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The thermodynamic processes can be classified into reversible and irreversible processes. The processes that can be restored to their initial state are called reversible processes. It is only possible if the process is in quasi-static equilibrium, i.e., it takes place in infinitesimally small steps, and the system remains at equilibrium However, these are ideal processes and do not occur naturally. An ideal system undergoing a reversible process is always in thermodynamic equilibrium within...
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Scalable Stamp Printing and Fabrication of Hemiwicking Surfaces
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深厚的坚固的可逆的水印.

Jiale Chen, Wei Wang, Chongyang Shi

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    此摘要是机器生成的。

    本研究介绍了深度稳定可逆水标 (DRRW),这是一种新的深度学习方法,可以克服传统方法的局限性. 对于嵌入和恢复图像中的水印,DRRW提供了更高的稳定性和效率.

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    科学领域:

    • 计算机科学 计算机科学
    • 信息安全 信息安全
    • 人工智能的人工智能

    背景情况:

    • 现有的强大的可逆水印 (RRW) 方法通常是复杂的,计算成本昂贵,缺乏稳定性.
    • 非深度学习方法需要针对不同图像扭曲的特定任务设计.

    研究的目的:

    • 提出一个基于深度学习的新方案,用于强大的可逆水标 (DRRW).
    • 提高RRW的稳定性,降低计算成本,提高RRW的实际应用性.

    主要方法:

    • 引入了一个整数可逆水印网络 (iIWN),用于整数数据分布的可逆映射.
    • 采用编码器-噪声层-解码器框架,通过端到端的训练来实现对各种扭曲的适应性稳定性.
    • 利用算术编码进行高效的比特流压缩和可逆数据隐藏,以及溢出惩罚损失和自适应重量调整.

    主要成果:

    • 在没有特定任务的设计的情况下,实现了对各种扭曲的适应性强度.
    • 显著减少像素溢出,提高stego图像质量和稳定性.
    • 通过适应性体重调整策略,证明了提高训练稳定性和表现.

    结论:

    • DRRW有效地解决了当前RRW方法中的关键挑战.
    • 拟议的方案显著推进了坚固和可逆水印的实际部署.