Structure connectivity and substructure connectivity are innovative indicators for assessing network reliability and fault tolerance. Similarly, fault diameter evaluates fault tolerance and transmission delays in networks. This paper extends the concept of fault diameter by introducing two new variants: structure fault diameter and substructure fault diameter, derived from structure connectivity and substructure connectivity respectively. For a connected graph $G$ with $W$-structure connectivity $κ(G;W)$ or $W$-substructure connectivity $κ^s(G;W)$, the $W$-structure fault diameter $D_f(G;W)$ and $W$-substructure fault diameter $D_f^s(G;W)$ are defined as the maximum diameter of any subgraph of $G$ resulting from removing up to $κ(G;W)-1$ $W$-structures or $κ^s(G;W)-1$ $W$-substructures. For the $n$-dimensional hypercube $Q_n$ with $n \geq 3$ and $1 \leq m \leq n - 2$, we determine both $D_f(Q_n;Q_m)$ and $D_f^s(Q_n;Q_1)$. These findings generalize existing results for the diameter and fault diameter of $Q_n$, providing a broader understanding of the hypercube's structural properties under fault conditions.

This paper studies simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided spectrum sharing cell-free (CF) and cognitive radio (CR) combined IoT system, and focuses on enhancing the secondary user’s (SU’s) achievable rate (AR) while guaranteeing the primary user’s lowest AR requirement. Different from the existing studies, we consider a special condition of limited power supply at RIS in this work, in which only a part of the electromagnetic (EM) elements in RIS can function properly due to lack of energy. To this end, we formulate a worst-case SU’s AR optimization problem under both perfect channel state information (CSI) and imperfect CSI conditions. To tackle these two complicated non-convex problems, alternating optimization framework is proposed to jointly optimize the beamformer at primary and secondary transmitters, transmitting/reflecting phase shift as well as EM element selection at STAR-RIS with provable convergence. In particular, semi-definite relaxation (SDR)+successive convex approximation+penalty-convex concave procedure (PCCP) combined algorithm and SDR+PCCP+Dinkelbach combined algorithm are proposed to solve the highly coupled EM element selection and phase shift at RIS under perfect and imperfect CSI cases, respectively. Numerical results verify that given insufficient power supply and RIS, the proposed scheme significantly improves the AR of SUs and outperforms the benchmark schemes of traditional reflecting RIS-aided or no RIS aided case as well as random EM element selection strategies.

Stylistic transformation of artistic images is an important part of the current image processing field. In order to access the aesthetic artistic expression of style images, recent research has applied attention mechanisms to the field of style transfer. This approach transforms style images into tokens by calculating attention and then migrating the artistic style of the image through a decoder. Due to the very low semantic similarity between the original image and the style image, this results in many fine-grained style features being discarded. This can lead to discordant artifacts or obvious artifacts. To address this problem, we propose MccSTN, a novel style representation and transfer framework that can be adapted to existing arbitrary image style transfers. Specifically, we first introduce a feature fusion module (Mccformer) to fuse aesthetic features in style images with fine-grained features in content images. Feature maps are obtained through Mccformer. The feature map is then fed into the decoder to get the image we want. In order to lighten the model and train it quickly, we consider the relationship between specific styles and the overall style distribution. We introduce a multi-scale augmented contrast module that learns style representations from a large number of image pairs.

In the task of image style transfer, accurately capturing style features is crucial. Current methods typically face two main issues: (1) mainstream methods often fail to capture fine-grained features in the image space and tend to lose details, resulting in images with noticeable artificial artifacts. Moreover, the style features extracted from a single image by neural network models are often underutilized, causing local distortions. (2) Due to the localized nature of convolutional neural networks (CNNs), extracting and preserving the global information of the input image becomes challenging. Traditional image style transfer methods suffer from biased content representation. To address these challenges, we propose ConsfomerST, a method based on transformers and augmented contrastive learning that directly learns style representations from large image datasets. This method considers long-range dependencies in input images during the style transfer process. Specifically, we utilize a transformer to process the style and content images into a specific sequence of generated images, followed by optimizing the model using an adaptive augmented contrastive learning method. We further introduce specialized negative sample libraries and sample penalization mechanisms to enhance model training. Qualitative and quantitative evaluations demonstrate that our method outperforms current state-of-the-art techniques.

Zero-shot sketch-based image retrieval (ZS-SBIR) is a great and important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage. Because sketches are extremely abstract, the commonly used backbone networks (such as VGG-16 and ResNet-50) cannot handle both sketches and photos. To solve this problem, we propose a novel and effective feature embedding model called Attention Map Feature Fusion (AMFF). The AMFF model combines the excellent feature extraction capability of the ResNet-50 network with the excellent representation capability of the attention network. By processing the residuals of the Res Net-50 network, the attention map is finally obtained without intro-ducing external semantic knowledge. Most previous approaches treat the ZS-SBIR problem as a classification problem, which ignores the huge domain gap between sketches and photos. This paper proposes an effective method to optimize the entire network, called domain-aware triplets (DAT). Domain feature discrimination and semantic feature embedding can be learned through DAT. In this paper, we also use the classification loss function, which is used to stabilize the training process to avoid getting trapped in a local optimum. Our code and related datasets are publicly available at https://github.com/haizhu12/ammln.git.

Cross-modal hashing is an important direction for multimodal data management and applications, which has recently received more and more attention. Unsupervised cross-modal retrieval does not rely on tag information and is more applicable to the real world. However it still faces some problems. Existing methods mainly encode for local features or global features. Due to the effect of negative samples, it is easy to cause noise interference. To solve these problems, we propose a Multi-view–enhanced modal fusion hashing for Unsupervised cross-modal retrieval (MUCH) to improve these problems. Firstly, we propose a multi-view network. Images inherently contain richer semantics, and we employ a multi-view network to observe the image from different perspectives and obtain the overall and local features of the image. Secondly, we introduce a noise cancellation module to approximate the cross-modal data feature alignment from both intra-modal and cross-modal perspectives before generating the hash code. Finally, we construct a distribution-based similarity weighting matrix to replace the graphical similarity matrix. And we performed multi-view enhancement experiments on JDSH and CIRH, with 1% to 2% enhancement over DAEH on all three datasets.