Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

Current semantic segmentation approaches for point cloud scenes heavily rely on manual labeling, while research on unsupervised semantic segmentation methods specifically for raw point clouds is still in its early stages. Unsupervised point cloud learning poses significant challenges due to the absence of annotation information and the lack of pre-training. The development of effective strategies is crucial in this context. In this paper, we propose a novel prototype library-driven unsupervised point cloud semantic segmentation strategy that utilizes Structure Learning and Consistent Reasoning (P-SLCR). First, we propose a Consistent Structure Learning to establish structural feature learning between consistent points and the library of consistent prototypes by selecting high-quality features. Second, we propose a Semantic Relation Consistent Reasoning that constructs a prototype inter-relation matrix between consistent and ambiguous prototype libraries separately. This process ensures the preservation of semantic consistency by imposing constraints on consistent and ambiguous prototype libraries through the prototype inter-relation matrix. Finally, our method was extensively evaluated on the S3DIS, SemanticKITTI, and Scannet datasets, achieving the best performance compared to unsupervised methods. Specifically, the mIoU of 47.1% is achieved for Area-5 of the S3DIS dataset, surpassing the classical fully supervised method PointNet by 2.5%.

Weakly-supervised point cloud semantic segmentation (WS-PCS) has attracted increasing attention due to the challenge of sparse annotations. A central problem is how to effectively extract informative features from the annotated points, enabling reliable supervision. Although many existing works extend 2D graph convolution to 3D point cloud data, 2D convolution inherently assumes feature localization, which is an assumption that does not hold in point clouds, and lacks consistent semantic offsets. To address this, we propose a novel Bilateral Graph Convolutional (BGC) method, which refines graph edges into two categories: regular edges and offset edges, providing improved guidance for WS-PCS. Firstly, we create the Local Bilateral Relations (LBR) module to learn the relational features of edges in local point cloud graphs, encompassing both regular and offset edges. To the best of our knowledge, we are the first to utilize offset edges to capture irregular semantic offsets in point cloud data. Secondly, we propose the Adaptive Pooling (AP) module, which adaptively pools edge information learned from LBR, enhancing the feature characterization ability by incorporating salient and pervasive features. Finally, we design BGC as BGC-Net and evaluate its performance against recent networks on four datasets, achieving state-of-the-art results.

Class imbalance of medical records is a critical challenge for disease classification in intelligent diagnosis. Existing machine learning algorithms usually assign equal weights to all classes, which may reduce classification accuracy of imbalanced records. In this paper, a new Imbalance Lessened Boosting (IMLBoost) algorithm is proposed to better classify imbalanced medical records, highlighting the contribution of samples in minor classes as well as hard and boundary samples. A tailored Cost-Fitting Loss (CFL) function is proposed to assign befitting costs to these critical samples. The first and second derivations of the CFL are then derived and embedded into the classical XGBoost framework. In addition, some feature analysis skills are utilized to further improve performance of the IMLBoost, which also can speed up the model training. Experimental results on five UCI imbalanced medical datasets have demonstrated the effectiveness of the proposed algorithm. Compared with other existing classification methods, IMLBoost has improved the classification performance in terms of F1-score, G-mean and AUC.

Automatic spine and vertebra segmentation from X-ray spine images is a critical and challenging problem in many computer-aid spinal image analysis and disease diagnosis applications. In this paper, a two-stage automatic segmentation framework for spine X-ray images is proposed, which can firstly locate the spine regions (including backbone, sacrum and ilium) in the coarse stage and then identify eighteen vertebrae (i.e., cervical vertebra 7, thoracic vertebra 1-12 and lumbar vertebra 1-5) with isolate and clear boundary in the fine stage. A novel Attention Gate based dual-pathway Network (AGNet) composed of context and edge pathways is designed to extract semantic and boundary information for segmentation of both spine and vertebra regions. Multi-scale supervision mechanism is applied to explore comprehensive features and an Edge aware Fusion Mechanism (EFM) is proposed to fuse features extracted from the two pathways. Some other image processing skills, such as centralized backbone clipping, patch cropping and convex hull detection are introduced to further refine the vertebra segmentation results. Experimental validations on spine X-ray images dataset and vertebrae dataset suggest that the proposed AGNet achieves superior performance compared with state-of-the-art segmentation methods, and the coarse-to-fine framework can be implemented in real spinal diagnosis systems.