In-memory dynamic graph processing faces three critical challenges: limited DRAM capacity, inefficient concurrent update/query handling, and vulnerability to crashes. Traditional segment-level systems struggle with write amplification on emerging Storage-Class Memory (SCM), while existing persistent-memory systems suffer from coarse-grained synchronization and high recovery overhead. This study presents the Storage-Class Memory Dynamic Graph (SMDG) processing framework, an architecture-level redesign centered on the block as the atomic unit across storage, concurrency, and recovery. The system addresses these challenges through three key innovations. First, a block-granular storage design organizes adjacency data at fixed-size block granularity on heterogeneous DRAM-SCM architecture, employing buffered batched writes to significantly reduce write amplification while preserving logarithmic update complexity. Second, block-level multi-version concurrency control maintains timestamped block versions under per-vertex read-write synchronization to provide task-ordered snapshot visibility for concurrent queries without copying entire vertices or pages. Third, a block-granular crash recovery protocol with decentralized per-vertex logs enables independent parallel reconstruction, ensuring application-level semantic consistency while achieving substantially faster recovery than sequential approaches. Experimental results validate that this unified block-granular design improves update efficiency, sustains mixed update-query workloads with controlled memory overhead, and accelerates crash recovery compared with prior dynamic graph systems.

Point cloud completion aims to reconstruct complete shapes from partial observations. Although current methods have achieved remarkable performance, they still have some limitations: Supervised methods heavily rely on ground truth, which limits their generalization to real-world datasets due to the synthetic-to-real domain gap. Unsupervised methods require complete point clouds to compose unpaired training data, and weakly-supervised methods need multi-view observations of the object. Existing self-supervised methods frequently produce unsatisfactory predictions due to the limited capabilities of their self-supervised signals. To overcome these challenges, we propose a novel self-supervised point cloud completion method. We design a set of novel self-supervised signals based on multi-view augmentations of the single partial point cloud. Additionally, to enhance the model’s learning ability, we first incorporate Mamba into self-supervised point cloud completion task, encouraging the model to generate point clouds with better quality. Experiments on synthetic and real-world datasets demonstrate that our method achieves state-of-the-art results.

We propose DeformingNet, an effective 3D point cloud completion network. Unlike existing methods that complete partial point cloud by directly learning the morphing function from 2D grids to 3D shapes, which limits the model’s inference capability due to the intrinsic gaps between feature spaces with different dimensions, we design a deforming-based point generator that emulates the deforming from multiple uniform 3D priors (i.e., pre-defined 3D point clouds in cube shape) into 3D shapes. In addition, we design a MAE-based encoder, which introduces the MAE encoder from point-MAE pre-trained on ShapeNet dataset to learn the correlation of local regions and fuse local and global features to enrich the latent representation. The 3D shapes generated by DeformingNet have both accurate local details and faithful global structure with less noise. Experiments demonstrate that our DeformingNet outperforms state-of-the-art methods in terms of quantitative metrics and visual quality.

Integrating a low-resolution hyperspectral image and a high-resolution multispectral image is widely acknowledged as an effective approach for generating a high-resolution hyperspectral image. Recent studies have highlighted the nuclear norm as an efficient method for this problem through the utilization of low-rankness. However, the standard nuclear norm has a limitation due to treating singular values equally. To address this issue, we have incorporated the concept of the weighted nuclear norm from the image denoising problem into hyperspectral image fusion, ensuring the retention of crucial data components. Furthermore, we propose a unified framework which integrates the weighted nuclear norm, a sparse prior, and total variation regularization. This framework utilizes the ℓ1norm of coefficients to promote spatial-spectral sparsity in the fused images, while total variation is employed to preserve the spatial piecewise smooth structure. To efficiently solve the proposed model, we have designed an alternating direction method of multipliers. The experimental results show that our proposed approach surpasses the state-of-the-art methods.

Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying images is a fundamental task in medical image analysis. In principle it is possible to use landmark detection or semantic segmentation for this task, but to work well these require large numbers of labeled data for each anatomical structure and sub-structure of interest. A more universal approach would learn the intrinsic structure from unlabeled images. We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM). SAM generates semantic embeddings for each image pixel that describes its anatomical location or body part. To produce such embeddings, we propose a pixel-level contrastive learning framework. A coarse-to-fine strategy ensures both global and local anatomical information are encoded. Negative sample selection strategies are designed to enhance the embedding's discriminability. Using SAM, one can label any point of interest on a template image and then locate the same body part in other images by simple nearest neighbor searching. We demonstrate the effectiveness of SAM in multiple tasks with 2D and 3D image modalities. On a chest CT dataset with 19 landmarks, SAM outperforms widely-used registration algorithms while only taking 0.23 seconds for inference. On two X-ray datasets, SAM, with only one labeled template image, surpasses supervised methods trained on 50 labeled images. We also apply SAM on whole-body follow-up lesion matching in CT and obtain an accuracy of 91%. SAM can also be applied for improving image registration and initializing CNN weights.

A data-driven strategy for diagnosing the water management failure in a Proton Exchange Membrane Fuel Cell (PEMFC) is proposed in this paper. In the proposed diagnosis approach, individual cell voltages are used as the variables for diagnosis. A dimension reduction tool, named principal component analysis (PCA), is used to extract important feature information from diagnostic variables collected at different time points. The pattern recognition tool, named support vector data description (SVDD), is then used to construct hyperspheres, each of which tightly contains a certain kind of data in the feature space. A multi-classification decision strategy, which considers the size of the hypersphere and the distance from the sample to the hypersphere center, is finally proposed to realize fault detection. The experimental results show that the PEMFC stack water management fault can be successfully diagnosed and distinguished based on the PCA and SVDD multi-classification fault diagnosis strategy.

There is clinical evidence that suppressing the bone structures in Chest X-rays (CXRs) improves diagnostic value, either for radiologists or computer-aided diagnosis. However, bone-free CXRs are not always accessible. We hereby propose a coarse-to-fine CXR bone suppression approach by using structural priors derived from unpaired computed tomography (CT) images. In the low-resolution stage, we use the digitally reconstructed radiograph (DRR) image that is computed from CT as a bridge to connect CT and CXR. We then perform CXR bone decomposition by leveraging the DRR bone decomposition model learned from unpaired CTs and domain adaptation between CXR and DRR. To further mitigate the domain differences between CXRs and DRRs and speed up the learning convergence, we perform all the aboved operations in Laplacian of Gaussian (LoG) domain. After obtaining the bone decomposition result in DRR, we upsample it to a high resolution, based on which the bone region in the original high-resolution CXR is cropped and processed to produce a high-resolution bone decomposition result. Finally, such a produced bone image is subtracted from the original high-resolution CXR to obtain the bone suppression result. We conduct experiments and clinical evaluations based on two benchmarking CXR databases to show that (i) the proposed method outperforms the state-of-the-art unsupervised CXR bone suppression approaches; (ii) the CXRs with bone suppression are instrumental to radiologists for reducing their false-negative rate of lung diseases from 15% to 8%; and (iii) state-of-the-art disease classification performances are achieved by learning a deep network that takes the original CXR and its bone-suppressed image as inputs.

Predictive accuracy has often been used as the main and often only evaluation criterion for the predictive performance of classification or data mining algorithms. In recent years, the area under the ROC (receiver operating characteristics) curve, or simply AUC, has been proposed as an alternative single-number measure for evaluating performance of learning algorithms. We proved that AUC is, in general, a better measure (defined precisely) than accuracy. Many popular data mining algorithms should then be reevaluated in terms of AUC. For example, it is well accepted that Naive Bayes and decision trees are very similar in accuracy. How do they compare in AUC? Also, how does the recently developed SVM (support vector machine) compare to traditional learning algorithms in accuracy and AUC? We will answer these questions. Our conclusions will provide important guidelines in data mining applications on real-world datasets.