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我们介绍了变分图自动编码器(VGAE)，这是一个基于变分自动编码器(VAE)的图结构数据的无监督学习框架[2，3]。该模型利用了潜在变量，能够学习无向图的可解释的潜在表示(见图1)。
我们用一个图形卷积网络(GCN)[4]编码器和一个简单的内积译码器演示了这个模型。我们的模型在引文网络中的链接预测任务上取得了有竞争力的结果。与大多数现有的基于图结构数据的无监督学习和链接预测模型[5，6，7，8]相比，我们的模型可以自然地加入节点特征，从而显著提高了在许多基准数据集上的预测性能。
图1：在CORA引文网络数据集上训练的无监督VGAE模型的潜在空间[1]。
灰色线 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/07/31/Graph%20Attentional%20Layer/" title="Graph Attention Networks (图注意力网络)">Graph Attention Networks (图注意力网络)</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-07-31T01:27:42.000Z" title="发表于 2021-07-31 09:27:42">2021-07-31</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-08-27T01:13:09.000Z" title="更新于 2021-08-27 09:13:09">2021-08-27</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E8%AE%BA%E6%96%87%E9%98%85%E8%AF%BB%E7%AC%94%E8%AE%B0/">论文阅读笔记</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/">图神经网络</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/tags/%E7%89%B9%E5%BE%81%E5%AD%A6%E4%B9%A0/">特征学习</a><span class="article-meta-link">•</span><a class="article-meta__tags" href="/tags/GAT/">GAT</a></span></div><div class="content">在本节中，我们将介绍用于构建任意图注意力网络的构建块层（通过堆叠该层），并直接概述其与神经图处理领域的先前工作相比的理论和实践优势和局限性。
我们将从描述单个图形注意层开始，作为我们实验中使用的所有 GAT 架构中使用的唯一层。 我们使用的特定注意设置密切遵循 Bahdanau 等人的工作。 (2015)——但该框架与注意力机制的特定选择无关。
我们层的输入是一系列的节点特征，h={h⃗1,h⃗2,…,h⃗N},h⃗i∈RF\mathbf{h}=\left\{\vec{h}_{1}, \vec{h}_{2}, \ldots, \vec{h}_{N}\right\}, \vec{h}_{i} \ ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/07/23/Tensorflow%E4%B8%AD%E7%9A%84GPU%E9%85%8D%E7%BD%AE%E5%8F%8A%E5%86%85%E5%AD%98%E5%88%86%E9%85%8D/" title="Tensorflow中的GPU配置及内存分配">Tensorflow中的GPU配置及内存分配</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-07-23T01:01:59.000Z" title="发表于 2021-07-23 09:01:59">2021-07-23</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-11-06T05:54:49.000Z" title="更新于 2021-11-06 13:54:49">2021-11-06</time></span></div><div class="content">Nvidia-smi
nvidia-sim简称NVSMI，提供监控GPU使用情况和更改GPU状态的功能。这个工具是N卡驱动附带的。smi(System management interface)。
更详细的介绍：https://blog.csdn.net/C_chuxin/article/details/82993350
CMD中输入:
1nvidia-smi
GPU：本机中的GPU编号（有多块显卡的时候，从0开始编号）图上GPU的编号是：0
Fan：风扇转速（0%-100%），N/A表示没有风扇
Name：GPU类型，图上GPU的类型是：Tesla T4
Temp：GPU的 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/07/13/Drug%E2%80%93drug%20interaction%20prediction%20with%20Wasserstein%20Adversarial%20Autoencoder-based%20knowledge%20graph%20embeddings/" title="Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings">Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-07-13T06:53:54.000Z" title="发表于 2021-07-13 14:53:54">2021-07-13</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-11-10T12:27:57.000Z" title="更新于 2021-11-10 20:27:57">2021-11-10</time></span></div><div class="content">摘要:
药理药物之间的相互作用可能会引发意想不到的不良事件。获取更丰富、更全面的药物-药物相互作用(DDIS)信息是公共卫生和药物开发的关键任务之一。近年来，几种知识图(KG)嵌入方法在DDI领域受到越来越多的关注，因为它们能够将药物和相互作用投影到低维特征空间中，用于预测链接和对三元组进行分类。然而，现有的方法只采用均匀随机模式来构造负样本。因此，这些样本往往过于简单，无法训练出有效的模型。通过引入基于Wasserstein距离和Gumbel-Softmax松弛的对抗性自动编码器(AAE)，提出了一种新的KG嵌入框架。在我们的框架中，自动编码器被用来产生高质量的阴性样本，并且自动编码器的隐藏 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/07/11/%E8%AE%BA%E6%96%87%E5%86%99%E4%BD%9C/" title="论文写作">论文写作</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-07-11T11:36:43.000Z" title="发表于 2021-07-11 19:36:43">2021-07-11</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-12-30T00:47:45.000Z" title="更新于 2021-12-30 08:47:45">2021-12-30</time></span></div><div class="content">典型结构：
摘要：用 100-200 词简介研究任务与挑战、解决思路与方法、实验效果与结论。
介绍：用 1 页左右篇幅，比摘要更详细地介绍研究任务、已有方法、主要挑战、解决思路、具体方法、实验结果。
相关工作：用 0.5-1 页左右篇幅介绍研究任务的相关工作，说明本文工作与已有工作的异同。
方法：用 2-3 页篇幅介绍本文提出的方法模型细节。
实验：用 2-3 页篇幅介绍验证本文方法有效性的实验设置、数据集合、实验结果、分析讨论等。
结论：简单总结本文主要工作，展望未来研究方向。
特别注意以下几点：
**（1）学会换位思考。**要始终站在审稿人或读者的角度审视论文，思考如何更清晰地表达。这 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/07/08/%E5%AF%B9git%E6%9A%82%E5%AD%98%E5%8C%BA%EF%BC%88index%EF%BC%89%E7%9A%84%E7%90%86%E8%A7%A3/" title="对git暂存区（index）的理解">对git暂存区（index）的理解</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-07-08T13:42:48.000Z" title="发表于 2021-07-08 21:42:48">2021-07-08</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-07-14T02:23:14.000Z" title="更新于 2021-07-14 10:23:14">2021-07-14</time></span></div><div class="content">VCS（version control system）。
在git中，对文件的一些修改（新增文件，删除文件，修改文件）涉及到三个区域：工作区（project），暂存区（project/.git/index），本地仓库（project/.git/objects）。
在实际的操作中, 我们处理的就是我们的工作目录, 这个目录是存在于我们电脑的文件系统上的.所有的修改都会保留在工作目录直到我们把它们加入到暂存区(通过git add命令)。
暂存区这是对下一次提交最好的表示方式,当我们执行git commit,git会获取暂存区中的修改,并将这些修改作为下一次的提交内容.暂存区的一个实际作用就是 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/06/28/anaconda/" title="anaconda">anaconda</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-28T03:20:21.000Z" title="发表于 2021-06-28 11:20:21">2021-06-28</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-08-16T03:17:16.000Z" title="更新于 2021-08-16 11:17:16">2021-08-16</time></span></div><div class="content">conda的基础操作:
查询在线包链接：
https://anaconda.org/
https://pypi.org
#查询conda信息
conda info
#查看conda版本
conda -V
#升级conda
conda update conda
#升级anaconda
conda update anaconda
#查看当前环境python版本
python -V
#升级python
conda update python
#列出当前的所有环境
conda info -e
conda info --envs
#创建一个conda环境
conda create -n my_py_en ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/06/20/git%E5%B7%A5%E4%BD%9C%E5%8C%BA%E3%80%81%E6%9A%82%E5%AD%98%E5%8C%BA%E3%80%81%E7%89%88%E6%9C%AC%E5%BA%93%E6%A6%82%E5%BF%B5/" title="git工作区、暂存区、版本库概念">git工作区、暂存区、版本库概念</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-20T10:12:39.000Z" title="发表于 2021-06-20 18:12:39">2021-06-20</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2022-01-07T12:59:35.000Z" title="更新于 2022-01-07 20:59:35">2022-01-07</time></span></div><div class="content">工作区、暂存区、版本库概念
拿自己亲身经历来说，初次接触git的时候最让人迷惑的无非是这三者的概念和他们之间的关系，搞懂这三个概念和他们之间的关系，可以说你对git了解已经三分熟了。本文是笔者使用git一年多后对一些基本概念的个人理解，写在这一方面给为初学者扫盲，同时也总结一下。
先上图，请务必将此图深深印在脑海里：
工作区：用来编辑保存项目文件的地方，也是用户能直接操作到的地方。
暂存区：保存了下次将提交的文件列表信息，一般在 Git 仓库目录中，是一个叫index的文件，通常多数说法还是叫暂存区域；
版本库：也叫本地版本库，之所以说git 快，是因为它是分布式版本控制系统，大部分提交都 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/06/10/Git%E5%B8%B8%E7%94%A8%E5%91%BD%E4%BB%A4/" title="Git常用命令">Git常用命令</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-06-10T07:29:26.000Z" title="发表于 2021-06-10 15:29:26">2021-06-10</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-12-20T11:45:34.000Z" title="更新于 2021-12-20 19:45:34">2021-12-20</time></span></div><div class="content">
作者：gybguohao
链接：https://www.jianshu.com/p/cdccfef91ae1
来源：简书
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。
简介
git有四个工作区域，分别是：工作目录（Workspace）、暂存区（Index/Stage）、本地仓库（Repository）、远程仓库（Remote）
配置
git config
git config命令的作用是配置git的相关信息。
配置全局的用户名和邮箱，mac下可通过终端输入命令cat ~/.gitconfig查看配置信息。
设置用户名：git config --global use ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/03/18/LaTex%E8%AF%AD%E6%B3%95/" title="LaTex语法">LaTex语法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-03-18T01:00:57.000Z" title="发表于 2021-03-18 09:00:57">2021-03-18</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-04-25T03:57:28.000Z" title="更新于 2021-04-25 11:57:28">2021-04-25</time></span></div><div class="content">LaTeX的长度单位
LaTeX中有多个长度单位，为方便以后查询使用，整理如下：
缩写
外文全名
汉语名称
说明
pt
point
点
= 1/72.27英寸 ≈ 0.35146mm。
pc
pica
皮卡
= 12pt ≈ 4.21752mm。
in
inch
英寸
= 72.27pt = 25.4mm。
bp
big point
大点
= 1/72英寸 ≈ 0.35278mm。
mm
millimeter
毫米
= 2.845pt
cm
centimeter
厘米
= 10mm
dd
didot point
狄多点
= 1.07pt ≈ 0.37606 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/03/05/deepDR_%20a%20network-based%20deep%20learning%20approach%20to%20in%20silico%20drug%20repositioning/" title="deepDR_ a network-based deep learning approach to in silico drug repositioning">deepDR_ a network-based deep learning approach to in silico drug repositioning</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-03-05T00:48:36.000Z" title="发表于 2021-03-05 08:48:36">2021-03-05</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-09-12T11:30:41.000Z" title="更新于 2021-09-12 19:30:41">2021-09-12</time></span></div><div class="content">DeepDR：一种基于网络的深度学习方法在计算药物重新定位中的应用
摘要
动机：传统药物的发现和开发往往既耗时又高风险。
重新调整已批准药物的用途/重新定位为快速开发有效的治疗方法提供了一种相对低成本和高效率的方法。大规模、异质生物网络的出现为电子药物定位方法的发展提供了前所未有的机遇。然而，用大多数现有的药物定位方法捕捉高度非线性、异质的网络结构一直是具有挑战性的。
结果：在这项研究中，我们通过整合10个网络：1个药物疾病网络、1个药物副作用网络、1个药物靶点网络和7个药物药物网络，开发了一种基于网络的深度学习方法，称为深度DR，用于电子药物再利用。具体地说，DeepDR通过多模式深度自动编 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2021/01/06/jupyter%20notebook%E7%9A%84%E4%BD%BF%E7%94%A8/" title="jupyter notebook的使用">jupyter notebook的使用</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2021-01-06T09:18:38.000Z" title="发表于 2021-01-06 17:18:38">2021-01-06</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-02-16T06:15:49.000Z" title="更新于 2021-02-16 14:15:49">2021-02-16</time></span></div><div class="content">安装
conda install jupyter notebook
修改主目录
Jupyter Notebook 默认的主目录是“我的文档”，
jupyter notebook --generate-config 显示配置文档所在位置
打开jupyter_notebook_config.py，查找NotebookApp.notebook_dir,后面文件夹改为指定目录。大概在393行
快捷方式属性，删除%USERPROFILE%
利用conda创建了虚拟环境，但是启动jupyter notebook之后却找不到虚拟环境。实际上是由于在虚拟环境下缺少kernel.json文件 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2020/12/29/Heterogeneous%20graph%20inference%20with%20matrix%20completion%20for%20computational%20drug%20repositioning/" title="Heterogeneous graph inference with matrix completion for computational drug repositioning">Heterogeneous graph inference with matrix completion for computational drug repositioning</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2020-12-29T02:04:11.000Z" title="发表于 2020-12-29 10:04:11">2020-12-29</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-12-19T05:55:41.000Z" title="更新于 2021-12-19 13:55:41">2021-12-19</time></span></div><div class="content">Abstract
动机：新的证据表明传统的药物发现实验耗时且成本高。计算药物重新定位在为药物研究和发现节省时间和资源方面起着至关重要的作用。因此，制定更准确、更有效的方法势在必行。异构图推理是计算药物重新定位的经典方法，它不仅具有较高的收敛精度，而且具有较快的收敛速度。然而，该方法没有充分考虑异构关联网络的稀疏性。此外，粗略的相似性度量可以降低识别药物相关适应症的性能。
结果：在这篇文章中，我们提出了一种基于矩阵完成的异质图推理（HGIMC）方法来预测批准药物和新药的潜在适应症。首先，我们使用有界矩阵完成（BMC）模型来预填充原始药物-疾病关联矩阵中的一部分缺失条目。这一步可以在药物网络和疾病 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2020/12/28/Numpy/" title="Numpy">Numpy</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2020-12-28T09:21:25.000Z" title="发表于 2020-12-28 17:21:25">2020-12-28</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-03-28T10:47:56.000Z" title="更新于 2021-03-28 18:47:56">2021-03-28</time></span></div><div class="content">1，np.random.choice的用法
import numpy as np
参数意思分别 是从a 中以概率P，随机选择3个, p没有指定的时候相当于是一致的分布
a1 = np.random.choice(a=5, size=3, replace=False, p=None)
print(a1)
非一致的分布，会以多少的概率提出来
a2 = np.random.choice(a=5, size=3, replace=False, p=[0.2, 0.1, 0.3, 0.4, 0.0])
print(a2)
replacement 代表的意思是抽样之后还放不放回去，如果是False的话，那 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2020/12/21/%E9%81%87%E5%88%B0%E7%9A%84%E9%94%99%E8%AF%AF%E4%BB%A5%E5%8F%8A%E8%A7%A3%E5%86%B3%E5%8A%9E%E6%B3%95/" title="遇到的错误以及解决办法">遇到的错误以及解决办法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2020-12-21T03:28:39.000Z" title="发表于 2020-12-21 11:28:39">2020-12-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2022-09-24T12:05:59.787Z" title="更新于 2022-09-24 20:05:59">2022-09-24</time></span></div><div class="content">1,
Failed to import pydot. You must install pydot and graphviz for pydotprint to work.
pip install pydot
pip install graphviz (这种方法无效，可能是安装的时候没有依赖，下面的可以正常解决问题）
conda install graphviz
2, python模块调用时，相对路径问题
https://blog.csdn.net/ljl6158999/article/details/78657799
临时添加搜索路径–python sys.path.append()和sys ...</div></div></div><nav id="pagination"><div class="pagination"><a class="extend prev" rel="prev" href="/"><i class="fas fa-chevron-left fa-fw"></i></a><a class="page-number" href="/">1</a><span class="page-number current">2</span><a class="page-number" href="/page/3/#content-inner">3</a><span class="space">&hellip;</span><a class="page-number" href="/page/5/#content-inner">5</a><a class="extend next" rel="next" href="/page/3/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">Ruoneo</div><div class="author-info__description">嗨! 你好!</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">64</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">26</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">4</div></a></div><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/ruoneo" target="_blank" title="Github"><i class="fab fa-github"></i></a><a class="social-icon" href="mailto:xum321@gmail.com" target="_blank" title="Email"><i class="fas fa-envelope"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">感谢来访。希望这里有对你有帮助的内容。</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/07/06/git%E5%B1%80%E5%9F%9F%E7%BD%91%E9%83%A8%E7%BD%B2%E6%96%B9%E6%B3%95/" title="git局域网部署方法">git局域网部署方法</a><time datetime="2024-07-06T08:13:10.000Z" title="发表于 2024-07-06 16:13:10">2024-07-06</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2023/09/25/%E5%85%B3%E4%BA%8EPyQt%E7%9A%84connect%E4%BC%A0%E5%85%A5lambda%E5%87%BD%E6%95%B0%E4%BD%9C%E4%B8%BA%E5%8F%82%E6%95%B0%E8%B0%83%E7%94%A8%E6%A7%BD%E5%87%BD%E6%95%B0%E7%9A%84%E4%B8%80%E7%82%B9%E6%80%9D%E8%80%83/" title="关于PyQt的connect传入lambda函数作为参数调用槽函数的一点思考">关于PyQt的connect传入lambda函数作为参数调用槽函数的一点思考</a><time datetime="2023-09-25T13:53:35.000Z" title="发表于 2023-09-25 21:53:35">2023-09-25</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2022/03/30/%E5%A4%8D%E4%B9%A05/" title="Spring注解">Spring注解</a><time datetime="2022-03-30T07:22:16.000Z" title="发表于 2022-03-30 15:22:16">2022-03-30</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2022/03/30/%E5%A4%8D%E4%B9%A04/" title="Spring">Spring</a><time datetime="2022-03-30T02:23:10.000Z" title="发表于 2022-03-30 10:23:10">2022-03-30</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2022/03/22/MultiDTI_%20drug%E2%80%93target%20interaction%20prediction%20based%20on%20multi-modal%20representation%20learning%20to%20bridge%20the%20gap%20between%20new%20chemical/" title="MultiDTI_ drug–target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical">MultiDTI_ drug–target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical</a><time datetime="2022-03-22T06:29:57.000Z" title="发表于 2022-03-22 14:29:57">2022-03-22</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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