Generative diffusion models have received increasing attention in medical imaging, particularly in limited-angle computed tomography (LACT). Standard diffusion models achieve high-quality image reconstruction but require a large number of sampling steps during inference, imposing a heavy computational burden. Although skip-sampling strategies have been proposed to improve efficiency, they often lead to loss of fine structural details. To address this issue, we propose a Prior-aware Wavelet Diffusion for Efficient Dental Limited-angle CT Reconstruction (PWD). The PWD enables efficient sampling while preserving reconstruction fidelity in LACT, and effectively mitigates the degradation typically introduced by skip-sampling. Specifically, during the training phase, PWD maps the distribution of LACT images to that of fully sampled target images, enabling the model to learn structural correspondences between them. During inference, the LACT image serves as an prior-aware to guide the sampling trajectory, allowing for high-quality reconstruction with significantly fewer steps. In addition, PWD performs multi-scale feature fusion in the wavelet domain, effectively enhancing the reconstruction of fine details by leveraging both low-frequency and high-frequency information. Quantitative and qualitative evaluations on clinical dental arch CBCT and periapical datasets demonstrate that PWD outperforms existing methods under the same sampling condition. Using only 50 sampling steps, PWD achieves at least 1.7 dB improvement in PSNR and 10% gain in SSIM.

Causal reasoning is vitally important for various natural language processing, which needs text semantic understanding and rich knowledge information reserve. Causal question-answering (CQA), one of the causal reasoning tasks, aims to choose either the cause or effect of a given story sentence. It requires both background causal knowledge and the ability to infer cause-effect relations. However, existing studies ignore the logical and commonsense relationship between the contexts, which limits the model capability. In this paper, we propose a novel model of Semantic Internal-External Enhancement (SIEE) by enhancing both the internal and external knowledge. The model employs Abstract Meaning Representation (AMR) to capture the core semantic information and explicit structures. In addition, we explore the commonsense knowledge behind the key information in the context to provide more clues for reasoning. Finally, we combine the above internal and external information by using a semantic aggregator to aggregate the semantic information of neighbors on the keyword nodes. Experimental studies show the competitive performance of our proposed model over the state-of-the-art published results on three CQA benchmarks, e-CARE, COPA and BCOPA.

Recovering the 3-D representation of anisotropic targets from synthetic aperture radar (SAR) images is an intractable difficulty in the field of SAR target image interpretation. Recently, deep learning-based approaches have made significant progress in classical 2-D vision tasks, such as SAR target recognition and detection. However, due to the lack of spatial and depth information in SAR images, as well as the more complex task modeling and data requirements for 3-D vision tasks, it is difficult to apply deep learning-based approaches to 3-D reconstruction from SAR images. To address these challenges, we reformulate the SAR images 3-D reconstruction as a sequence-to-sequence prediction problem and propose an end-to-end deep learning framework based on a feature hybrid Transformer network, called SAR-3DTR. The proposed method first extracts visual features from SAR image sequences. In addition, considering the unique scattering imaging properties of SAR images, we also extract and construct scattering features, which represent rich physical structure information of target by introducing a novel vector of electromagnetic scattering features (VESFs) module. Finally, Transformer network learns 3-D features and generates model prediction by exploring global correlations in visual, scattering, and 3-D spatial feature domains. Experiments conducted on the synthesized aircraft target dataset and the MSTAR ground target dataset show that our method has superior quantitative and qualitative results and reaches state-of-the-art performance on SAR 3-D reconstruction.

We study the secure distributed matrix multiplication (SDMM) problem under arbitrary collusion pattern. In the one-sided SDMM problem, where only one matrix of the matrix multiplication needs to be kept secure, we propose an achievable scheme that attains the optimal normalized download cost. The optimal scheme distributes a different number of encoded copies to each server, and the servers that collude more with others are given fewer encoded copies. The converse result is proved using Shearer’s lemma. In the two-sided SDMM problem under arbitrary collusion pattern, where the user would want to keep both matrices of the matrix multiplication secure, we provide an achievable scheme whose key parameters, including the method with which the random matrices are appended, the number of random matrices appended, the number of encoded copies generated, the number of encoded copies distributed to each server, are given by the proposed algorithm. We also demonstrate, via numerical results, the performance of the proposed scheme in terms of normalized upload-download cost trade-off, and show that it is much better than the current known scheme devised for the homogeneous collusion pattern.