features. We evaluated WDM-UNet in three public datasets: DRIVE, STARE, and CHASE_DB1. The quantitative evaluations demonstrate that WDM-UNet consistently outperforms state-of-the-art methods, achieving 96.92% accuracy, 83.61% sensitivity, and an 82.87% F1-score in the DRIVE dataset, with superior performance across all the benchmark datasets in both segmentation accuracy and robustness, particularly in complex vascular scenarios.

Identifying and forecasting macro-financial conditions is critical to stabilizing the economy. This study aims to develop a novel methodology for constructing China’s Financial Conditions Index, utilizing monthly data from six major Chinese financial markets (comprising 33 key financial indicators) along with 25 external macroeconomic variables from both China and the United States, spanning January 2002 to June 2024. Although the traditional TVP-FAVAR model can capture the linear relationship in the financial market, it cannot adequately characterize the nonlinear or asymmetric nature of the macro-financial conditions exhibited when major risk events occur at home and abroad. In this paper, we propose an innovative kernel factor-augmented time-varying parameter vector autoregressive model (TVP-KFAVAR), which can better capture the nonlinear nature of the macro-financial situation. It is shown that the TVP-KFAVAR model successfully reflects the impact of major domestic and international risk events on China’s Financial Conditions Index. Meanwhile, the ARIMA model and five machine learning techniques (GRU, LSTM, BiLSTM, TCN and Transformer) are used in this study to predict the Macro-Financial Conditions Index, and it is found that the vast majority of the machine learning techniques outperform the traditional time-series models in terms of forecasting performance. TCN has the outstanding prediction performance under different input configurations. This study can provide policymakers with a powerful tool for macro-financial regulation and risk early warning, and help improve macro-financial management in emerging markets.