Multi-view clustering (MVC) has gained widespread recognition as a valuable technique for enhancing clustering performance by harnessing diverse data sources. Nonetheless, current methods mainly concentrate on obtaining consistent information, often ignoring the risk of redundant information across different views. In this study, we propose a novel methodology, called Sufficient Multi-View Clustering (STMVC), which evaluates the multi-view clustering framework through an information-theoretic lens, intending to learn inter-view consistency information while removing redundant information among views. Specifically, we first utilize variational analysis to extract inter-view consistency information, and to further enhance the consistency information and minimize the redundant information between different views, we propose a sufficient representation lower bound. Furthermore, in order to improve the adaptability and generalizability of our proposed approach, we expand the application of STMVC to single-view scenarios and incomplete multi-view scenarios. The STMVC method provides a promising solution to the challenge of multi-view clustering and introduces a fresh perspective for analyzing multi-view data. To validate our model, we conducted a theoretical analysis based on the Bayesian error rate, and experiments on several multi-view datasets and single-view datasets show the outstanding performance of STMVC.

Cleaning high-altitude building windows is traditionally a high-risk task for human workers due to the danger of falls. To enhance safety in facade cleaning and similar tasks, the development of window-cleaning robots has become increasingly essential. However, current robots face challenges in navigating exoskeleton structures due to obstructive beams, limiting their application. This article proposes a novel biped window-cleaning robotic system (BWCRS) designed for exoskeleton structures. The system comprises two key components: a multifunctional biped climbing robot (BCR) and a specialized cleaning robot. The BCR is adept at overcoming obstacles inherent in exoskeleton structures and transitioning across various inclined window surfaces. The BCR adopts a symmetrical construction, and the ends of the feet are equipped with vacuum suction cups and electromagnets, which can switch between different actuators at the geometric base and end of the system. The system is further enhanced by air pressure sensors that monitor the suction cup adhesion in real time, ensuring stable operation. A crucial functionality of the BCR is its manipulation mode, guided by a visual camera. This mode enables precise location and manipulation of the cleaning robot, ensuring effective coverage of all window areas. The mechanical design and mathematical modeling of the BWCRS are thoroughly examined, demonstrating its robust window-cleaning capabilities. The experimental results reveal that the proposed BCR has a favorable window-climbing and manipulation ability and that the BWCRS has a favorable cleaning effect on the exoskeleton-structure windows. Note to Practitioners—This paper is motivated by the problem of window-cleaning for exoskeleton structures, but it also applies to solving window maintenance and inspection. Previous window-cleaning robots were mainly aimed at the facades or surfaces with low obstacles that cannot be applied directly to exoskeleton-structured windows. This paper proposes an exoskeleton-structure window-cleaning method based on a biped window-climbing robot and a cleaning robot. The biped robot can climb over obstacles on the exoskeleton structures and manipulate the cleaning robot to different cleaning areas. Here, we introduce the biped window-climbing robot from the perspective of mechanical design and mathematical kinematics. We then demonstrate multiple obstacle-crossing motion modes of the biped window-climbing robot. Combined with visual-based manipulation, the window-cleaning process for the exoskeleton structures by a biped window-climbing robot integrated with a cleaning robot is demonstrated. The results verify that the proposed design can complete window-cleaning tasks on exoskeleton structures.

Knowledge graph (KG) is of growing significance in enabling explainable recommendations. Recent research works involve constructing propagation-based recommendation models. Nevertheless, most of the current propagation-based recommendation methods cannot explicitly handle the diverse relations of items, resulting in the inability to model the underlying hierarchies and diverse relations, and it is difficult to capture the high-order collaborative information of items to learn premium representation. To address these issues, we leverage hyperbolic dynamic neural networks for knowledge-aware recommendation (KHDNN). Technically speaking, we embed users and items (forming user–item bipartite graphs), along with entities and relations (constituting KGs), into hyperbolic space, followed by encoding these embeddings using an encoder. The encoded embedding is passed through a hyperbolic dynamic filter to explicitly handle relations and model different relational structures. Furthermore, we design a fresh aggregation strategy based on relations to propagate and capture higher-order collaborative signals as well as knowledge associations. Meanwhile, we extract semantic information via a bilateral memory network to fuse item collaborative signals and knowledge associations. Empirical results from four datasets show that KHDNN surpasses cutting-edge baseline methods. Additionally, we demonstrate that the KHDNN can perform knowledge-aware recommendations with complex relations.

Medical image segmentation and classification tasks have become increasing accurate by employing deep neural networks. However, existing convolution neural networks models (CNNs) are challenging to achieve quite satisfactory results as medical objects and backgrounds are usually indistinguishable in spatial-domain images. In comparison, it is easier to analyze complex objects in frequency-domain images as different object information is retained in different frequency components. However, training CNNs in the frequency domain requires complex modification for network architecture. Thus, this paper proposes a method of learning in the frequency domain to train CNNs called Frequency domain attention (FDAM) Workflow, which only requires little parameters rise and modification in CNNs. FDAM utilizes the relationship of intra-class frequency to retain valuable frequency information and suppress trivial ones. Furthermore, to reduce computation, a Gate module is designed for deleting redundant frequency channels by exploiting the relationship of inter-class frequency. The proposed methods can be applied in various CNNs, such as U-Net, ResNet and DenseNet, while accepting frequency-domain data as input. Experiment results show a significant performance improvement compared to original CNNs for retinal vessel segmentation, glaucoma classification and pneumonia classification. Specifically, Gate module can improve accuracy while using less input data size.

Automatic generation of medical reports has gained increasing research interests, as the significant potential to assist physicians in decision-making. Medical reports generated by most existing methods are often inaccurate and inconsistent with clinical practice because these methods ignored multiple views and only focused on a single modal. Therefore, we introduced multi-view and multi-modal images that contain more valuable information into this task to generate more accurate and reliable diagnostic reports, the first attempt in this domain. In this paper, we propose a multi-task method to generate diagnostic reports for multi-view and multi-modal fundus images, which can simultaneously predict retinal-related diseases and diagnostic descriptions. Moreover, a multi-view and multi-modal features fusion module (MVMFF) is designed to fuse visual features extracted from different views and modalities, where channel attention mechanism (CAM) and a 3D convolution module are utilized to improve accuracy. Extensive experiments conducted on an internal dataset demonstrate that our method can generate accurate and reliable diagnostic reports that are clinically relevant.