In recent years, numerous hashing techniques have been developed to boost efficient cross-modal retrieval. Once a retrieval model is deployed, the hash code length is fixed to achieve optimal performance. To address different retrieval scenarios while maintaining retrieval accuracy, a common approach is to redesign and retrain the original model with different hash code length. However, this retraining process can increase the training load and may lead to worse results. To tackle these challenges, we present Regenerated Cross-Modal Hashing (RCMH), a novel cross-modal hashing framework designed to improve the quality of existing hash codes and convert them to arbitrary lengths with high efficiency. First, we clip or pad the existing hash codes to initialize them with the target length, under the supervision of the similarity matrix generated by the augmented label information. Second, we introduce a linear-nonlinear competitive reconstruction approach to reduce the semantic gaps and further capture the deeper relationships from linear image features and nonlinear text features. In this way, each pair of samples is compared and selected to obtain reconstructed binary codes that can preserve the modality-specific properties. Finally, to reduce the training costs caused by iterations of variables, the regenerate hashing term is utilized to regenerate final hash codes with the reconstructed binary codes while preserving the information from the existing hash codes without iterative optimization. Notably, RCMH can be integrated with existing state-of-the-art (SOTA) methods with robustness, helping them to adjust the hash code length and achieve better retrieval performance.

The task of predicting product quality in injection molding aims to forecast quality based on the manufacturing process parameters. Previous works have gravitated toward constructing ANFIS model integrated with heuristic algorithms to relocate the model to the optimal position. However, this constrains the model's own capacity for learning by relying on the heuristic algorithm and depends on time-consuming and costly industrial data. To address these issues, we pro pose an Evolution-ANFIS (EANFIS) framework incorporating the KL-Divergence diffusion model, Self-Evolutionary Search (SES), Neuron Evolutionary Iteration (NEI) and Space Pre Optimization (SPO) modules. Our method is divided into two parts: data generation and model learning. The KL-Divergence diffusion model aligns the distribution between pseudo-sample and original-sample to complete the first stage. The latter stage utilizes multiple ANFIS sub-models and enables these models to autonomously learn deep representations through diverse strategies. Specifically, SES facilitates deep-level feature extraction by employing arc-length strategy to steer sub-models toward optimal feature representations. Furthermore, NEI and SPO are proposed to prevent learning bias, which assists each sub model in escaping suboptimal dilemmas and focuses on learning fine-grained feature mapping. Extensive experiments validate the superiority of our EANFIS approach, even outperforming certain machine learning counterparts. The code is available at https://github.com/hejk/EANFIS.

Spatial transcriptomics (ST) comprehensively measure the gene expression profiles while preserving the spatial information. Accumulated computational frameworks have been proposed to identify spatial domains, one of the fundamental tasks of ST data analysis, to understand the tissue architecture. However, current methods often overlook pathway-level functional context and struggle with data sparsity. Therefore, we develop Path-MGCN, a multi-view graph convolutional network (GCN) with attention mechanism, which integrates pathway information. We first calculate spot-level pathway activity scores via gene set variation analysis from gene expression and construct distinct adjacency graphs representing spatial and functional proximity. A multi-view GCN learns spatial, pathway, and shared embeddings adaptively fused by attention and followed by a Zero-inflated negative binomial decoder to retain the original transcriptome information. Comprehensive evaluations across diverse datasets (human dorsolateral prefrontal cortex, breast cancer and mouse brain) at various resolution demonstrate Path-MGCN's superior accuracy and robustness, significantly outperforming state-of-the-art methods and maintaining high performance across different pathway databases (Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, Reactome). Crucially, Path-MGCN enhances biological interpretability, enabling the identification of Tertiary lymphoid structure-like regions and spatially resolved metabolic heterogeneity (hypoxia, glycolysis, AMP-activated protein kinase signaling) linked to tumor progression stages in human breast cancer. By effectively integrating functional context, Path-MGCN advances ST analysis, providing an accurate and interpretable framework to dissect tissue heterogeneity and enables detailed spatial mapping of molecular pathways that highlights potential targeted therapeutic strategies crucial for developing safe and effective synergistic anti-tumor therapies.

The graph-based multiview clustering has gained significant attention due to its effectiveness in representing complex relationships among multiview data for enhanced clustering. Among the previous graph-based methods, the multiview graph learning (or graph fusion) technique has rapidly emerged as a promising direction, which, however, still suffers from two critical limitations. First, most of previous methods adopt a single-level of graph fusion, which lack the ability to go from single-level graph fusion to multilevel (deep) graph fusion. Second, they generally focus on constructing an optimal unified graph but cannot fully investigate the correlations among multiple views. Therefore, it is difficult to establish a comprehensive and obvious graph structure. In light of this, this article presents a new multiview graph learning method called deep similarity graph fusion (DSGF) for the multiview clustering task, where three pathways are simultaneously leveraged to fuse multilevel similarity into a unified graph. Particularly, multilevel graph fusion is utilized to obtain a view-specific similarity graph for each view and then fuse these single-view graphs (via three levels of graph fusion) into a robust graph, which takes advantage of deeper consensus information between various similarity graphs and improves the quality of the learned graph for the final spectral clustering process. Extensive experiments are conducted on six real-world multiview datasets, which demonstrate the highly competitive clustering performance of DSGF in comparison with state-of-the-art methods.

In Internet of Vehicles (IoV), it is critical to collect sufficient data for model training, to support vehicular intelligent applications. However, the environment of IoV is highly dynamic due to the mobility of vehicles, making it challenging to efficiently allocate resources for data collection. Additionally, timely training of machine learning models with collected data is important for accurate representation in a constantly changing environment. This article aims to improve the performance of model training by collecting sufficient data from vehicles in IoV. A system throughput maximization problem is formulated under the limited bandwidth, storage, and computing resources, which is an NP-hard problem. To solve the problem, an iterative algorithm, namely, the iterative algorithm based on approaching minimum bandwidth (IAMB), is proposed to preferentially collect data from the vehicles with sufficient data and reliable communication quality. Besides, a genetic algorithm, namely, the genetic algorithm based on approaching minimum bandwidth (GAMB), is proposed to further improve the probability of superior individual by replacing operation. We also customize three greedy strategy-based algorithms as the baselines. Extensive experimental results show that our proposed algorithms outperform the baseline algorithms for all cases. Specifically, IAMB and GAMB can improve the throughput by up to 5% and 8%, respectively, compared with baseline algorithms. In addition, the customized genetic algorithm is also superior to the iterative algorithm on performance of system throughput. Moreover, the customized genetic algorithm is more stable than the proposed iterative algorithm in terms of system throughput for model training in the dynamic network environment.

Deep contrastive clustering has recently gained significant attention due to its advantageous ability to leverage the contrastive learning paradigm for joint representation learning and clustering. However, previous deep contrastive clustering approaches mostly focus on instance discrimination or cluster discrimination, which often overlook the rich semantic information latent in the vastintermediatelevels of granularity between instances and clusters. Moreover, they are typically prone to utilizing relationships only within the same level of granularity, e.g., instance-instance relationships and cluster-cluster relationships, but frequently neglect the interactions between different granularity-levels that are ubiquitous in real-world scenarios. To tackle these issues, this paper presents a novel end-to-end deep contrastive clustering approach termed Deep Clustering with Hybrid-Grained Contrastive and Discriminative Learning (DCHL). Particularly, the instance-level contrastive learning and cluster-level contrastive learning are first formulated, where the cluster-level contrastive learning is further split into fine-grained and coarse-grained branches. To capture the global dependencies, the cluster-level contrastiveness is explored on the coarse-grained cluster branch. Meanwhile, to capture the hybrid-grained relationships, the dual-level instance-group discrimination learning is enforced between the instance branch and the fine-grained cluster branch, where the self instance-group discrimination and the cross instance-group discrimination are simultaneously optimized for enhancing the deep clustering performance. Experimental results on five challenging image datasets confirm the superiority of DCHL over state-of-the-art. Code available: https://github.com/dengxiaozhi/DCHL.

Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observations with outlier corruption. The recently proposed tensor ring (TR) model has demonstrated superiority in solving the RTC problem. However, the methods using the TR model either require a preassigned TR rank or aggressively pursue the minimum TR rank, where the latter often leads to biased solutions in the presence of noise. To tackle these bottlenecks, a Bayesian robust TR decomposition (BRTR) method is proposed to give a more accurate solution for the RTC problem, which can avoid exquisite selection of the TR rank and penalty parameters. A variational Bayesian (VB) algorithm is developed to infer the probability distribution of posteriors. During the learning process, BRTR can prune off zero components of core tensors, resulting in automatic TR rank determination. Extensive experiments show that BRTR can achieve significantly improved performance than other state-of-the-art methods.

Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor completion due to its powerful representation ability of high-order tensors. However, most of the existing TR-based methods tend to suffer from deterioration when the selected rank is larger than the true one. To address this issue, this article proposes a new low-rank sparse TR completion method by imposing the Frobenius norm regularization on its latent space. Specifically, we theoretically establish that the proposed method is capable of exploiting the low rankness and Kronecker-basis-representation (KBR)-based sparsity of the target tensor using the Frobenius norm of latent TR-cores. We optimize the proposed TR completion by block coordinate descent (BCD) algorithm and design a modified TR decomposition for the initialization of this algorithm. Extensive experimental results on synthetic data and visual data have demonstrated that the proposed method is able to achieve better results compared to the conventional TR-based completion methods and other state-of-the-art methods and, meanwhile, is quite robust even if the selected TR-rank increases.

This article proposes a novel data reconstruction method, called projective cross-reconstruction (PCR) for cross-domain recognition. The intrinsic philosophy behind PCR is that the data from different domains but with the same label have a strong correlation and thus they can be reconstructed with each other. To this end, we first rearrange the data of source and target domains to form two new cross-data matrices, and the data with the same label but from different domains can be arranged together. Then, we use two different projection matrices to project the new cross-data into two approximate subspaces and perform the cross-reconstruction without introducing any extra matrix as the reconstruction coefficient matrix. This guarantees that the data from different domains can be interlaced well and the data from different domains but sharing the same label can be aligned together. In doing so, the problem of cross-domain distribution mismatch is solved and a discriminative and transferrable feature representation can be obtained. Moreover, PCR integrates the classifier learning and feature representation learning into a unified framework so that these two tasks can be iteratively improved until the termination condition is met. Extensive experiments on six datasets validate the effectiveness of our proposed PCR, compared with the state-of-the-art methods.

Previous work on image clustering focused on seeking a low-dimensional structure from the high-dimensional image data by a shallow linear model, such as sparse subspace clustering (SSC) or low-rank representation (LRR). The recent advance of deep learning shows its superiority via handling data with nonlinear structure, i.e., sparse auto-encoder and independent subspace analysis(ISA), etc. However, most of this type of methods may ignore lots of useful information embedded in the original data. To this end, we propose a novel unsuper-vised learning algorithm via ISA incorporating the subspace structure within data. Specifically, we adopt the ISA to learn local translation invariant feature from data and integrate a prior subspace information into the output of the network simultaneously. This method performs an impressive powerful ability to learn the nature of data. By evaluating on public databases, CMU-PIE and ORL, the experimental results show that the proposed approach achieves better clustering results compared with the state-of-the-art ones.

In recent years, it was one of the currently hot issues, which study the evolution of river way and hydro-system using 3S technology. Aerial photos, TM/ETM+ and SPOT5 imagery were offered to study the evolution of riverway in QingKou region. Automatic detection for change information method such as spectral variation method and false color composite method were used to detect change information, the results showed that the former method was better. Five Rivers in Qingkou Region were selected for our research, change information such as length and width was extracted separately. On this basis, the driving factors of evolution were analyzed. The riverway gradually become steady due to effective river rectification, the influence caused by man-made factors was greater than natural factors in the evolution.

Traditional land cover performance from remote sensing imagery using the statistical characteristics of the pixel have encountered a lot of difficulties in dealing with the issue of classification of high-resolution images. Object-oriented classification techniques based on image segmentation are a good solution to this problem. Qingkou region, the Beijing-Hangzhou Grand Canal and other eight natural rivers junctions, has take break dramatic changes in the last 50 years, therefore, research on land cover performance in this region have great significance. In this paper, object-oriented classification method was used to extract land cover information from SPOT5 imagery in Qingkou region. The imagery was segmented in two scale parameters to create homogeneous objects for different classes. And the overall classification stability is up to more than 87%. It is available to provide research data for land cover performance evaluation in Beijing-Hangzhou Grand Canal and development research on cities along the canal.

Grand Canal of China (GCC) is considered as a significant historical cultural heritage in China. Conservation and management of GCC are becoming an extremely urgent issue. In this paper, a design of GCC conservation integrated information system was designed after analyzing GCC conservation of business requirement. The design focuses on the analysis of the system's overall structure, function model and data model. Practice shows that, GCC conservation integrated information system can play an important role in GCC's heritage data collection, data management, data analysis, conservation planning of GCC and the establishment of dynamic monitoring and management.