• For the next MusicXML release we may want to update these media types to reflect the transfer of MusicXML development to the W3C Music Notation Community Group. <p>This is related to issue #50 about...
• <div><p>This PR is based on https://github.com/Libresonic/libresonic/pull/476 and relates to #495 in that it implements .cue support for <em>music</em> and <em>audiobook</em> media types. It matches ...
• Note: since the connection direction of the synapses is reversed compared to the spike output neuron it is not possible to misuse receptor_types for the music_channel. <p>Possible issues: New synapse...
• </li><li>[X] My code has no errors or misuse of raylib</li></ul> <h3>Issue description <p>Okay, I was wondering why I can't open my WAV files as Music objects. After some searching, building this...
• <p>As an example, the Apple Music application displays the two targets with an icon depicting their use. <p><img alt="image" src=...
• 425 131073 3001 131072 Music From The Motion Picture Fred Claus 426 131073 3001 131072 Johnny Clegg & Juluka 427 131073 3001 131072 Koos Kombuis 428 131073 3001 131072 LOST.DIR </code></pre> ...
• 大部分的知识图谱嵌入方法仅仅...而每一个类型又是分层的，如，music/artist这个实体类型有两个子类型music和artist。由于实体有多个类型，那么在不同的关系下应当凸显不同的类型表示，如在book/author/works_written
大部分的知识图谱嵌入方法仅仅集中于三元组的结构信息，忽视了实体的层次类型信息，实体层次类型信息是指实体所属类型的信息，而这个类型是分层的，有很多的子类型。如论文中的图1
莎士比亚这个头实体有很多的类型，如music/artist，award/award_nominee，book/author。而每一个类型又是分层的，如，music/artist这个实体类型有两个子类型music和artist。由于实体有多个类型，那么在不同的关系下应当凸显不同的类型表示，如在book/author/works_written这个关系下，应该突出book/author这个类型，而罗密欧与朱丽叶这个实体应该突出book/written_work这个类型。
TKRL模型也是基于平移模型的假设，在考虑到知识图谱结构信息的同时引入实体层次类型信息，每一个实体在不同关系下应当突出不同的实体类型。
具体的，模型的能量函数如下：
$E(h,r,t)=||M_{r,h}h+r-M_{rt}t||$
其中$M_{rh}$就是类型特化映射矩阵，是指头实体在关系$r$下应该凸显哪一个类型。$M_{rt}$就是指尾实体在关系r下应该凸显那个类型。
整个模型的损失函数仍是最大间隔函数.
所以重点在于怎么获得$M_{rh}$和$M_{rt}$呢。
论文采用层次映射编码：
假设每一个实体有的类别集合是c，有n个类别，$c=\{c_1,c_2,\cdots,c_n\}$。$c_i$是实体的第i个类别，而每一个类别又是分层的，有很多个子类型，假设有m个子类型，那么$c_i=\{c_i^1,c_i^2,\cdots,c_i^m\}$，其中$c_i^j$代表实体的第i个类型的第j个子类型。
$M_{rh}$的计算方式为：
$M_{rh}=\alpha_1 M_{c_1^h}+\alpha_2 M_{c_2^h}+\cdots+\alpha_n M_{c_n^h}$
其中$M_{c_i^h}$代表头实体在它的第i个类型下的映射矩阵，每一个$\alpha_i$就是权重，表示的就是实体在当前第i个类型下的凸显程度。很显然在上面的例子中，对于实体莎士比亚，此时book/author这个类型就应该凸显出来。
对于尾实体来说是一样的，我们仍以论文图一中的例子为例。罗密欧与朱丽叶这个实体有两个类别book/written_book和TV/TV_subject。那么此时罗密欧与朱丽叶的类型映射矩阵如下：
$M_{rt}=\alpha_1 M_{c_1^t}+\alpha_2 M_{c_2^t}$
由于在关系book/author/works_written下我么应该凸显book/written_book这个类别，所以此时$\alpha_1$要大于$\alpha_2$。
每一个类别又是有很多子类型的，论文同样把每一个子类型也都用子类型映射矩阵表示。所以对于头实体第i个类型的类型映射矩阵$M_{c_i^h}$，它又是由第i个类型的所有子类型的映射矩阵表示出来的。假设有m个子类型，那么此时所有子类型的映射矩阵表示为$M_{c_i^h}^{(1)}，M_{c_i^h}^{(2)},\cdots,M_{c_i^h}^{(m)}$。
论文中有两种组合方式：
递归层次编码：
$M_{c_i^h}=\prod_{i=1}^{m}M_{c_i^h}^{(i)}=M_{c_i^h}^{(1)}M_{c_i^h}^{(2)}...M_{c_i^h}^{(m)}$
$M_{c_i^h}^{(2)}$代表的是头实体的第i个类型的第2个子类型的映射矩阵。
第二种组合方式是加权层次编码：
$M_{c_i^h}=\sum_{i=1}^{m}\beta_iM_{c_i^h}^{(i)}$
实体类型作为约束条件构建负例三元组
在最典型的TransE模型中，负例三元组是随机替换正例三元组中的头实体或者尾实体构建的。但是问题在于由于分布式表示学习的假设：拥有相似关系的实体在最终学习好的语义空间中往往聚集在一起。此时如果应用于三元组分类任务或者图谱补全任务那么往往会出错，因为模型可能会挑选那些与真实实体有着类似关系的实体作为预测答案。主要原因就是在于模型并没有为这些有着相似关系或类型相似的实体进行特别的更加精准的学习。
举个例子：
(吴承恩，是…作者，西游记)
(施耐庵，是…作者，水浒传)
显然吴承恩和施耐庵是很近的，西游记与水浒传更近。那么假如所图谱补全任务（吴承恩，是…作者，）那么此时预测水浒传的几率是很大的。
为了解决这个问题，有学者使用硬类型限制来控制负例三元组的生成。具体的，之前没有类型限制时是这样的：
$(h,r,t)$这是正例三元组。$(h^{'},r,t)$是负例三元组，其中$h^{'}$是从整个实体集合$E$中选出来的一个实体，同时要保证$(h^{'},r,t)$不在$T$中，$T$指的是正例三元组集合。硬类型限制是指限制$h^{'}$的选取范围在$E^c$中，$E^c$是一个实体集合它是$E$的子集，$E^c$中的每一个实体都有类型$c$
所以
硬类型限制就是强制负例三元组中替换的实体与源实体属于同一个类型，强行的增大了拥有相同类型实体之间的差异，一定程度上可以解决拥有相似实体类型的实体在表示上难以区分的问题
但是又有点太强硬了，那些实体类型相似的实体就应当距离比较近，这种方式强行的拉开了它们的距离。
因此论文中提到软类型限制：
软类型限制
不同于强类型限制要求替换的实体必须有源实体具有同一个类型，而是以一定比例的。即某些情况下替换的实体与
源实体是同一个类型的。
$P(e^{'}\in E_c)=\frac{(k+1)|E_c|}{|E|+k|E_c|}$
上式代表随机选取的实体$e^{'}$与源实体在同一个类别下的概率。
使用软类型限制，既能够保证实体的向量空间中原有的相似实体距离较近的现象，又能使得模型学习到这些相似实体之间的差异，使得学习到的实体表示更加精确。
具体的例子：
对于正例三元组：（吴承恩，是…作者，西游记）
，软类型限制以一定的比例提高负例实体与正例实体属于同一个类型的概率，即模型选择施耐庵作为负例的几率要比姚明作为负例的几率大。最后模型利用最大间隔法学习到的更多是 施耐庵不是西游记的作者，这种信息比姚明不是西游记的作者要重要很多。此外由于是软类型限制，模型同样会选择一些与源实体不是同一个类型的实体作为负例，这样通过对图谱中所有实体的整体学习，那些相似的实体在嵌入空间中仍然距离较近，比如吴承恩和施耐庵的距离一定比吴承恩和姚明的距离近。
本文与SSE（Semantically Smoothing Embedding）模型的区别在于，SSE模型假设每一个实体只有一个类别，而且这个类别就只有一个类型，没有分层的子类型。因此这篇论文TKRL模型对实体类别信息的利用更加细致精确。但是显然它把每一个类型的每一个子类型都用映射矩阵表示，计算量肯定不小。


展开全文
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• <p>The music not playing, is loading infinitily. <h2><strong>Console Panel Error Message:</strong> (Computer 1**) <pre><code> C:\Users\srmp\AppData\Local\Programs\Kaku\resources\app.asar\node_modules\...
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