Structure-aware contrastive hashing for unsupervised cross-modal retrieval.

Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational efficiency and low storage overhead. However, constructing similarity relationship among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix across modalities. Moreover, we design a multimodal structure-aware alignment network to minimize heterogeneous gap in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.

Development of a supportive care framework for breast cancer survivor's unmet needs: A modified Delphi study.

AIM: To establish a supportive care framework for addressing unmet needs among breast cancer survivors, providing practical guidance for healthcare providers to assess and manage these needs, ultimately enhancing the health outcomes and quality of life of breast cancer survivors. DESIGN: We conducted a two-round Delphi survey to gather expert opinions regarding the unmet needs supportive care framework for breast cancer survivors. METHODS: Initial framework identification and inquiry questionnaire creation was achieved via literature search and expert group discussions, which included 15 experts from nursing practice, clinical medicine, nursing management and nursing education was conducted using a Delphi survey. To establish consensus, a two-round Delphi poll was done, using criteria based on the mean (≥4.0), coefficient of variation (CV < 0.25) and percentage for entire score (≥20%). RESULTS: Experts reached a consensus, leading to six care modules, and 28 care entries: Tumour Detection Support (three care entries), Management of Complications of Antitumor Therapy (seven care entries), Healthy Lifestyle Management (five care entries), Sexual and Fertility Support (four care entries), Psychosocial Support (four care entries) and Resource and Linkage Support (five care entries). CONCLUSION: To address breast cancer survivors' unmet needs, a supportive framework was developed to actively enhance their health outcomes. However, further refinement and feasibility testing using mobile devices or artificial intelligence are required. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: This pioneering framework prioritises addressing unmet needs and equips healthcare providers to assess and manage these needs effectively, facilitating the implementation of programs aimed at improving the well-being of breast cancer survivors. REPORTING METHOD: This study was guided by a modified guideline for the Conducting and Reporting of Delphi Studies (CREDES) (Palliative Medicine, 31(8), 684, 2017). PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution. TRIAL AND PROTOCOL REGISTRATION: The Delphi study methodology does not require registration.

Anchor Graph Network for Incomplete Multiview Clustering.

Incomplete multiview clustering (IMVC) has received extensive attention in recent years. However, existing works still have several shortcomings: 1) some works ignore the correlation of sample pairs in the global structural distribution; 2) many methods are computational expensive, thus cannot be applicable to the large-scale incomplete data clustering tasks; and 3) some methods ignore the refinement of the bipartite graph structure. To address the above issues, we propose a novel anchor graph network for IMVC, which includes a generative model and a similarity metric network. Concretely, the method uses a generative model to construct bipartite graphs, which can mine latent global structure distributions of sample pairs. Later, we use graph convolution network (GCN) with the constructed bipartite graphs to learn the structural embeddings. Notably, the introduction of bipartite graphs can greatly reduce the computational complexity and thus enable our model to handle large-scale data. Unlike previous works based on bipartite graph, our method employs bipartite graphs to guide the learning process in GCNs. In addition, an innovative adaptive learning strategy that can construct robust bipartite graphs is incorporated into our method. Extensive experiments demonstrate that our method achieves the comparable or superior performance compared with the state-of-the-art methods.

Incomplete multi-view clustering network via nonlinear manifold embedding and probability-induced loss.

Incomplete multi-view clustering, which included missing data in different views, is more challenging than multi-view clustering. For the purpose of eliminating the negative influence of incomplete data, researchers have proposed a series of solutions. However, the present incomplete multi-view clustering methods still confront three major issues: (1) The interference of redundant features hinders these methods to learn the most discriminative features. (2) The importance role of local structure is not considered during clustering. (3) These methods fail to utilize data distribution information to guide models update to decrease the effects of outliers and noise. To address above issues, a novel deep clustering network which exerted on incomplete multi-view data was proposed in this paper. We combine multi-view autoencoders with nonlinear manifold embedding method UMAP to extract latent consistent features of incomplete multi-view data. In the clustering method, we introduce Gaussian Mixture Model (GMM) to fit the complex distribution of data and deal with the interference of outliers. In addition, we reasonably utilize the probability distribution information generated by GMM, using probability-induced loss function to integrate feature learning and clustering as a joint framework. In experiments conducted on multiple benchmark datasets, our method captures incomplete multi-view data features effectively and perform excellent.

Low-Rank Graph Completion-Based Incomplete Multiview Clustering.

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views; 2) we append a low-rank constraint to adequately exploit the global structure information among views; and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

Moving Object Localization Based on UHF RFID Phase and Laser Clustering.

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m.