Software approaches for resilience of high performance computing systems: a survey

很高兴为您翻译这篇文章。"Software approaches for resilience of high performance computing systems: a survey" 是关于高性能计算系统的软件方法的容错能力的一篇文章，是对目前为止已经发表的关于这个主题的文章进行了一个总结。文章中提到，随着计算机技术的发展，高性能计算系统变得越来越复杂，它们需要更高的可靠性和容错能力来应对各种挑战。文章对这个主题的软件方法进行了一个系统的概述，包括程序自恢复、错误隔离和检测、自动化故障修复、软件容错等。文章还提到了软件容错方法的优缺点，并对未来的研究方向进行了展望。

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Transformer-Based Visual Segmentation: A Survey

Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.

This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.

The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.

Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.

翻译A Survey of Deep Learning Approaches for OCR and Document Understanding这篇文献

收款日期 租期起始日期 租期终止日期 租期单价 租期（月数） 租金
2022-12-25 2023-01-15 2023-04-14 20 3 600
深度学习在OCR和文档理解方面的应用综述

OCR（光学字符识别）和文2023-03-25 2023-04-15 2023-07-14 20 3 636
2023档理解是深度学习的重要应用之一。已经提出了许多深度学习方法来-06-25 2023-07-15 2023-10-14 20 3 673
2023-09应对OCR和文档理解的问题。其中一些最流行的方法包括卷积神经网络（CNN）、-25 2023-10-15 2024-01-14 20 3 711
2023-12-25循环神经网络（RNN）和注意力机制。

CNN被用于OCR中从文本图像中提取 2024-01-15 2024-04-14 20.12 3 605.12
2024-03特征。这些特征被送入分类器中以识别字符。CNN也被用于文档理解，例如-25 2024-04-15 2024-07-14 20.12 3 643.66
2024在文档图像中检测和识别物体。

RNN被用于OCR中识别手写文本。R-06-25 2024-07-15 2024-10-14 20.12 3 682.81
NN可以处理可变长度的输入序列，这使它们适用于识别手写文本。RNN也被2024-09-25 2024-10-15 2025-01-14 20.12 3 722.用于文档理解，例如识别文档的结构并从中提取信息。

注意力机制已被用58
2024-12-25 2025-01-15 2025-04-14 20.34 3 610.22
2025-03-25 2025-04-15 2025-07-14 20.34 于OCR和文档理解中，以聚焦于图像或文档的重要区域。注意力机制允3 649.28
2025-06-25 2025-07-15 2025-10-14 20.许模型有选择性地聚焦于输入的某些部分，这可以提高性能。

总的来说，深度学习已经在OCR和文档理解方面展示了巨大的潜力。然而，仍然34 3 689.01
2025-09-25 2025-10-15 2026-01-14 20.34 3 729.41