Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We presen...
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Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the res...
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Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter...
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Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter-view correspondences in low-resolution (LR) space. The potential of referencing a high-quality SR image of one view benefits the SR for the other is often overlooked, while those with abundant textures contribute to accurate correspondences. Therefore, we propose Reference-based Iterative Interaction (RIISSR), which utilizes reference-based iterative pixel-wise and patch-wise matching, dubbed $P^{2}$ -Matching, to establish cross-view and cross-resolution correspondences for SSR. Specifically, we first design the information perception block (IPB) cascaded in parallel to extract hierarchical contextualized features for different views. Pixel-wise matching is embedded between two parallel IPBs to exploit cross-view interaction in LR space. Iterative patch-wise matching is then executed by utilizing the SR stereo pair as another mutual reference, capitalizing on the cross-scale patch recurrence property to learn high-resolution (HR) correspondences for SSR performance. Moreover, we introduce the supervised side-out modulator (SSOM) to re-weight local intra-view features and produce intermediate SR images, which seamlessly bridge two matching mechanisms. Experimental results demonstrate the superiority of RIISSR against existing state-of-the-art methods.
Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modificati...
Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.
As the IT industry grows rapidly, information security is particularly important. Digital development is a double-edged sword for the medical field, which not only brings convenience to patients and doctors, but also ...
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When using the projection method(or fractional step method)to solve the incompressible Navier-Stokes equations,the projection step involves solving a large-scale pressure Poisson equation(PPE),which is computationally...
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When using the projection method(or fractional step method)to solve the incompressible Navier-Stokes equations,the projection step involves solving a large-scale pressure Poisson equation(PPE),which is computationally expensive and *** this study,a machine learning based method is proposed to solve the large-scale *** machine learning(ML)-block is used to completely or partially(if not sufficiently accurate)replace the traditional PPE iterative solver thus accelerating the solution of the incompressible Navier-Stokes *** ML-block is designed as a multi-scale graph neural network(GNN)framework,in which the original high-resolution graph corresponds to the discrete grids of the solution domain,graphs with the same resolution are connected by graph convolution operation,and graphs with different resolutions are connected by down/up prolongation *** well trained MLblock will act as a general-purpose PPE solver for a certain kind of flow *** proposed method is verified via solving two-dimensional Kolmogorov flows(Re=1000 and Re=5000)with different source *** the premise of achieving a specified high precision(ML-block partially replaces the traditional iterative solver),the ML-block provides a better initial iteration value for the traditional iterative solver,which greatly reduces the number of iterations of the traditional iterative solver and speeds up the solution of the *** experiments show that the ML-block has great advantages in accelerating the solving of the Navier-Stokes equations while ensuring high accuracy.
Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. The production process of firearms components is highly challenging and quality problem...
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Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs a...
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Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models l...
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Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automa...
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Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.
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