everyDayEnglish

这是一个关于两只掉进深坑的青蛙的故事。面对其他青蛙的消极言语,一只青蛙放弃了努力并死去,而另一只则误解了这些话语为鼓励并最终成功脱困。这个故事提醒我们语言的力量既能给予生命也能带来毁灭。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

                        **TWO FROGS**

A group of frogs were traveling through the woods, and two of them fell into a deep pit. When the other frogs saw how deep the pit was, they told the two frogs that they were as good as dead. The two frogs ignored the comments and tried to jump up out of the pit with all their might. The other frogs kept telling them to stop, that they were as good as dead. Finally, one of the frogs took heed to what the other frogs were saying and gave up. He fell down and died. The other frog continued to jump as hard as he could. Once again, the crowd of frogs yelled at him to stop the pain and just die. He jumped even harder and finally made it out. When he got out, the other frogs said, “Did you not hear us?” The frog explained to them that he was deaf. He thought they were encouraging him the entire time. This story teaches two lessons:

  1. There is power of life and death in the tongue. An encouraging word to someone who is down can lift them up and help them make it through the day.

  2. A destructive word to someone who is down can be what it takes to kill them. Be careful of what you say. Speak life to those who cross your path. The power of words… itis sometimes hard to understand that an encouraging word can go such a long way. Anyone can speak words that tend to rob another of the spirit to continue in difficult times. Special is the individual who will take the time to encourage another.
    词语解释: as good as dead 和死了一样
    He is as good as dead already.
    他和简直和死人一样。
    He was as good as dead after so much exercise.
    这么多训练过后他几乎累得快死了。
    Whoever does not know it and can no longer wonder,no longer marvel,is as good as dead
    任何一个不懂得这点也不再会对此感到神奇、为之惊叹的人,就如行尸走肉一般

内容概要:本文档详细介绍了一个基于MATLAB实现的跨尺度注意力机制(CSA)结合Transformer编码器的多变量时间序列预测项目。项目旨在精准捕捉多尺度时间序列特征,提升多变量时间序列的预测性能,降低模型计算复杂度与训练时间,增强模型的解释性和可视化能力。通过跨尺度注意力机制,模型可以同时捕获局部细节和全局趋势,显著提升预测精度和泛化能力。文档还探讨了项目面临的挑战,如多尺度特征融合、多变量复杂依赖关系、计算资源瓶颈等问题,并提出了相应的解决方案。此外,项目模型架构包括跨尺度注意力机制模块、Transformer编码器层和输出预测层,文档最后提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,尤其是熟悉MATLAB和深度学习的科研人员、工程师和研究生。 使用场景及目标:①需要处理多变量、多尺度时间序列数据的研究和应用场景,如金融市场分析、气象预测、工业设备监控、交通流量预测等;②希望深入了解跨尺度注意力机制和Transformer编码器在时间序列预测中的应用;③希望通过MATLAB实现高效的多变量时间序列预测模型,提升预测精度和模型解释性。 其他说明:此项目不仅提供了一种新的技术路径来处理复杂的时间序列数据,还推动了多领域多变量时间序列应用的创新。文档中的代码示例和详细的模型描述有助于读者快速理解和复现该项目,促进学术和技术交流。建议读者在实践中结合自己的数据集进行调试和优化,以达到最佳的预测效果。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

京之火

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值