Zihui Jin

1 Introduction Colloidal semiconductor QDs have been extensively studied over the past four decades as a prominent class of functional nanomaterials, [1-7] finding widespread applications in displays [8], energy conversion [9] and biomedicine [10]. In contrast, magic-sized clusters (MSCs), often emerging as byproducts during QD synthesis, have attracted comparatively limited research attention [11-18].

Abstract Understanding causal mechanisms in deep learning is essential for clinical adoption, where interpretability and reliability are critical. Most existing AI systems act as black boxes, limiting transparency in medicine. We propose a causal inference framework integrated into neural networks to assess the influence of individual features on predictions.

4. Results In order to evaluate the performance of the proposed network, four types of test sets are used for the experiments: (1) NYU2 indoor synthetic hazy images; (2) RESIDE OTS outdoor synthesized hazy images; (3) RTTS real-world hazy images; and (4) real-world forestry scene hazy images.