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      "author": "chenlocus",
      "body": "每天的reward pool是一定的，别人文章获得点赞的多了，你的文章价格就降低了。",
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      "body": "### 原因1：\n\n一个用户一天之内投票的次数越多，他每一票的权重就会被稀释，变得更低。\n所以为你的文章投票的人在之后一定时间段内又给别的文章投了票，这样你的文章所产生的报酬就会变低了，所以会产生下降的情况。\n\n### 原因2：\n\n据说这个波动也可能是由于Steem Dollar和Steem本身的汇率变化。但这个说法的准确性有待考证。相比之下，原因1更站得住脚。",
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      "author": "winniex",
      "body": "谢谢",
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      "body": "对新手很有帮助！谢谢",
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      "body": "这里采用Soundcloud的服务来加载音频\n\n1. 选择一个你希望加载的音频。\n2. 点击音频下方“Share”按钮。\n3. 弹出对话框后选择第二个标签页“Embed”\n4. 根据提示复制如下code\n\n<code>\\<iframe width=\"100%\" height=\"300\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/349105721&amp;color=%23ff5500&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;show_teaser=true&amp;visual=true\"></iframe>\n</code>\n\n插入Steemit后自动会显示如下效果：\n\n<iframe width=\"100%\" height=\"300\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/349105721&amp;color=%23ff5500&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;show_teaser=true&amp;visual=true\"></iframe>",
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      "body": "Steemit完美兼容youtube链接。\n\n直接输入比如 <code>https://www.youtube.com/watch?v=XUs6CznN8pw</code>\n\n即可实现视频展示\n\nhttps://www.youtube.com/watch?v=XUs6CznN8pw",
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      "body": "1. 哥本哈根解释：意识主宰世界。所有物体都处于一个概率的叠加态，一个抛起的硬币即是正面同时也是反面。只有当意识主体对其进行观测后，概率波函数瞬间压缩成一个确定的值，转变为确定的正面或反面。\n\n2. 多宇宙解释：所有概率同时发生。当我们掷一个硬币以后，同时存在两个互不相干的世界，一个世界硬币为正面，另一个世界为反面，两个世界无法相互观测。如同x轴和y轴两个一维世界，它们互相在彼此世界的投影为0。\n\n3. 隐变量解释：本质上是在反对量子力学的不确定性，声称这种不确定性是由于某种没有被发现的隐变量产生的。",
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      "body": "Don Norman的design in the business world读书笔记\n\n![](https://steemitimages.com/DQmWqsCvccyrDBTcEEAGSo7T3YQ1yoUsi1yYCfenWDM7ySv/image.png)\n\n## 竞争压力\n\n由于竞争者的存在，无法按照设计思维的方式完整地进行迭代。为了比竞争者更早一步占领市场，不得不将一些初期阶段的产品推向市场。这样的压力不仅仅是对创业公司，对大公司也同样存在。\n\n此外，为了保密产品不得不减少从用户那里获取反馈，保密的目的一是为了避免竞争者抄袭，二是为了避免顾客得知了新产品特性而不再购买现有产品。需要思考的是这些做法究竟是不是对的。\n\n## 需求泛滥\n\n在发布了一款用户导向的完美产品后，问题也没有停止。这个时候一定会遇到的问题是需求泛滥。\n\n现有的用户很喜欢你们的产品，他们会提出更多的需求。\n竞争对手在他们的产品中加入了新功能，你不想在竞争中落入下风。\n市场逐渐饱和，是时候开发新特性来吸引用户升级了。\n需求泛滥不可避免，它在一定程度上会让你的产品比你最初的版本更强大但更复杂。\n\n比较好的应对需求泛滥的做法是：不盲目跟随，集中在强项上而不是弱项。真正好的产品不应该专注在竞争者的压力上，而是始终落实在用户的真实需求上。盲目地应对与接受泛滥的需求，或许会获得短期的用户增长，但是从长远角度其实会使你的产品更趋同，容易在长期上流失用户。\n\n## 新技术\n\n技术会驱使设计做出改变，但人本质的需求很少会变化。我们需要重视新技术带来改变的可能性。\n\n## 推出一个新产品需要多久\n\n技术发展变化非常迅速，但是人和文化的变化却没那么快。大部分现代技术都会面临这样的规律：用较短的时间发明出了新技术，却需要用相当长的时间使这项技术被人们所接受，然后经历过更长的时间后人们才会逐渐不再使用或遗忘这项技术。\n\n大部分基于新技术的产品会因为上述的规律而失败。大公司是不能容忍失败的，所以一般是创业公司一头扑向了这些新领域，成功的也是其中的一小部分。\n\n总而言之，一个产品从想法诞生到成功一定会经历很长一个过程。\n\n## 两种创新：渐进与颠覆\n\n这两种主要的创新方式，渐进式创新遵循一种更自然的更新过程，而颠覆式创新则是一种阶跃的过程。人们通常觉得颠覆式创新才是主要的变革方式，然而事实是现实中的大部分创新都来自于渐进式。\n\n颠覆式创新会改变人们的生活方式甚至整个产业，渐进式创新则会让事物变得更美好，两者都是不可或缺的。",
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      "body": "（翻译整理自Dieleman的博客）\n\n针对不同用户推荐合适的音乐是每个音乐类软件都希望实现的目标。本文从传统的协同过滤方法的优缺点说起，引出一套基于音频信号的音乐推荐算法，并在最后对实现的深度神经网络进行可视化剖析，理解模型的运行原理。\n\n## 协同过滤\n\n协同过滤是音乐推荐的一种基础方法，基本原理是通过历史使用数据来判断用户的喜好。举个例子，如果有两个用户听了大量相同的歌曲，那他们的兴趣应该是基本类似的。从歌曲角度来讲，如果两首歌被同一组人群听过的话，那这两首歌曲也可以被认为是类似的。上面说的两个角度也就是所谓的User-based和Item-based。这些信息是可以用来作为推荐的。\n\n单纯的协同过滤是不需要知道歌曲内容的，同样一开始也不需要知道你是个什么样的人。所以这个方法的优势就是其适用性非常强，可以同样用于书本推荐和电影推荐等。\n\n然而这个方法也有一个非常大的缺点，由于它采用的是历史使用数据，这也导致了那些比较流行与大众的内容更容易被推荐，而小众或者不知名的歌曲则基本不会被推荐到。这也使得这类推荐算法得到的结果会稍显无聊。\n\n另一个问题是用户在听歌的时候，歌曲的分布是不纯粹的，当你在听某个专辑的时候其实有很多歌并非是你喜欢的，另外专辑中有很多类似转场的音乐的内容其实对于推荐来说是无关的。协同过滤并不会考虑着一些问题。\n\n不过综合下来，最大的问题还是那些新歌和非主流的歌曲无法被推荐。这个可以被认为是一个冷启动问题。我们希望能够把合适的新歌及时推荐给用户，或者推荐一个用户从没听说过的乐队。为了这些目标，我们需要寻找一个不一样的方案。\n\n\n## 基于内容的推荐\n\n对于歌曲而言，可以获取到的内容信息包括了：标签、歌手、专辑信息、歌词、用户评论以及音频信息本身。在这些信息中，最难高效利用的应该是音频信号本身。在歌曲音频和用户偏好之间有一个较大的鸿沟，需要我们通过各种方法从音频中提取信息，比如一些比较便于提取的像：音乐的类型或者使用的乐器等等。剩余的一些信息稍微难一些，比如歌曲所传达的情绪，歌曲所在的时期。还有一些信息基本从音频是不可获取的：歌手的地区和歌词的主题。\n\n尽管有那么多挑战，但有一点是很清晰的，歌曲的音乐本身会决定了你是否喜欢这首歌。对于歌曲音频信号本身的分析是非常重要且有意义的。\n\n\n## 深度学习预测听歌习惯\n\n本文采用的方案是通过现有的协同过滤模型进行训练，训练得到一个回归模型（深度神经网络），可以通过这个模型预测得到每首歌的隐含表达式。\n\n这个方法的理念是根据协同过滤模型把用户和歌曲投影到一个低维度的隐含空间里，歌曲的位置包含了所有影响用户听歌习惯的因素。如果两首歌在这个空间中非常靠近，那么可以说它们是类似的曲目。如果一首歌非常靠近某个用户，那这也许是一个很好的推荐选项（假设用户还没有听过）。如果我们可以通过歌曲的音频预测出歌曲在空间中的位置，那我们就可以在不使用历史数据的情况下推荐音乐了。\n\n训练后的模型可以通过t-SNE算法降维到二维空间用于可视化展示，可以从下图看到，同类型的歌曲会聚集到一起，比如下图中左上角是Rap，而最下方是电子乐。\n\n![](https://steemitimages.com/DQmNQJ5QfPC41hf9716i48kegU5E6LuTr4nj7XMBKBhJVbt/image.png)\n\n## 架构\n\n音乐推荐深度神经网络的架构图如下所示，包含了四个卷积层和三个稠密层，可以看出对于音频和图像，卷积层的设计差别还是非常大的。\n\n![](https://steemitimages.com/DQmToLiv71o9hHUydPqySv3C2yJKrV3ixmpzSZMdsRjZEgT/image.png)\n\n\n这里省略了细节设计，如果感兴趣的话可以直接阅读原文。\n\n\n## 分析\n\n这里可以说是最酷的部分了。到底这个神经网络学到了什么？这些特征到底看起来是什么样子的？使用卷积神经网络来处理这个问题的主要原因是在于，通过音频信号推荐音乐是一个非常复杂的问题，需要很多级的抽象。如同处理图像分类问题一样，连续的神经网络层级可以提取更多复杂的抽象特征。\n\n### 输入层\n\n首先是神经网络的输入，这里每一次的输入是3秒钟音频片段的频谱。对于一段长音频来说，可以把3秒作为一个时间窗口，把歌曲可以切成N多个片段，把这么多个片段的预测值做一个平均。\n\n这里具体的计算是将音频信号的各个窗口做一次傅里叶变换，每一次傅里叶变换可以构成一帧，这些连续帧连接在一起形成一个声音频谱。最后一步是将频率的时间轴从线性转换为梅尔刻度以降低维度。最终的输入是梅尔频谱（599帧/128个频点）。\n\n### 隐含层第一层\n\n让我们来观察第一个卷积层（直接对应的是输入频谱），这个卷积层已经学习到了一组filter，而这些filter是可以简单可视化的，如下图所示，负值是红色，正值蓝色，0是白色。每一个filter仅包含4帧的宽度，各个filter之间以垂直红线分割。\n\n![](https://steemitimages.com/DQmdGYm6AUmdVND3nM8utLHjmbFbNJqafeL4DkSGGLSkjJ2/image.png)\n\n横轴是时间轴，纵轴是频率从下至上由低到高\n从图像中可以看出，大部分filter在各个频率下，红蓝分布很均匀，说明它们过滤出了歌曲的背景音乐或者是和声部分。而另一些filter则指向性很明确，在某些特定的频率出现升高或降低，说明这些filter探测到了演唱者的人声。\n\n进一步的测试发现了一些有意思的现象（原文中包含了某些测试集的演示），这些filter甚至探测出了音频中特定的音高与和弦，可见这些细微的因素也会影响到人们对于歌曲的偏好。\n\n### 隐含层最后一层\n\n神经网络的每一层都会从前一层的输入中提取更高维度的抽象并传输给下一层。输出层前一层的全连接网络基本可以挑选出歌曲的子类型了。这里已经不需要通过频谱进行可视化分析了，每一个filter基本可以代表一个特定的分类或者某几个分类。有些filter还很有意思地区分了语言（比如中文歌曲），还有些定位了某种语言下的特定歌曲类型（像西班牙Rap音乐）。\n\n这一层的设计主要参考了AlexNet，可见除了图像分类问题，这些filter对于音频信号的信息提取也非常有用。\n\n### 输出层\n\n输出层为一个40维的空间向量。我们可以将测试数据放入训练完成后的模型，对每一首歌找到各自距离最相近的其它歌曲，从而可以列出多个基于相似度生成的歌单。从结果来看，效果虽然不是最完美，但还是非常不错的，这里就不列出结果了，感兴趣也可以去原文看。\n\n\n## 总结\n\n这篇文章整理了基于内容的深度学习音乐推荐系统，系统的具体设计内容都可以在这篇论文Deep content-based music recommendation里找到。",
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