Betweenness centrality is one of the key centrality measures in many applications including community detections in biological networks, vulnerability detections in communication networks, misinformation filtering in social networks, etc. The top-K group betweenness centrality problem is to find a group of K nodes from a network so that the total fraction of shortest paths that pass through the K nodes is maximized. Existing studies proposed randomized sampling algorithms for the problem. We notice that the existing studies ensured that, the maximum deviation of the estimated centrality of every group from its expectation is no greater than a small given threshold for all potential groups with no more than K nodes, thereby generating too many samples, as the number of such groups is prohibitively large. In contrast, in this paper we first devise a novel algorithm that enables to estimate the centrality of a tentative group adaptively, and the algorithm immediately stops once the centrality is large enough; otherwise, the algorithm uses more samples to find a better group. We then theoretically show that, even the proposed algorithm uses much less samples, it still can find a performance-guaranteed group with high probability. Experimental results with real-world networks demonstrate that the number of samples used by the proposed algorithm is up to 36 times smaller than the state-of-the-art, while the centrality of the group found by the algorithm is no more than 4.5% smaller than the latter.

Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Nevertheless, traditional methods primarily focus on achieving interdomain alignment through sample-level constraints, such as maximum mean discrepancy (MMD), neglecting three pivotal aspects: 1) the potential of data augmentation; 2) the significance of intradomain alignment; and 3) the design of cluster-level constraints. In this article, we introduce a novel hardness-driven strategy for MDA tasks, named $\mathrm {A}^{3}\mathrm {MDA}$ , which collectively considers these three aspects through adaptive hardness quantification and utilization in both data augmentation and domain alignment. To achieve this, $\mathrm {A}^{3}\mathrm {MDA}$ progressively proposes three adaptive hardness measurements (AHMs), i.e., basic, smooth, and comparative AHMs, each incorporating distinct mechanisms for diverse scenarios. Specifically, basic AHM aims to gauge the instantaneous hardness for each source/target sample. Then, hardness values measured by smooth AHM will adaptively adjust the intensity level of strong data augmentation to maintain compatibility with the model's generalization capacity. In contrast, comparative AHM is designed to facilitate cluster-level constraints. By leveraging hardness values as sample-specific weights, the traditional MMD is enhanced into a weighted-clustered variant, strengthening the robustness and precision of interdomain alignment. As for the often-neglected intradomain alignment, we adaptively construct a pseudo-contrastive matrix (PCM) by selecting harder samples based on the hardness rankings, enhancing the quality of pseudo-labels, and shaping a well-clustered target feature space. Experiments on multiple MDA benchmarks show that $\mathrm {A}^{3}\mathrm {MDA}$ outperforms other methods.

Positron emission tomography (PET) is a widely adopted nuclear imaging technique for early tumor detection and brain disorder diagnosis, while its intrinsic tracer radiation inevitably poses health risks for patients. Recently, to achieve high-quality PET imaging while reducing radiation exposure, numerous methods have been proposed to reconstruct high-quality standard-dose PET (SPET) images from low-dose PET (LPET) images. However, these methods usually overlooked crucial regions and details during the reconstruction, leading to high-frequency distortions in the reconstructed images. To this end, we propose D2GAN, a dual-domain generative adversarial network that utilizes spatial and frequency domain information to mitigate high-frequency disparities, facilitating high-quality PET reconstruction. The core of our approach is the Dual-Domain Learning Block (DLB), comprising a Spatial Domain Learning Block (SDLB) for identifying key regions and details in PET images, and a Frequency Domain Learning Block (FDLB) to further refine these areas by amplifying the high-frequency signals of the image. In addition, we introduce a multi-scale residual block (MSRB) to efficiently extract features at various scales and incorporate a focal frequency loss to encourage the consistency between the reconstructed and the real SPET images in the frequency domain. The DLBs and MSRBs are embedded into a U-shaped structure to form our generator. Furthermore, we apply a patch-based discriminator to enforce the data distribution consistency of the reconstructed PET images. Extensive experiments on two public datasets and an in-house clinical dataset demonstrate that our approach outperforms the state-of-the-art PET reconstruction methods.

Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challenging multi-organ segmentation (MoS). However, the prevailing class-imbalance problem in MoS, caused by the substantial variations in organ size, exacerbates the learning difficulty of the SSL network. To alleviate this issue, we present a two-phase semi-supervised network (BSR-Net) with balanced subclass regularization for MoS. Concretely, in Phase I, we introduce a class-balanced subclass generation strategy based on balanced clustering to effectively generate multiple balanced subclasses from original biased ones according to their pixel proportions. Then, in Phase II, we design an auxiliary subclass segmentation (SCS) task within the multi-task framework of the main MoS task. The SCS task contributes a balanced subclass regularization to the main MoS task and transfers unbiased knowledge to the MoS network, thus alleviating the influence of the class-imbalance problem. Extensive experiments conducted on two publicly available datasets, i.e., the MICCAI FLARE 2022 dataset and the WORD dataset, verify the superior performance of our method compared with other methods.

In this paper, a sensing image super-resolution (SR) reconstruction method is proposed. Sparse dictionary dealing with remote sensing image SR problem is introduced in this work. The sparse dictionary is based on a sparsity model where the dictionary atoms have sparse representation over a basic dictionary. The sparse dictionary consists of two parts: basic dictionary and atom representation matrix. The sparse dictionary leads to compact representation and it is both adaptive and efficient. Furthermore, compared with conventional SR methods, two dictionary pairs, i.e. primitive sparse dictionary pair and residual sparse dictionary pair, are proposed. The primitive sparse dictionary pair is learned to reconstruct initial high-resolution (HR) remote sensing image from a single low-resolution (LR) input. However, the initial HR remote sensing image loses some details compare with the corresponding original HR image completely. Therefore, residual sparse dictionary pair is learned to reconstruct residual information. The proposed method is tested on remote sensing images, and the experimental results indicate that the proposed algorithm can provide substantial improvement in resolution of remote sensing images, and the results are superior in quality to the results produced by other methods.