ABSTRACT This paper addresses the issue of neglecting the cultural connotations of traditional decorative patterns in image segmentation research. It proposes an image segmentation method for decorative patterns based on the improved DeepLabv3+ model. Images of decorative patterns and symbolic features in the Xiangtang Grottoes are acquired and preprocessed. Extension semantics is used to categorize the decorative patterns and extract semantic features of cultural symbols using representation models. The improved DeepLabv3+ model is used to segment the decorative pattern images. A comparison of the proposed model with classical models indicates the former's superiority in segmentation speed, segmentation performance, and the number of parameters. This method has high segmentation accuracy for extracting decorative patterns from images. This research provides a foundational approach for the modular reconfiguration and service‐oriented reuse of decorative elements within manufacturing systems. By integrating semantic understanding with technical innovation, this method not only enhances the precision of pattern extraction but also supports the creative reinterpretation and digital preservation of traditional decorative designs. The study contributes to bridging artificial intelligence with co‐creative practices in design and cultural heritage.

Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on generative adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on https://github.com/Rss1124/StegOT.

Large language models (LLMs) have revolutionized various AI applications. However, their billions of parameters pose significant challenges for practical deployment. Structured pruning is a hardware-friendly compression technique and receives widespread attention. Nonetheless, existing literature typically targets a single structure of LLMs. We observe that the structure units of LLMs differ in terms of inference cost and functionality. Therefore, pruning a single structure unit in isolation often results in an imbalance between performance and efficiency. In addition, previous works mainly employ a prescribed pruning ratio. Since the significance of LLM modules may vary, it is ideal to distribute the pruning load to a specific structure unit according to its role within LLMs. To address the two issues, we propose a pruning method that targets multiple LLM modules with dynamic pruning ratios. Specifically, we find the intrinsic properties of LLMs can guide us to determine the importance of each module and thus distribute the pruning load on demand, i.e., what to prune and how much to prune. This is achieved by quantifying the complex interactions within LLMs. Extensive experiments on multiple benchmarks and LLM variants demonstrate that our method effectively balances the trade-off between efficiency and performance.

The Kuroshio intrusion into the South China Sea via the Luzon Strait significantly influences regional ocean dynamics. However, predicting this intrusion, especially the Kuroshio Loop Current (KLC), remains challenging due to its complex mesoscale and submesoscale processes. Traditional physical models struggle to capture the nonlinear and multiscale features of the Kuroshio intrusion, while deep learning approaches face challenges in incorporating the essential physical processes that characterize the KLC. To address these challenges, we developed the Kuroshio Intrusion Forecast Network (KIFnet), a multi-prior knowledge guided deep learning model. KIFnet integrates physical oceanographic principles with data-driven predictions, enhancing its ability to capture complex ocean dynamics. KIFnet incorporates an SST-guided SSH prediction module and a vorticity-guided loss function to explicitly model thermal and dynamic features of the KLC, advancing the challenging task of forecasting KLC intrusion events. Experimental results demonstrate the model achieves an accuracy of 88% for KLC intrusion events and provides reliable predictions up to 10 days ahead. Prior limitations in KLC forecasting have constrained SCS climate modeling and marine ecosystem management. KIFnet provides accurate KLC predictions, supporting proactive climate adaptation and sustainable ecosystem strategies.

Teaching unbiased decision-making is crucial for addressing biased decision-making in daily life. Although both raising awareness of personal biases and providing guidance on unbiased decision-making are essential, the latter topics remains under-researched. In this study, we developed and evaluated an AI system aimed at educating individuals on making unbiased decisions using fairness-aware machine learning. In a between-subjects experimental design, 99 participants who were prone to bias performed personal assessment tasks. They were divided into two groups: a) those who received AI guidance for fair decision-making before the task and b) those who received no such guidance but were informed of their biases. The results suggest that although several participants doubted the fairness of the AI system, fair machine guidance prompted them to reassess their views regarding fairness, reflect on their biases, and modify their decision-making criteria. Our findings provide insights into the design of AI systems for guiding fair decision-making in humans.

Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of the StyleGAN, creating meaningful subspaces. We use a multi-armed bandit algorithm to decide the dimensions to explore, focusing on the preferences of the user. Experiments show that our method is more efficient in generating preferred images than the baseline methods. Furthermore, changes in preferred images during image generation or the display of entirely different image styles were observed to provide new inspirations, subsequently altering user preferences. This highlights the dynamic nature of user preferences, which our proposed approach recognizes and enhances.