No Self, No Problem(Audiobook) by Chris Niebauer

本文探讨了思考如何创造自我,引用老子、禅宗等哲学思想,深入解析自我与思考的关系,以及如何理解真实自我与内心声音的区别。

Stop thinking, and end your problems. —Lao Tzu, The Tao Te Ching (Stephen Mitchell translation)

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It is the process of thinking that creates the self, rather than there being a self having any independent existence separate from thought.

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For now, the essence of this idea is captured brilliantly by Taoist philosopher and author Wei Wu Wei when he writes, “Why are you unhappy? Because 99.9 percent of everything you think, and of everything you do, is for yourself—and there isn't one.”2

2. Wei, W. W. (1963). Ask the Awakened: the Negative Way. Sentient Publications.

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The mind is a tool. The question is, do you use the tool or does the tool use you? —Zen proverb

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Our association of our true self with the constant voice in our head is an instance of mistaking the map (the voice) for the territory (who we really are).

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Furthermore, our left brain is so tied to the power of words that it is hard to see their effect. Think of an example in your own life when someone said something to you that you found hurtful. You may have suffered greatly, but the truth is that this person was simply sharing an opinion and expressing it via sounds emanating from their voice box. How is it possible that such a thing “hurt” you? Obviously you were hurt by your interpretation of it or the map that these sounds created in your left brain. Next, imagine for a moment if there were no self to hurt? Would words directed at this “you” ever be seen as a problem?

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This is consistent with my view that the self is more like a verb than a noun. It only exists when we think it does, because the process of thinking creates it.

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During a now famous lecture, the Eastern philosopher and spiritual teacher J. Krishnamurti asked the audience “Do you want to know what my secret is?” According to several accounts of this story, in a soft voice, he said, “I don't mind what happens.”

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Advaita Vedanta teacher Nisargadatta Maharaj said, “You are not in the world, but the world is in you. It is only a result of consciousness.”

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Former Harvard professor Richard Alpert (now known as Ram Dass)  said, “All spiritual practices are illusions created by illusionists to escape illusion.”

【电能质量扰动】基于ML和DWT的电能质量扰动分类方法研究(Matlab实现)内容概要:本文研究了一种基于机器学习(ML)和离散小波变换(DWT)的电能质量扰动分类方法,并提供了Matlab实现方案。首先利用DWT对电能质量信号进行多尺度分解,提取信号的时频域特征,有效捕捉电压暂降、暂升、中断、谐波、闪变等常见扰动的关键信息;随后结合机器学习分类器(如SVM、BP神经网络等)对提取的特征进行训练与分类,实现对不同类型扰动的自动识别与准确区分。该方法充分发挥DWT在信号去噪与特征提取方面的优势,结合ML强大的模式识别能力,提升了分类精度与鲁棒性,具有较强的实用价值。; 适合人群:电气工程、自动化、电力系统及其自动化等相关专业的研究生、科研人员及从事电能质量监测与分析的工程技术人员;具备一定的信号处理基础和Matlab编程能力者更佳。; 使用场景及目标:①应用于智能电网中的电能质量在线监测系统,实现扰动类型的自动识别;②作为高校或科研机构在信号处理、模式识别、电力系统分析等课程的教学案例或科研实验平台;③目标是提高电能质量扰动分类的准确性与效率,为后续的电能治理与设备保护提供决策依据。; 阅读建议:建议读者结合Matlab代码深入理解DWT的实现过程与特征提取步骤,重点关注小波基选择、分解层数设定及特征向量构造对分类性能的影响,并尝试对比不同机器学习模型的分类效果,以全面掌握该方法的核心技术要点。
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