Willlam Shakespeare——1、Toby remembers

Toby讲述了与英国最伟大作家莎士比亚长达三十年的友谊,从一起工作在剧院到共同经历的欢乐与挑战。他回忆了莎士比亚作品在剧院的盛况,以及女王伊丽莎白一世对《第十二夜》的喜爱。同时,Toby表达了对如今剧院关闭,无法继续表演的遗憾。

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My name is Toby.I'm an old man,eighty-three this spring.My house is right in the middle of Stratford-upon-Avon,and I can watch the street market from my window.But I live very quietly now.I'mjust an old man,sitting in a chair.
I once knew the greatest man in England.For thirty years I was his friend.I worked with him in the theatre,through the good times and the bad time.He was a good friend to me.He was also the best playwright,the best poet,that ever lived in England.Will Shakespeare was his name.
I saw all his plays in the theatre.People loved them.They shouted,laughed and cried,ate oranges,and called for more.All kinds of people.kings,Queens,Princes,great lords and ladies,poor people,the boys who held the horses...everyone.Will Shakespeare could please them all.
He put me in a play once.Well,he used my name-Toby.Twelfth Night was the play,I remember.Sir Toby Belch.He was a big fat man,who liked drinking too much and having a good time.Queen Elizabeth the First watched that play-on Twelfth Night,the 6th of January,1601.She liked it,too.
Will's dead now,of course.He's been dead more than thiryt years,and no one sees his plays now.The Puritans have closed all the theatres.There's no singing,no dancing,no plays.It wasn't like that in my young days.We had a good time in London,Will and I...
I've no teeth now,and my hair has all fallen out,but I can still think-and remember.I remember when Will and I were young,just boys really...
内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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