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<?xml version="1.0" encoding="utf-8"?>
<search>
<entry>
<title>2019年书单</title>
<url>/posts/353b91f9/</url>
<content><![CDATA[<p>新的一年，保持阅读和写作的习惯，每一个月读完一本书写一篇读书笔记(简称三一计划)。</p>
<span id="more"></span>
<p><img src="https://i.loli.net/2019/01/02/5c2cce1e92717.jpg" alt="booklist.jpg"></p>
<p>今年准备阅读的12本书如下:</p>
<ol>
<li>易经</li>
<li>理想国</li>
<li>自然哲学的数学原理</li>
<li>红楼梦</li>
<li>瓦尔登湖</li>
<li>科学与方法</li>
<li>科学史</li>
<li>小王子</li>
<li>西方哲学史</li>
<li>中国哲学简史</li>
<li>从一到无穷大</li>
<li>美的历程</li>
</ol>
]]></content>
<categories>
<category>随笔感悟</category>
</categories>
<tags>
<tag>书籍</tag>
<tag>阅读</tag>
</tags>
</entry>
<entry>
<title>Add Two Numbers</title>
<url>/posts/64c8f80f/</url>
<content><![CDATA[<h5 id="问题描述"><a href="#问题描述" class="headerlink" title="问题描述"></a>问题描述</h5><p>You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.</p>
<p>You may assume the two numbers do not contain any leading zero, except the number 0 itself.</p>
<span id="more"></span>
<h5 id="示例"><a href="#示例" class="headerlink" title="示例"></a>示例</h5><p>Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)<br>Output: 7 -> 0 -> 8<br>Explanation: 342 + 465 = 807.</p>
<h5 id="算法思路"><a href="#算法思路" class="headerlink" title="算法思路"></a>算法思路</h5><p>在这个问题中，有两种解决思路，第一种是先将输入的链表计算结果转换成字符串，再转成链表输出；第二种是直接采用链表操作。总的来说，第一种方法更易于理解，执行效率更高（runtime is 116ms），第二种方法更加复杂，耗时更长(runtime is 288ms),所以推荐使用方法一。</p>
<h5 id="代码实现（Python3）"><a href="#代码实现（Python3）" class="headerlink" title="代码实现（Python3）"></a>代码实现（Python3）</h5><p>方法一：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><code class="hljs python"><span class="hljs-keyword">class</span> <span class="hljs-title class_">Solution</span>:<br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">addTwoNumbers</span>(<span class="hljs-params">self, l1, l2</span>):<br> twoSum = <span class="hljs-built_in">str</span>(<span class="hljs-built_in">int</span>(self.getNumber(l1)) + <span class="hljs-built_in">int</span>(self.getNumber(l2)))<br> <span class="hljs-keyword">return</span> self.constructList(twoSum)<br> <br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">getNumber</span>(<span class="hljs-params">self, head</span>):<br> current, res = head, []<br> <span class="hljs-keyword">while</span> current:<br> res.append(<span class="hljs-built_in">str</span>(current.val))<br> current = current.<span class="hljs-built_in">next</span><br> <span class="hljs-keyword">return</span> <span class="hljs-string">''</span>.join(res[::-<span class="hljs-number">1</span>]) <span class="hljs-comment"># reverse res</span><br><br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">constructList</span>(<span class="hljs-params">self, num</span>):<br> head = ListNode(<span class="hljs-built_in">int</span>(num[-<span class="hljs-number">1</span>])) <span class="hljs-comment"># get the last element</span><br> current = head<br> <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(num) - <span class="hljs-number">2</span>, -<span class="hljs-number">1</span>, -<span class="hljs-number">1</span>): <span class="hljs-comment"># range(start, stop, step)</span><br> node = ListNode(<span class="hljs-built_in">int</span>(num[i]))<br> current.<span class="hljs-built_in">next</span> = node<br> current = current.<span class="hljs-built_in">next</span><br> <span class="hljs-keyword">return</span> head<br></code></pre></td></tr></table></figure>
<p>方法二：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><code class="hljs python"><span class="hljs-comment"># Definition for singly-linked list.</span><br><span class="hljs-comment"># class ListNode(object):</span><br><span class="hljs-comment"># def __init__(self, x):</span><br><span class="hljs-comment"># self.val = x</span><br><span class="hljs-comment"># self.next = None</span><br><br><span class="hljs-keyword">class</span> <span class="hljs-title class_">Solution</span>(<span class="hljs-title class_ inherited__">object</span>):<br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">addTwoNumbers</span>(<span class="hljs-params">self, l1, l2</span>):<br> <span class="hljs-string">"""</span><br><span class="hljs-string"> :type l1: ListNode</span><br><span class="hljs-string"> :type l2: ListNode</span><br><span class="hljs-string"> :rtype: ListNode</span><br><span class="hljs-string"> """</span><br> <br> <span class="hljs-comment"># Carry over place holder.</span><br> carry = <span class="hljs-number">0</span><br> <br> <span class="hljs-comment"># Create a dummy head, tail node to keep track of the start and end of the list</span><br> dummy_head = tail = ListNode(<span class="hljs-number">0</span>)<br> <br> <span class="hljs-comment"># While there are elements in either list we add them</span><br> <span class="hljs-keyword">while</span> l1 <span class="hljs-keyword">or</span> l2:<br> <br> <span class="hljs-comment"># Get the two element values, if there is not a node use 0</span><br> num_one = l1.val <span class="hljs-keyword">if</span> l1 <span class="hljs-keyword">else</span> <span class="hljs-number">0</span><br> num_two = l2.val <span class="hljs-keyword">if</span> l2 <span class="hljs-keyword">else</span> <span class="hljs-number">0</span><br> <br> <span class="hljs-comment"># Get the new total with two numbers plus the carry over</span><br> new_sum = num_one + num_two + carry<br> <br> <span class="hljs-comment"># Check to see if the number will create a carry</span><br> <span class="hljs-keyword">if</span> new_sum > <span class="hljs-number">9</span>:<br> <span class="hljs-comment"># Set the tail node equal to the new node.</span><br> tail.<span class="hljs-built_in">next</span> = ListNode(new_sum - <span class="hljs-number">10</span>)<br> <span class="hljs-comment"># Set a carry because the number was larger than 10</span><br> carry = <span class="hljs-number">1</span><br> <span class="hljs-keyword">else</span>:<br> tail.<span class="hljs-built_in">next</span> = ListNode(new_sum)<br> carry = <span class="hljs-number">0</span><br> <br> <span class="hljs-comment"># Set the tail node to the new tail.</span><br> tail = tail.<span class="hljs-built_in">next</span> <br> <br> <span class="hljs-comment"># Set the current nodes to the next number</span><br> <span class="hljs-keyword">if</span> l1:<br> l1 = l1.<span class="hljs-built_in">next</span><br> <span class="hljs-keyword">if</span> l2:<br> l2 = l2.<span class="hljs-built_in">next</span><br> <br> <span class="hljs-comment"># Once the adding has been completed if there is still a carry append a new node</span><br> <span class="hljs-keyword">if</span> carry:<br> tail.<span class="hljs-built_in">next</span> = ListNode(carry)<br> <br> <span class="hljs-comment"># Return the first number set, dummy head was just a place holder. The tail would point to the last node and not the first number in the linked list.</span><br> <span class="hljs-keyword">return</span> dummy_head.<span class="hljs-built_in">next</span><br></code></pre></td></tr></table></figure>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="https://stackoverflow.com/questions/31633635/what-is-the-meaning-of-inta-1-in-python">https://stackoverflow.com/questions/31633635/what-is-the-meaning-of-inta-1-in-python</a> What is the meaning of “int(a[::-1])” in Python?</li>
<li><a href="https://stackoverflow.com/questions/930397/getting-the-last-element-of-a-list-in-python">https://stackoverflow.com/questions/930397/getting-the-last-element-of-a-list-in-python</a> Getting the last element of a list in Python</li>
<li><a href="https://www.pythoncentral.io/pythons-range-function-explained/">https://www.pythoncentral.io/pythons-range-function-explained/</a> Python’s range() Function Explained</li>
<li><a href="https://leetcode.com/problems/add-two-numbers/">https://leetcode.com/problems/add-two-numbers/</a> Add Two Numbers</li>
</ol>
]]></content>
<categories>
<category>算法</category>
</categories>
<tags>
<tag>算法</tag>
<tag>python</tag>
</tags>
</entry>
<entry>
<title>【论文投稿-2021】2021年机器学习和数据挖掘相关会议投稿时间</title>
<url>/posts/82aa9d8e/</url>
<content><![CDATA[<p>按月份对2021年机器学习和数据挖掘相关会议征稿进行统计。</p>
<span id="more"></span>
<h3 id="2021年"><a href="#2021年" class="headerlink" title="2021年"></a>2021年</h3><h4 id="2021-01"><a href="#2021-01" class="headerlink" title="2021-01"></a>2021-01</h4><ol>
<li>IJCNN 2021（The international joint conference on neural networks），Online，截稿日期：2021-01-15 （推迟为2021-02-10）<a href="https://www.ijcnn.org/">https://www.ijcnn.org/</a></li>
<li>IJCAI 2021 (International Joint Conference on Artificial Intelligence), Montreal, Canada, 截稿日期：2021-01-20（摘要提交截止日期：2021-01-13）<a href="https://ijcai-21.org/">https://ijcai-21.org/</a></li>
</ol>
<h4 id="2021-02"><a href="#2021-02" class="headerlink" title="2021-02"></a>2021-02</h4><ol>
<li>KDD 2021（ACM SIGKDD Conference on Knowledge Discovery and Data Mining），Singapore，截稿日期：2021-02-08 <a href="https://www.kdd.org/kdd2021/">https://www.kdd.org/kdd2021/</a></li>
</ol>
<h4 id="2021-04"><a href="#2021-04" class="headerlink" title="2021-04"></a>2021-04</h4><ol>
<li>CICAI 2021 (CAAI International Conference on artificial intelligence 2021) (cicai.caai.cn), 中国杭州，截稿日期：2021-04-18 <a href="https://cicai.caai.cn/">https://cicai.caai.cn/</a></li>
</ol>
<h4 id="2021-05"><a href="#2021-05" class="headerlink" title="2021-05"></a>2021-05</h4><ol>
<li>CCKS 2021（China Conference on Knowledge Graph and Semantic Computing），中国广州，截稿日期：2021-5-10 <a href="http://sigkg.cn/ccks2021/">http://sigkg.cn/ccks2021/</a></li>
<li>CIKM 2021 （Conference on Information and Knowledge Management），Online，截稿日期：2021-05-26 （摘要提交截止日期：2021-05-19）<a href="https://www.cikm2021.org/">https://www.cikm2021.org/</a></li>
</ol>
<h4 id="2021-06"><a href="#2021-06" class="headerlink" title="2021-06"></a>2021-06</h4><ol>
<li>ACM SIGSPATIAL 2021 (ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems)，Beijing, China，截稿日期：2021-06-10 （摘要提交截止日期：2021-05-27）<a href="https://sigspatial2021.sigspatial.org/#:~:text=The%20ACM%20SIGSPATIAL%20International%20Conference%20on%20Advances%20in,the%20situation%20of%20COVID-19%20and%20international%20travel%20restrictions">https://sigspatial2021.sigspatial.org/#:~:text=The%20ACM%20SIGSPATIAL%20International%20Conference%20on%20Advances%20in,the%20situation%20of%20COVID-19%20and%20international%20travel%20restrictions</a>.</li>
<li>ICDM2021 （International Conference on Data Mining），Auckland, New Zealand，截稿日期：2021-06-11 <a href="https://icdm2021.auckland.ac.nz/">https://icdm2021.auckland.ac.nz/</a></li>
<li>ISKE2021 （International Conference on Intelligent Systems and Knowledge Engineering），中国成都，截稿日期：2021-06-30 (（推迟为2021-07-31）<a href="http://iske2021.org/#:~:text=Welcome%20to%20ISKE%202021%20It%20is%20our%20great,following%20the%20successful%20previous%20ISKE%20Conferences%20since%202006">http://iske2021.org/#:~:text=Welcome%20to%20ISKE%202021%20It%20is%20our%20great,following%20the%20successful%20previous%20ISKE%20Conferences%20since%202006</a>.</li>
</ol>
<h4 id="2021-08"><a href="#2021-08" class="headerlink" title="2021-08"></a>2021-08</h4><ol>
<li>WSDM2022 （Web Search and Data Mining）, Arizona, USA, 截止日期：2021-08-09 <a href="http://www.wsdm-conference.org/2022/">http://www.wsdm-conference.org/2022/</a></li>
</ol>
<h4 id="2021-09"><a href="#2021-09" class="headerlink" title="2021-09"></a>2021-09</h4><ol>
<li>AAAI2022（The Thirty-Sixth AAAI Conference on Artificial Intelligence），Vancouver, BC, Canada, 截稿日期：2021-09-08 （摘要提交截止日期：2021-08-30）<a href="https://aaai.org/Conferences/AAAI-22/">https://aaai.org/Conferences/AAAI-22/</a></li>
</ol>
<h4 id="2021-10"><a href="#2021-10" class="headerlink" title="2021-10"></a>2021-10</h4><ol>
<li>WWW 2022（International World Wide Web Conferences），Lyon, France, 截稿日期：2021-10-21 (摘要提交截止日期：2021-10-14) <a href="https://www2022.thewebconf.org/">https://www2022.thewebconf.org/</a></li>
<li>PAKDD 2022（The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining），Chengdu, China，截稿日期：2021-10-31 <a href="http://pakdd.net/">http://pakdd.net/</a></li>
</ol>
<h3 id="2022年"><a href="#2022年" class="headerlink" title="2022年"></a>2022年</h3><h4 id="2022-01"><a href="#2022-01" class="headerlink" title="2022-01"></a>2022-01</h4><ol>
<li>ICDM2022（IEEE International Conference on Data Mining），New York, USA，截稿日期：2022-01-15 <a href="https://www.data-mining-forum.de/icdm2022.php">https://www.data-mining-forum.de/icdm2022.php</a></li>
</ol>
<h3 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h3><ol>
<li><a href="http://www.wikicfp.com/cfp/series?t=c&i=A">http://www.wikicfp.com/cfp/series?t=c&i=A</a></li>
</ol>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>机器学习</tag>
<tag>数据挖掘</tag>
<tag>论文投稿</tag>
</tags>
</entry>
<entry>
<title>2022年国庆小记</title>
<url>/posts/9fb794da/</url>
<content><![CDATA[<p>趁年轻，去运动，去思考，去见识，成长自己。</p>
<span id="more"></span>
<p>疫情之下，保持一个良好的身心状态，对于应对各方面的压力，随地随地可能发生的变化，过好生活就显得至关重要。今天是国庆假期的第一天，由于疫情的缘故，不能安排旅行，就只能待在成都，待在自己平时生活的地方，读书、学习、散步、思考、网上交流。</p>
<p>趁着这个机会，将之前运动的一些经验和心得记录一下，希望对那些愿意调整自己身心状态，在身体和心理上感觉更加舒服些的朋友们有所帮助。</p>
<p>从2020年发生疫情以来，今年快到第3年了，时间过得很快，一溜烟的，但是疫情给我们带来的苦闷或者心烦，确实一直持续的，久久难以散去。希望有那么一天，我们可以开心地面对面交谈，热情地问候，幸福地拥抱。</p>
<p>从2021年12月10日，开始发起#趁年轻 去运动#的活动，到2022年09月29日，保持运动，同时记录的习惯，自己已经运动130天整。最开始在操场跑步，后来在校园里跑步，再后来在室内做无器械运动，身体发生了明显的改变，手臂的肌肉变得更加结实，腿部变得更加有力，精气神变得更加棒。总的来说，一个感受就是，如果室外条件适合，就在室外运动，室内条件合适，就在室内运动，最重要的是，运动起来，注意安全。关于跑步，还在知乎写过一篇文章（<a href="https://zhuanlan.zhihu.com/p/438670862">空气质量知多少，早上跑步好不好？</a>），感兴趣的朋友也可以去看看。无器械运动推荐这本书（<a href="https://book.douban.com/subject/11608712/">无器械健身</a>）。</p>
<p>有时候，心情比较烦闷或者比较无聊的时候，也会选择看书、看电影，看纪录片，从年初到现在，大概看了11本书，23部电影，5部纪录片，在身体没法到远方旅行的时候，让思想和心灵去远航也是一次难得的体验，这是一次次穿越时空，跨越江河的旅行，很棒。如果你不知道选什么书看，这里有份书单，供你选阅（<a href="https://xiepeng21.cn/2019-01-02/2019%E5%B9%B4%E4%B9%A6%E5%8D%95.html">2019年书单</a>）。想找经典的电影，可以看看豆瓣电影Top250列表（<a href="https://movie.douban.com/top250">豆瓣电影Top250</a>），里面很多电影质量很高，值得一看。纪录片的话，自己之前整理了一个列表（<a href="https://www.zhihu.com/question/534427413/answer/2505056045">纪录片推荐</a>），可以选择自己感兴趣的纪录片来看，挺有意思的。</p>
<p>如果晚上睡不太好，也会听听轻音乐，助眠音乐，做冥想练习（<a href="https://www.17mingxiang.com/">Now冥想</a>），回到当下，放空自己的大脑，对于缓解压力，睡个好觉，挺有帮助。</p>
<p>10月的第一天，碎碎念，写了这些。今年剩下的日子还剩3个月，2022年已经过了3/4，希望我们都能安心生活，踏实做事，一点一点，在一件件小事上，修炼自己，保持身心健康，向阳生长。</p>
<hr>
<p>欢迎各位提出建议、问题，我们一起交流、学习、成长。</p>
<p>“问渠那得清如许？为有源头活水来” ヾ(◍°∇°◍)ﾉﾞ</p>
<p>– 我在半亩方塘等你 ^_^</p>
]]></content>
<categories>
<category>随笔感悟</category>
</categories>
<tags>
<tag>阅读</tag>
<tag>趁年轻，去运动</tag>
<tag>国庆</tag>
<tag>电影</tag>
<tag>纪录片</tag>
</tags>
</entry>
<entry>
<title>【论文投稿-2022】2022年人工智能和数据挖掘顶级会议投稿日期</title>
<url>/posts/7c9718ae/</url>
<content><![CDATA[<p>2022的前两个月，在过年、改论文，修改基金项目的过程中，时间不知不觉就过去了。刚好今天周六，趁这个时间，对2022年人工智能和数据挖掘相关顶级会议征稿日期进行统计。</p>
<span id="more"></span>
<table>
<thead>
<tr>
<th><strong>Year</strong></th>
<th><strong>Conference</strong></th>
<th><strong>Link</strong></th>
<th>**Deadline **</th>
</tr>
</thead>
<tbody><tr>
<td>2022</td>
<td>WSDM 2022</td>
<td><a href="http://www.wsdm-conference.org/2022/">http://www.wsdm-conference.org/2022/</a></td>
<td>2021-08-13</td>
</tr>
<tr>
<td></td>
<td>AAAI 2022</td>
<td><a href="https://aaai.org/Conferences/AAAI-22/aaai22call/">https://aaai.org/Conferences/AAAI-22/aaai22call/</a></td>
<td>2021-09-08</td>
</tr>
<tr>
<td></td>
<td>ICLR 2022</td>
<td><a href="https://iclr.cc/Conferences/2022/CallForPapers">https://iclr.cc/Conferences/2022/CallForPapers</a></td>
<td>2021-10-05</td>
</tr>
<tr>
<td></td>
<td>SDM 2022</td>
<td><a href="https://www.siam.org/conferences/cm/conference/sdm22">https://www.siam.org/conferences/cm/conference/sdm22</a></td>
<td>2021-10-12</td>
</tr>
<tr>
<td></td>
<td>WWW 2022</td>
<td><a href="https://www2022.thewebconf.org/">https://www2022.thewebconf.org/</a></td>
<td>2021-10-21</td>
</tr>
<tr>
<td></td>
<td>IJCAI 2022</td>
<td><a href="https://ijcai-22.org/calls-papers/">https://ijcai-22.org/calls-papers/</a></td>
<td>2022-01-14</td>
</tr>
<tr>
<td></td>
<td>ICML 2022</td>
<td><a href="https://icml.cc/Conferences/2022/CallForPapers">https://icml.cc/Conferences/2022/CallForPapers</a></td>
<td>2022-02-03</td>
</tr>
<tr>
<td></td>
<td>KDD 2022</td>
<td><a href="https://kdd.org/kdd2022/cfpResearch.html">https://kdd.org/kdd2022/cfpResearch.html</a></td>
<td>2022-02-10</td>
</tr>
<tr>
<td></td>
<td>CIKM 2022</td>
<td><a href="https://www.cikm2022.org/calls">https://www.cikm2022.org/calls</a></td>
<td>2022-05-16</td>
</tr>
<tr>
<td></td>
<td>NIPS 2022</td>
<td><a href="https://neurips.cc/Conferences/2022/CallForPapers">https://neurips.cc/Conferences/2022/CallForPapers</a></td>
<td>2022-05-19</td>
</tr>
<tr>
<td></td>
<td>ICDM 2022</td>
<td><a href="https://icdm22.cse.usf.edu/calls/Papers.html">https://icdm22.cse.usf.edu/calls/Papers.html</a></td>
<td>2022-06-10</td>
</tr>
<tr>
<td></td>
<td>SIGSPATIAL 2022</td>
<td><a href="https://sigspatial2022.sigspatial.org/">https://sigspatial2022.sigspatial.org/</a></td>
<td>2022-06-10</td>
</tr>
</tbody></table>
<p><strong>More:</strong> If you want to find more conferences information, you can visit the website (<a href="http://www.wikicfp.com/cfp/series?t=c&i=A">http://www.wikicfp.com/cfp/series?t=c&i=A</a>).</p>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>数据挖掘</tag>
<tag>论文投稿</tag>
<tag>人工智能</tag>
</tags>
</entry>
<entry>
<title>如何计算经过CNN（卷积神经网络）卷积后的图片大小</title>
<url>/posts/fb776320/</url>
<content><![CDATA[<p>对于给定的一张图片，如下图，32×32大小的输入图片经过一次卷积之后，如何得到28×28的feature maps?</p>
<span id="more"></span>
<p><img src="https://i.loli.net/2018/12/23/5c1fac8bbfa94.png" alt="Screenshot_2018-09-29-15-31-03-61.png"></p>
<p>设卷积前的图像大小为n×n, 过滤器大小为f×f, padding(填充)为p, stride(步长)为 s, 则卷积后的图像 m×m 大小为：<br>m = (n+2p-f)/s + 1.（如果商不为整数，向下取整）</p>
<p>(32+0-5)/1 + 1 = 28. 所以上图中的输入图片经过一次卷积之后，得到了28×28的feature maps.</p>
]]></content>
<categories>
<category>机器学习</category>
</categories>
<tags>
<tag>深度学习</tag>
<tag>CNN</tag>
</tags>
</entry>
<entry>
<title>Hexo常用命令</title>
<url>/posts/24caea6b/</url>
<content><![CDATA[<p>hexo的基本命令有13个，但平时发布文章，经常用的命令只有下列几个：</p>
<span id="more"></span>
<h2 id="常用命令（按发布文章时使用命令的顺序）"><a href="#常用命令（按发布文章时使用命令的顺序）" class="headerlink" title="常用命令（按发布文章时使用命令的顺序）"></a>常用命令（按发布文章时使用命令的顺序）</h2><figure class="highlight actionscript"><table><tr><td class="code"><pre><code class="hljs actionscript">hexo <span class="hljs-keyword">new</span> <span class="hljs-string">"hello"</span><br></code></pre></td></tr></table></figure>
<p>新建一篇标题为“hello”的文章。文章在文件夹\Hexo\source_posts下查看，也可以缩写成 hexo n。</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">hexo g</span><br></code></pre></td></tr></table></figure>
<p>生成博客的静态文件到文件夹\Hexo\public。便于查看生成博客的静态文件。该命令是hexo generate的缩写。如果使用过gulp压缩过博客，可以使用组合命令“hexo g && gulp”来达到压缩博客静态文件，提高博客访问速度的目的。</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">hexo s</span><br></code></pre></td></tr></table></figure>
<p>用于启动本地服务器，查看网站预览效果。默认本地访问地址为：<a href="http://localhost:4000/">http://localhost:4000/</a> 。该命令是hexo server的缩写。预览的同时可以修改文章内容或该主题的代码，保存后刷新当前页即可。如果要修改博客根目录的_config.yml文件，需要重新执行hexo s才能预览效果。</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">hexo d</span><br></code></pre></td></tr></table></figure>
<p>将本地博客文件部署到设定的github仓库中去。到该步骤，外网可以访问该博客。该命令是hexo deploy的缩写。</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">hexo cl</span><br></code></pre></td></tr></table></figure>
<p>清除缓存文件db.json 和已生成的静态文件 public。通常在博客访问异常时使用。该命令是hexo clean的缩写。</p>
<figure class="highlight haxe"><table><tr><td class="code"><pre><code class="hljs haxe">hexo <span class="hljs-keyword">new</span> <span class="hljs-type">page</span> <span class="hljs-string">"mypage"</span><br></code></pre></td></tr></table></figure>
<p>新建一个标题为mypage的页面，默认超链接地址为博客主页地址/mypage/。该页面不会出现在首页文章列表和归档中，也不支持设置分类和标签。</p>
<h2 id="参考文献"><a href="#参考文献" class="headerlink" title="参考文献"></a>参考文献</h2><ol>
<li><a href="http://moxfive.xyz/2015/12/21/common-hexo-commands/#comments">http://moxfive.xyz/2015/12/21/common-hexo-commands/#comments</a></li>
<li><a href="https://leaferx.online/2017/06/16/use-gulp-to-minimize/">https://leaferx.online/2017/06/16/use-gulp-to-minimize/</a></li>
</ol>
]]></content>
<categories>
<category>经验技巧</category>
</categories>
<tags>
<tag>hexo命令</tag>
</tags>
</entry>
<entry>
<title>OneDrive 登录时一直转圈圈</title>
<url>/posts/7340ecaf/</url>
<content><![CDATA[<p>今天早上突然遇到OneDrive登录时，一直转圈圈，登录不了，采取了几种方法，查询了一些网上资料，现总结如下。</p>
<span id="more"></span>
<ol>
<li>换一个网络连接。可以使用手机热点，有人报告说电信网络曾经登录不了，我刚好就是电信网络；</li>
<li>更改网络设置。在“更改网络适配器”选项中，打开当前网络的“属性”，将“Internet 协议版本6”前面的√去掉，然后用快捷键“Win+R”输出cmd，调出命令窗口，输入“ipconfig /flushdns”命令即可；</li>
<li>关闭科学上网软件。</li>
</ol>
<p>笔者是在经历了以上三个步骤之后，OneDrive就可以正常登录。如果你遇到不能登录OneDrive，以上方法不能解决或者有新的办法，欢迎留言交流。</p>
]]></content>
<categories>
<category>经验技巧</category>
</categories>
<tags>
<tag>OneDrive</tag>
</tags>
</entry>
<entry>
<title>L1、L2正则化</title>
<url>/posts/a02b6105/</url>
<content><![CDATA[<h5 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h5><p>在之前的博客里介绍了<a href="http://xiepeng21.cn/%E3%80%90%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%91%E9%98%B2%E6%AD%A2%E8%BF%87%E6%8B%9F%E5%90%88%E7%9A%84%E6%96%B9%E6%B3%95.html">常见的防止过拟合的方法</a>,这次主要就其中的L1、L2正则化方法进行介绍并比较它们的不同。</p>
<span id="more"></span>
<p>我们使用L1、L2正则化方法的目的，在于减缓机器学习中的过拟合现象。</p>
<p>为什么它们能减缓过拟合现象呢，由于正则项的加入，使得权重矩阵的值减小，因为它假定一个拥有更小权重矩阵的神经网络将产生更简单的模型，进而在一定程度上能减缓过拟合。</p>
<p>在L1和L2中，所采用的正则化项是不同的。</p>
<p>在L2中，其中λ是正则化参数，这个超参数可以通过优化得到更好的结果。同时L2正则化也被称为权重衰减（weight decay），因为它使权重衰减至0（但不等于0）。</p>
<p><img src="https://i.loli.net/2018/11/07/5be2dc708e5c7.png" alt="1.png"></p>
<p>在L1中，我们惩罚权重的绝对值，这里权重可能会减至0。因此，当我们尝试压缩我们的模型时，使用L1十分有用。在其他方面，我们更倾向于使用L2。</p>
<p><img src="https://i.loli.net/2018/11/07/5be2ddd4db9af.png" alt="2.png"></p>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="http://xiepeng21.cn/%E3%80%90%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%91%E9%98%B2%E6%AD%A2%E8%BF%87%E6%8B%9F%E5%90%88%E7%9A%84%E6%96%B9%E6%B3%95.html">http://xiepeng21.cn/%E3%80%90%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%91%E9%98%B2%E6%AD%A2%E8%BF%87%E6%8B%9F%E5%90%88%E7%9A%84%E6%96%B9%E6%B3%95.html</a> 【机器学习】防止过拟合的方法</li>
<li><a href="https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/">https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/</a> An Overview of Regularization Techniques in Deep Learning (with Python code)</li>
</ol>
]]></content>
<categories>
<category>机器学习</category>
</categories>
<tags>
<tag>机器学习</tag>
<tag>正则化</tag>
</tags>
</entry>
<entry>
<title>Longest Substring Without Repeating Characters</title>
<url>/posts/8e8877f3/</url>
<content><![CDATA[<h5 id="问题描述"><a href="#问题描述" class="headerlink" title="问题描述"></a>问题描述</h5><p>Given a string, find the length of the longest substring without repeating characters.</p>
<span id="more"></span>
<h5 id="示例"><a href="#示例" class="headerlink" title="示例"></a>示例</h5><ul>
<li>Example 1:</li>
</ul>
<p>Input: “abcabcbb”<br>Output: 3<br>Explanation: The answer is “abc”, with the length of 3. </p>
<ul>
<li>Example 2:</li>
</ul>
<p>Input: “bbbbb”<br>Output: 1<br>Explanation: The answer is “b”, with the length of 1.</p>
<ul>
<li>Example 3:</li>
</ul>
<p>Input: “pwwkew”<br>Output: 3<br>Explanation: The answer is “wke”, with the length of 3.<br> Note that the answer must be a substring, “pwke” is a subsequence and not a substring. </p>
<h5 id="算法思路"><a href="#算法思路" class="headerlink" title="算法思路"></a>算法思路</h5><p>该问题可以简化成查找一个字符串中最长的不重复字符子串，返回子串的长度。第一种方法用到了枚举和字典，感觉有点绕，我没有理解清楚；第二种方法采用了字符串的split方法，很巧妙，不过比较难想到。两种方法相比，第二种方法运行时间更短（84ms < 88ms）,推荐使用第二种方法，感兴趣的朋友可以研究下第一种方法。</p>
<p>方法一：</p>
<figure class="highlight vim"><table><tr><td class="code"><pre><code class="hljs vim">class Solution(object):<br> def lengthOfLongestSubstring(self, s):<br> dic, <span class="hljs-keyword">res</span>, start, = {}, <span class="hljs-number">0</span>, <span class="hljs-number">0</span><br> <span class="hljs-keyword">for</span> i, ch in enumerate(s):<br> # when char already in dictionary<br> <span class="hljs-keyword">if</span> ch in dic:<br> # check length from start of <span class="hljs-built_in">string</span> <span class="hljs-keyword">to</span> <span class="hljs-built_in">index</span><br> <span class="hljs-keyword">res</span> = <span class="hljs-built_in">max</span>(<span class="hljs-keyword">res</span>, i-start)<br> # <span class="hljs-keyword">update</span> start of <span class="hljs-built_in">string</span> <span class="hljs-built_in">index</span> <span class="hljs-keyword">to</span> the <span class="hljs-keyword">next</span> <span class="hljs-built_in">index</span><br> start = <span class="hljs-built_in">max</span>(start, dic[ch]+<span class="hljs-number">1</span>)<br> # <span class="hljs-built_in">add</span>/<span class="hljs-keyword">update</span> char <span class="hljs-keyword">to</span>/of dictionary <br> dic[ch] = i<br> # answer <span class="hljs-keyword">is</span> either in the begining/middle OR some mid <span class="hljs-keyword">to</span> the end of <span class="hljs-built_in">string</span><br> <span class="hljs-keyword">return</span> <span class="hljs-built_in">max</span>(<span class="hljs-keyword">res</span>, <span class="hljs-built_in">len</span>(s)-start)<br></code></pre></td></tr></table></figure>
<p>方法二：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><code class="hljs python"><span class="hljs-keyword">class</span> <span class="hljs-title class_">Solution</span>:<br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">lengthOfLongestSubstring</span>(<span class="hljs-params">self, s</span>):<br> <span class="hljs-string">"""</span><br><span class="hljs-string"> :type s: str</span><br><span class="hljs-string"> :rtype: int</span><br><span class="hljs-string"> """</span><br> maxnum,num,ss =<span class="hljs-number">0</span>,<span class="hljs-number">0</span>,<span class="hljs-string">''</span><br> <span class="hljs-keyword">for</span> each <span class="hljs-keyword">in</span> s:<br> <span class="hljs-keyword">if</span> each <span class="hljs-keyword">in</span> ss:<br> ss = ss.split(each)[-<span class="hljs-number">1</span>]+each <span class="hljs-comment"># ‘’+each</span><br> num =<span class="hljs-built_in">len</span>(ss)<br> <span class="hljs-keyword">else</span>:<br> num += <span class="hljs-number">1</span><br> ss += each<br> <span class="hljs-keyword">if</span> num>=maxnum:<br> maxnum = num<br> <span class="hljs-keyword">return</span> maxnum<br></code></pre></td></tr></table></figure>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="https://www.w3schools.com/python/ref_string_split.asp">https://www.w3schools.com/python/ref_string_split.asp</a> Python String split() Method</li>
<li><a href="https://leetcode.com/problems/longest-substring-without-repeating-characters/">https://leetcode.com/problems/longest-substring-without-repeating-characters/</a> longest-substring-without-repeating-characters</li>
</ol>
]]></content>
<categories>
<category>算法</category>
</categories>
<tags>
<tag>算法</tag>
<tag>python</tag>
</tags>
</entry>
<entry>
<title>Two Sum</title>
<url>/posts/e83060be/</url>
<content><![CDATA[<h5 id="问题描述"><a href="#问题描述" class="headerlink" title="问题描述"></a>问题描述</h5><p>Given an array of integers, return indices of the two numbers such that they add up to a specific target.<br>You may assume that each input would have exactly one solution, and you may not use the same element twice.</p>
<span id="more"></span>
<h5 id="示例"><a href="#示例" class="headerlink" title="示例"></a>示例</h5><p>Given nums = [2, 7, 11, 15], target = 9,<br>Because nums[0] + nums[1] = 2 + 7 = 9,<br>return [0, 1].</p>
<h5 id="算法思路"><a href="#算法思路" class="headerlink" title="算法思路"></a>算法思路</h5><p>比较容易想到的是，用两个循环，两两元素之和与目标数比较，但是算法复杂度为O（n^2）,会超时。另一种办法是用字典和枚举，用字典存储查询过的值和索引，用枚举来遍历整个列表，算法复杂度为O（n）,所以推荐用第二种方法。</p>
<h5 id="代码实现（python3）"><a href="#代码实现（python3）" class="headerlink" title="代码实现（python3）"></a>代码实现（python3）</h5><p>方法一：</p>
<figure class="highlight python"><table><tr><td class="code"><pre><code class="hljs python"><span class="hljs-keyword">class</span> <span class="hljs-title class_">Solution</span>:<br> <span class="hljs-keyword">def</span> <span class="hljs-title function_">twoSum</span>(<span class="hljs-params">self, nums, target</span>):<br> <span class="hljs-string">"""</span><br><span class="hljs-string"> :type nums: List[int]</span><br><span class="hljs-string"> :type target: int</span><br><span class="hljs-string"> :rtype: List[int]</span><br><span class="hljs-string"> """</span><br> r = []<br> l = <span class="hljs-built_in">len</span>(nums)<br> <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(l):<br> <span class="hljs-keyword">for</span> j <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(l):<br> <span class="hljs-keyword">if</span> nums[i] + nums[j] == target <span class="hljs-keyword">and</span> i != j:<br> <span class="hljs-keyword">return</span> i,j<br> r = r.append(i,j)<br> <span class="hljs-keyword">return</span> r<br></code></pre></td></tr></table></figure>
<p>方法二：</p>
<figure class="highlight angelscript"><table><tr><td class="code"><pre><code class="hljs angelscript"><span class="hljs-keyword">class</span> <span class="hljs-symbol">Solution:</span><br><span class="hljs-symbol"> <span class="hljs-symbol">def</span></span> <span class="hljs-symbol">twoSum</span>(<span class="hljs-symbol">self, <span class="hljs-symbol">nums</span>, <span class="hljs-symbol">target</span></span>):<br> """<br> :<span class="hljs-symbol">type</span> <span class="hljs-symbol">nums: <span class="hljs-symbol">List</span></span>[<span class="hljs-symbol">int</span>]<br> :<span class="hljs-symbol">type</span> <span class="hljs-symbol">target: <span class="hljs-symbol">int</span></span><br> :<span class="hljs-symbol">rtype: <span class="hljs-symbol">List</span></span>[<span class="hljs-symbol">int</span>]<br> """<br> <span class="hljs-symbol">collected</span> = {}<br> <span class="hljs-keyword">for</span> i,x <span class="hljs-keyword">in</span> enumerate(nums):<br> diff = target-x<br> <span class="hljs-keyword">if</span> diff <span class="hljs-keyword">in</span> collected:<br> <span class="hljs-keyword">return</span> [collected[diff], i]<br> collected[x] = i<br></code></pre></td></tr></table></figure>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="https://www.tutorialspoint.com/python/python_dictionary.htm">https://www.tutorialspoint.com/python/python_dictionary.htm</a> Python - Dictionary</li>
<li><a href="https://www.geeksforgeeks.org/enumerate-in-python/">https://www.geeksforgeeks.org/enumerate-in-python/</a> Enumerate() in Python</li>
<li><a href="https://leetcode.com/problems/two-sum/">https://leetcode.com/problems/two-sum/</a> two-sum</li>
</ol>
]]></content>
<categories>
<category>算法</category>
</categories>
<tags>
<tag>算法</tag>
<tag>python</tag>
</tags>
</entry>
<entry>
<title>hexo博客搭建及多电脑同步</title>
<url>/posts/53375297/</url>
<content><![CDATA[<p>之前写了一篇用hexo+github搭建博客的教程，大致流程为：本地博客搭建，将博客部署到github，配置域名访问。详细可参考我的博客（<a href="https://www.jianshu.com/p/058d4054bc3f">Hexo搭建个人博客教程</a>）。最近因为遇到在实验室写好博客，但是想在寝室发布博客，所以涉及到多电脑同步的问题，现将解决方案记录如下。</p>
<span id="more"></span>
<h5 id="方案一"><a href="#方案一" class="headerlink" title="方案一"></a>方案一</h5><p>用百度云或onedrive一类的云盘备份hexo文件夹。这种方法方便快捷，但是仅仅可以用作博客备份，不方便用于同步更新博客，每次更改博客后都要等待云盘同步，才能继续完成接下来的发布步骤。</p>
<h5 id="方案二"><a href="#方案二" class="headerlink" title="方案二"></a>方案二</h5><p>利用coding.net存放hexo博客源文件，github存放hexo博客的网页静态文件。这种方案可以实现多电脑同步。</p>
<h6 id="实验室电脑"><a href="#实验室电脑" class="headerlink" title="实验室电脑"></a>实验室电脑</h6><ol>
<li>注册coding账号（<a href="https://coding.net/login">注册</a>），新建项目hexo_blog（设置成不公开），存放博客源文件。</li>
<li>关联远程库并推送到hexo_blog。<figure class="highlight armasm"><table><tr><td class="code"><pre><code class="hljs armasm"><span class="hljs-symbol">git</span> remote <span class="hljs-keyword">add</span> origin https:<span class="hljs-comment">//git.coding.net/xiepeng21/hexo_blog.git</span><br><span class="hljs-symbol">git</span> <span class="hljs-keyword">add</span> .<br><span class="hljs-symbol">git</span> commit -m <span class="hljs-string">"backup_v1"</span><br><span class="hljs-symbol">git</span> <span class="hljs-keyword">push</span> -u origin master<br></code></pre></td></tr></table></figure></li>
</ol>
<h6 id="寝室电脑"><a href="#寝室电脑" class="headerlink" title="寝室电脑"></a>寝室电脑</h6><ol>
<li><p>安装好nodejs,git,hexo，具体操作，不再赘述，可参考文首的博客(<a href="https://www.jianshu.com/p/058d4054bc3f">Hexo搭建个人博客教程</a>)。</p>
</li>
<li><p>将存放在远程的hexo_blog克隆到本地</p>
<figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">git clone https:<span class="hljs-regexp">//gi</span>t.coding.net<span class="hljs-regexp">/xiepeng21/</span>hexo_blog.git<br></code></pre></td></tr></table></figure>
</li>
<li><p>安装博客依赖的包(读取的是package.json的配置)</p>
<figure class="highlight cmake"><table><tr><td class="code"><pre><code class="hljs cmake">npm <span class="hljs-keyword">install</span><br></code></pre></td></tr></table></figure>
</li>
<li><p>接下来就可以在这台电脑上发布博客了。</p>
</li>
</ol>
<p>注：如果遇到其他问题，可以按照搭建博客的流程分析问题解决办法（<a href="https://www.jianshu.com/p/058d4054bc3f">Hexo搭建个人博客教程</a>）。</p>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="http://chenfengkg.cn/personal-blog-build/">http://chenfengkg.cn/personal-blog-build/</a> 博客一之博客搭建</li>
<li><a href="https://godweiyang.com/2018/04/13/hexo-blog/">https://godweiyang.com/2018/04/13/hexo-blog/</a> 超详细Hexo+Github博客搭建小白教程</li>
<li><a href="https://www.zybuluo.com/mdeditor">https://www.zybuluo.com/mdeditor</a> 作业部落（在线markdown编辑）</li>
</ol>
]]></content>
<categories>
<category>经验技巧</category>
</categories>
<tags>
<tag>博客</tag>
<tag>Hexo</tag>
</tags>
</entry>
<entry>
<title>【AAAI 2022】时空数据挖掘论文</title>
<url>/posts/ab9afbeb/</url>
<content><![CDATA[<p>对AAAI 2022的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，并进行任务分类。</p>
<span id="more"></span>
<p>AAAI 2022录用论文列表：<br><a href="https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf">https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf</a></p>
<p>全文还没有放出来，如果需要，可以在谷歌学术上查找，部分论文已经放在arXiv上。</p>
<h3 id="Traffic-forecasting"><a href="#Traffic-forecasting" class="headerlink" title="Traffic forecasting"></a>Traffic forecasting</h3><ol>
<li><strong>STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction</strong>. Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, Hu Zhang</li>
<li><strong>Graph Neural Controlled Differential Equations for Traffic Forecasting</strong>. Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, Noseong Park</li>
</ol>
<h3 id="Traffic-assignment"><a href="#Traffic-assignment" class="headerlink" title="Traffic assignment"></a>Traffic assignment</h3><ol>
<li><strong>Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment</strong>. Lukas Graf, Tobias Harks, Kostas Kollias, Michael Markl</li>
</ol>
<h3 id="Trajectory-prediction"><a href="#Trajectory-prediction" class="headerlink" title="Trajectory prediction"></a>Trajectory prediction</h3><ol>
<li><strong>Social Interpretable Tree for Pedestrian Trajectory Prediction</strong>. Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Fang Zheng, Nanning Zheng, Gang Hua</li>
<li><strong>Complementary Attention Gated Network for Pedestrian Trajectory Prediction</strong>. Jinghai Duan, Le Wang, Chengjiang Long, Sanping Zhou, Fang Zheng, Liushuai Shi, Gang Hua</li>
</ol>
<h3 id="Meteorological-forecasting"><a href="#Meteorological-forecasting" class="headerlink" title="Meteorological forecasting"></a>Meteorological forecasting</h3><ol>
<li><strong>Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting</strong> Haitao Lin, Zhangyang Gao, Yongjie Xu, Lirong Wu, Ling Li, Stan Z. Li</li>
<li><strong>Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration</strong>. Sijie He, Xinyan Li, Laurie Trenary, Benjamin A. Cash, Timothy DelSole, Arindam Banerjee</li>
</ol>
<h3 id="Driver-request-assignment"><a href="#Driver-request-assignment" class="headerlink" title="Driver-request assignment"></a>Driver-request assignment</h3><ol>
<li><strong>Real-Time Driver-Request Assignment in Ridesourcing</strong>. Hao Wang, Xiaohui Bei</li>
</ol>
<h3 id="Time-series-anomaly-detection"><a href="#Time-series-anomaly-detection" class="headerlink" title="Time-series anomaly detection"></a>Time-series anomaly detection</h3><ol>
<li><strong>Towards a Rigorous Evaluation of Time-Series Anomaly Detection</strong>. Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon</li>
</ol>
<h3 id="Spatiotemporal-Graph"><a href="#Spatiotemporal-Graph" class="headerlink" title="Spatiotemporal Graph"></a>Spatiotemporal Graph</h3><ol>
<li><strong>Disentangled Spatiotemporal Graph Generative Model</strong>. Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Zhao Liang</li>
</ol>
<h3 id="Urban-crime-prediction"><a href="#Urban-crime-prediction" class="headerlink" title="Urban crime prediction"></a>Urban crime prediction</h3><ol>
<li><strong>Multi-Type Urban Crime Prediction</strong>. Xiangyu Zhao, Wenqi Fan, Hui Liu, Jiliang Tang</li>
<li><strong>HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting</strong>. Chenyu Wang, Zongyu Lin, Xiaochen Yang, Jiao Sun, Mingxuan Yue, Cyrus Shahabi</li>
</ol>
<h3 id="Time-series-forecasting"><a href="#Time-series-forecasting" class="headerlink" title="Time series forecasting"></a>Time series forecasting</h3><ol>
<li><strong>CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting</strong>. Hui He, Qi Zhang, Simeng Bai, Kun Yi, Zhendong Niu</li>
<li><strong>Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting</strong>. Yuwei Fu, Di Wu, Benoit Boulet</li>
</ol>
<h3 id="Spatio-temporal-patterns-modeling"><a href="#Spatio-temporal-patterns-modeling" class="headerlink" title="Spatio-temporal patterns modeling"></a>Spatio-temporal patterns modeling</h3><ol>
<li><strong>SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss</strong>. Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio, Daniel B. Neill</li>
</ol>
<h3 id="Time-series-representation"><a href="#Time-series-representation" class="headerlink" title="Time series representation"></a>Time series representation</h3><ol>
<li><strong>TS2Vec: Towards Universal Representation of Time Series</strong>. Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, Bixiong Xu</li>
</ol>
<h3 id="Extreme-events-modeling"><a href="#Extreme-events-modeling" class="headerlink" title="Extreme events modeling"></a>Extreme events modeling</h3><ol>
<li><strong>DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events</strong>. Tyler Wilson, Pang-Ning Tan, Lifeng Luo</li>
</ol>
<h3 id="Multimodal-mobility-nowcasting"><a href="#Multimodal-mobility-nowcasting" class="headerlink" title="Multimodal mobility nowcasting"></a>Multimodal mobility nowcasting</h3><ol>
<li><strong>Event-Aware Multimodal Mobility Nowcasting</strong>. Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki</li>
</ol>
<h3 id="Time-series-analysis-and-embedding"><a href="#Time-series-analysis-and-embedding" class="headerlink" title="Time series analysis and embedding"></a>Time series analysis and embedding</h3><ol>
<li><strong>I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding</strong>. Sirisha Rambhatla, Zhengping Che, Yan Liu</li>
</ol>
<h3 id="Time-series-generation"><a href="#Time-series-generation" class="headerlink" title="Time series generation"></a>Time series generation</h3><ol>
<li><strong>Conditional Loss and Deep Euler Scheme for Time Series Generation</strong>. Carl Remlinger, Joseph Mikael, Romuald Elie</li>
</ol>
<h3 id="Air-pollution-monitoring"><a href="#Air-pollution-monitoring" class="headerlink" title="Air pollution monitoring"></a>Air pollution monitoring</h3><ol>
<li><strong>Bayesian Optimisation for Active Monitoring of Air Pollution</strong>. Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard</li>
</ol>
<h3 id="Air-quality-inference"><a href="#Air-quality-inference" class="headerlink" title="Air quality inference"></a>Air quality inference</h3><ol>
<li><strong>Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference</strong>. Zeel B Patel, Palak Purohit, Harsh Patel, Shivam Sahni, Nipun Batra</li>
</ol>
<h3 id="Epidemic-forecasting"><a href="#Epidemic-forecasting" class="headerlink" title="Epidemic forecasting"></a>Epidemic forecasting</h3><ol>
<li><strong>CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting</strong>. Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, Madhav Marathe</li>
</ol>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>时空数据挖掘</tag>
<tag>AAAI 2022</tag>
</tags>
</entry>
<entry>
<title>【ICLR 2022】时空数据挖掘论文</title>
<url>/posts/38a6ca75/</url>
<content><![CDATA[<p>对ICLR 2022的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，并进行任务分类。</p>
<span id="more"></span>
<p>ICLR 2022完整录用论文列表：<a href="https://openreview.net/group?id=ICLR.cc/2022/Conference#oral-submissions">https://openreview.net/group?id=ICLR.cc/2022/Conference#oral-submissions</a></p>
<h3 id="Time-series-signal-analysis"><a href="#Time-series-signal-analysis" class="headerlink" title="Time series signal analysis"></a>Time series signal analysis</h3><ol>
<li><a href="https://openreview.net/forum?id=U4uFaLyg7PV">T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis</a>. Minhao LIU, Ailing Zeng, Qiuxia LAI, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu</li>
</ol>
<h3 id="Time-series-forecasting"><a href="#Time-series-forecasting" class="headerlink" title="Time series forecasting"></a>Time series forecasting</h3><ol>
<li><a href="https://openreview.net/forum?id=JpNH4CW_zl">Multivariate Time Series Forecasting with Latent Graph Inference</a>. Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski</li>
<li><a href="https://openreview.net/forum?id=wv6g8fWLX2q">TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting</a>. Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, Yulia Gel</li>
<li><a href="https://openreview.net/forum?id=0EXmFzUn5I">Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting</a>. Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar</li>
<li><a href="https://openreview.net/forum?id=AJAR-JgNw__">DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting</a>. Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu</li>
<li><a href="https://openreview.net/forum?id=PilZY3omXV2">CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting</a>. Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi</li>
<li><a href="https://openreview.net/forum?id=cGDAkQo1C0p">Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift</a>. Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, Jaegul Choo</li>
</ol>
<h3 id="Time-series-classification"><a href="#Time-series-classification" class="headerlink" title="Time series classification"></a>Time series classification</h3><ol>
<li><a href="https://openreview.net/forum?id=PDYs7Z2XFGv">Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification</a>. Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang</li>
</ol>
<h3 id="Time-series-anomaly-detection"><a href="#Time-series-anomaly-detection" class="headerlink" title="Time series anomaly detection"></a>Time series anomaly detection</h3><ol>
<li><a href="https://openreview.net/forum?id=45L_dgP48Vd">Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series</a>. Enyan Dai, Jie Chen</li>
<li><a href="https://openreview.net/forum?id=LzQQ89U1qm_">Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy</a>. Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long</li>
</ol>
<h3 id="Time-series-imputation"><a href="#Time-series-imputation" class="headerlink" title="Time series imputation"></a>Time series imputation</h3><ol>
<li><a href="https://openreview.net/forum?id=kOu3-S3wJ7">Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks</a>. Andrea Cini, Ivan Marisca, Cesare Alippi</li>
</ol>
<h3 id="Spatial-Temporal-Representation-Learning"><a href="#Spatial-Temporal-Representation-Learning" class="headerlink" title="Spatial-Temporal Representation Learning"></a>Spatial-Temporal Representation Learning</h3><ol>
<li><a href="https://openreview.net/forum?id=nBU_u6DLvoK">UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning</a>. Kunchang Li, Yali Wang, Gao Peng, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao</li>
<li><a href="https://openreview.net/forum?id=Jh9VxCkrEZn">Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs</a>. Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan</li>
</ol>
<h3 id="Spatial-temporal-GNN"><a href="#Spatial-temporal-GNN" class="headerlink" title="Spatial-temporal GNN"></a>Spatial-temporal GNN</h3><ol>
<li><a href="https://openreview.net/forum?id=XJiajt89Omg">Space-Time Graph Neural Networks</a>. Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro</li>
</ol>
<h3 id="Traffic-forecasting"><a href="#Traffic-forecasting" class="headerlink" title="Traffic forecasting"></a>Traffic forecasting</h3><ol>
<li><a href="https://openreview.net/forum?id=wwDg3bbYBIq">Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting</a>. Hyunwook Lee, Seungmin Jin, Hyeshin Chu, Hongkyu Lim, Sungahn Ko</li>
</ol>
<h3 id="Trajectory-prediction"><a href="#Trajectory-prediction" class="headerlink" title="Trajectory prediction"></a>Trajectory prediction</h3><ol>
<li><a href="https://openreview.net/forum?id=POxF-LEqnF">You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction</a>. Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf</li>
</ol>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>时空数据挖掘</tag>
<tag>ICLR 2022</tag>
</tags>
</entry>
<entry>
<title>【ICLR 2024】时空数据挖掘论文</title>
<url>/posts/3d1ac43b/</url>
<content><![CDATA[<p>对ICLR 2024的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，并进行任务分类，共50篇。</p>
<span id="more"></span>
<h1 id="Air-Quality-Prediction-1-空气质量预测"><a href="#Air-Quality-Prediction-1-空气质量预测" class="headerlink" title="Air Quality Prediction (1) 空气质量预测"></a>Air Quality Prediction (1) 空气质量预测</h1><ol>
<li><strong>AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction</strong>. <a href="https://openreview.net/forum?id=JW3jTjaaAB">https://openreview.net/forum?id=JW3jTjaaAB</a></li>
</ol>
<h1 id="Climate-Forecasting-1-气候预测"><a href="#Climate-Forecasting-1-气候预测" class="headerlink" title="Climate Forecasting (1) 气候预测"></a>Climate Forecasting (1) 气候预测</h1><ol>
<li><strong>ClimODE: Climate Forecasting With Physics-informed Neural ODEs</strong>. <a href="https://openreview.net/forum?id=xuY33XhEGR">https://openreview.net/forum?id=xuY33XhEGR</a></li>
</ol>
<h1 id="Dynamic-Graph-1-动态图"><a href="#Dynamic-Graph-1-动态图" class="headerlink" title="Dynamic Graph (1) 动态图"></a>Dynamic Graph (1) 动态图</h1><ol>
<li><strong>Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks</strong>. <a href="https://openreview.net/forum?id=AJBkfwXh3u">https://openreview.net/forum?id=AJBkfwXh3u</a></li>
</ol>
<h1 id="Geospatial-Knowledge-Extraction-1-地理空间知识提取"><a href="#Geospatial-Knowledge-Extraction-1-地理空间知识提取" class="headerlink" title="Geospatial Knowledge Extraction (1) 地理空间知识提取"></a>Geospatial Knowledge Extraction (1) 地理空间知识提取</h1><ol>
<li><strong>GeoLLM: Extracting Geospatial Knowledge from Large Language Models</strong>. <a href="https://openreview.net/forum?id=TqL2xBwXP3">https://openreview.net/forum?id=TqL2xBwXP3</a></li>
</ol>
<h1 id="Long-term-Time-Series-Forecasting-2-长时间序列预测"><a href="#Long-term-Time-Series-Forecasting-2-长时间序列预测" class="headerlink" title="Long-term Time Series Forecasting (2) 长时间序列预测"></a>Long-term Time Series Forecasting (2) 长时间序列预测</h1><ol>
<li><strong>Periodicity Decoupling Framework for Long-term Series Forecasting</strong>. <a href="https://openreview.net/forum?id=dp27P5HBBt">https://openreview.net/forum?id=dp27P5HBBt</a></li>
<li><strong>Self-Supervised Contrastive Forecasting</strong>. <a href="https://openreview.net/forum?id=nBCuRzjqK7">https://openreview.net/forum?id=nBCuRzjqK7</a></li>
</ol>
<h1 id="Spatio-Temporal-Graph-Transfer-Learning-1-时空图迁移学习"><a href="#Spatio-Temporal-Graph-Transfer-Learning-1-时空图迁移学习" class="headerlink" title="Spatio-Temporal Graph Transfer Learning (1) 时空图迁移学习"></a>Spatio-Temporal Graph Transfer Learning (1) 时空图迁移学习</h1><ol>
<li><strong>A Generative Pre-Training Framework for Spatio-Temporal Graph Transfer Learning</strong>. <a href="https://openreview.net/forum?id=QyFm3D3Tzi">https://openreview.net/forum?id=QyFm3D3Tzi</a></li>
</ol>
<h1 id="Spatio-Temporal-Causal-inference-1-时空因果推断"><a href="#Spatio-Temporal-Causal-inference-1-时空因果推断" class="headerlink" title="Spatio-Temporal Causal inference (1) 时空因果推断"></a>Spatio-Temporal Causal inference (1) 时空因果推断</h1><ol>
<li><strong>NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling</strong>. <a href="https://openreview.net/forum?id=sLdVl0q68X">https://openreview.net/forum?id=sLdVl0q68X</a></li>
</ol>
<h1 id="Time-Series-Forecasting-20-时间序列预测"><a href="#Time-Series-Forecasting-20-时间序列预测" class="headerlink" title="Time Series Forecasting (20) 时间序列预测"></a>Time Series Forecasting (20) 时间序列预测</h1><ol>
<li><strong>Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values</strong>. <a href="https://openreview.net/forum?id=O9nZCwdGcG">https://openreview.net/forum?id=O9nZCwdGcG</a></li>
<li><strong>CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=MJksrOhurE">https://openreview.net/forum?id=MJksrOhurE</a></li>
<li><strong>Copula Conformal prediction for multi-step time series prediction</strong>. <a href="https://openreview.net/forum?id=ojIJZDNIBj">https://openreview.net/forum?id=ojIJZDNIBj</a></li>
<li><strong>DAM: A Foundation Model for Forecasting</strong>. <a href="https://openreview.net/forum?id=4NhMhElWqP">https://openreview.net/forum?id=4NhMhElWqP</a></li>
<li><strong>iTransformer: Inverted Transformers Are Effective for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=JePfAI8fah">https://openreview.net/forum?id=JePfAI8fah</a></li>
<li><strong>Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction</strong>. <a href="https://openreview.net/forum?id=aFWUY3E7ws">https://openreview.net/forum?id=aFWUY3E7ws</a></li>
<li><strong>Multi-Resolution Diffusion Models for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=mmjnr0G8ZY">https://openreview.net/forum?id=mmjnr0G8ZY</a></li>
<li><strong>MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process</strong>. <a href="https://openreview.net/forum?id=CZiY6OLktd">https://openreview.net/forum?id=CZiY6OLktd</a></li>
<li><strong>Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=lJkOCMP2aW">https://openreview.net/forum?id=lJkOCMP2aW</a></li>
<li><strong>Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators</strong>. <a href="https://openreview.net/forum?id=JiTVtCUOpS">https://openreview.net/forum?id=JiTVtCUOpS</a></li>
<li><strong>RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies</strong>. <a href="https://openreview.net/forum?id=ltZ9ianMth">https://openreview.net/forum?id=ltZ9ianMth</a></li>
<li><strong>SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series</strong>. <a href="https://openreview.net/forum?id=s9z0HzWJJp">https://openreview.net/forum?id=s9z0HzWJJp</a></li>
<li><strong>STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction</strong>. <a href="https://openreview.net/forum?id=6iwg437CZs">https://openreview.net/forum?id=6iwg437CZs</a></li>
<li><strong>Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=qae04YACHs">https://openreview.net/forum?id=qae04YACHs</a></li>
<li><strong>Time-LLM: Time Series Forecasting by Reprogramming Large Language Models</strong>. <a href="https://openreview.net/forum?id=Unb5CVPtae">https://openreview.net/forum?id=Unb5CVPtae</a></li>
<li><strong>TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=YH5w12OUuU">https://openreview.net/forum?id=YH5w12OUuU</a></li>
<li><strong>TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series</strong>. <a href="https://openreview.net/forum?id=xtOydkE1Ku">https://openreview.net/forum?id=xtOydkE1Ku</a></li>
<li><strong>Towards Transparent Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=TYXtXLYHpR">https://openreview.net/forum?id=TYXtXLYHpR</a></li>
<li><strong>TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=7oLshfEIC2">https://openreview.net/forum?id=7oLshfEIC2</a></li>
<li><strong>VQ-TR: Vector Quantized Attention for Time Series Forecasting</strong>. <a href="https://openreview.net/forum?id=IxpTsFS7mh">https://openreview.net/forum?id=IxpTsFS7mh</a></li>
</ol>
<h1 id="Temporal-Graphs-1-时序图"><a href="#Temporal-Graphs-1-时序图" class="headerlink" title="Temporal Graphs (1) 时序图"></a>Temporal Graphs (1) 时序图</h1><ol>
<li><strong>Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs</strong>. <a href="https://openreview.net/forum?id=uvFhCUPjtI">https://openreview.net/forum?id=uvFhCUPjtI</a></li>
</ol>
<h1 id="Time-Series-Imputation-1-时间序列填补"><a href="#Time-Series-Imputation-1-时间序列填补" class="headerlink" title="Time Series Imputation (1) 时间序列填补"></a>Time Series Imputation (1) 时间序列填补</h1><ol>
<li><strong>Conditional Information Bottleneck Approach for Time Series Imputation</strong>. <a href="https://openreview.net/forum?id=K1mcPiDdOJ">https://openreview.net/forum?id=K1mcPiDdOJ</a></li>
</ol>
<h1 id="Time-Series-Causal-Discovery-1-时间序列因果发现"><a href="#Time-Series-Causal-Discovery-1-时间序列因果发现" class="headerlink" title="Time Series Causal Discovery (1) 时间序列因果发现"></a>Time Series Causal Discovery (1) 时间序列因果发现</h1><ol>
<li><strong>CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery</strong>. <a href="https://openreview.net/forum?id=iad1yyyGme">https://openreview.net/forum?id=iad1yyyGme</a></li>
</ol>
<h1 id="Time-Series-Generation-2-时间序列生成"><a href="#Time-Series-Generation-2-时间序列生成" class="headerlink" title="Time Series Generation (2) 时间序列生成"></a>Time Series Generation (2) 时间序列生成</h1><ol>
<li><strong>Diffusion-TS: Interpretable Diffusion for General Time Series Generation</strong>. <a href="https://openreview.net/forum?id=4h1apFjO99">https://openreview.net/forum?id=4h1apFjO99</a></li>
<li><strong>Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs</strong>. <a href="https://openreview.net/forum?id=eY7sLb0dVF">https://openreview.net/forum?id=eY7sLb0dVF</a></li>
</ol>
<h1 id="Time-Series-Representations-2-时间序列表示"><a href="#Time-Series-Representations-2-时间序列表示" class="headerlink" title="Time Series Representations (2) 时间序列表示"></a>Time Series Representations (2) 时间序列表示</h1><ol>
<li><strong>Disentangling Time Series Representations via Contrastive based $l$− Variational Inference</strong>. <a href="https://openreview.net/forum?id=iI7hZSczxE">https://openreview.net/forum?id=iI7hZSczxE</a> </li>
<li><strong>T-Rep: Representation Learning for Time Series using Time-Embeddings</strong>. <a href="https://openreview.net/forum?id=3y2TfP966N">https://openreview.net/forum?id=3y2TfP966N</a></li>
</ol>
<h1 id="Time-Series-Analysis-2-时间序列分析"><a href="#Time-Series-Analysis-2-时间序列分析" class="headerlink" title="Time Series Analysis (2) 时间序列分析"></a>Time Series Analysis (2) 时间序列分析</h1><ol>
<li><strong>FITS: Modeling Time Series with 10$k$ Parameters</strong>. <a href="https://openreview.net/forum?id=bWcnvZ3qMb">https://openreview.net/forum?id=bWcnvZ3qMb</a></li>
<li><strong>ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis</strong>. <a href="https://openreview.net/forum?id=vpJMJerXHU">https://openreview.net/forum?id=vpJMJerXHU</a></li>
</ol>
<h1 id="Time-Series-Explanation-1-时间序列解释"><a href="#Time-Series-Explanation-1-时间序列解释" class="headerlink" title="Time Series Explanation (1) 时间序列解释"></a>Time Series Explanation (1) 时间序列解释</h1><ol>
<li><strong>Explaining Time Series via Contrastive and Locally Sparse Perturbations</strong>. <a href="https://openreview.net/forum?id=qDdSRaOiyb">https://openreview.net/forum?id=qDdSRaOiyb</a></li>
</ol>
<h1 id="Time-Series-Pattern-Recognition-1-时间序列模式识别"><a href="#Time-Series-Pattern-Recognition-1-时间序列模式识别" class="headerlink" title="Time Series Pattern Recognition (1) 时间序列模式识别"></a>Time Series Pattern Recognition (1) 时间序列模式识别</h1><ol>
<li><strong>Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns</strong>. <a href="https://openreview.net/forum?id=CdjnzWsQax">https://openreview.net/forum?id=CdjnzWsQax</a></li>
</ol>
<h1 id="Time-Series-Embedding-3-时间序列嵌入"><a href="#Time-Series-Embedding-3-时间序列嵌入" class="headerlink" title="Time Series Embedding (3) 时间序列嵌入"></a>Time Series Embedding (3) 时间序列嵌入</h1><ol>
<li><strong>GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings</strong>. <a href="https://openreview.net/forum?id=c56TWtYp0W">https://openreview.net/forum?id=c56TWtYp0W</a></li>
<li><strong>Learning to Embed Time Series Patches Independently</strong>. <a href="https://openreview.net/forum?id=WS7GuBDFa2">https://openreview.net/forum?id=WS7GuBDFa2</a></li>
<li><strong>TEST: Text Prototype Aligned Embedding to Activate LLM’s Ability for Time Series</strong>. <a href="https://openreview.net/forum?id=Tuh4nZVb0g">https://openreview.net/forum?id=Tuh4nZVb0g</a></li>
</ol>
<h1 id="Time-Series-Classification-2-时间序列分类"><a href="#Time-Series-Classification-2-时间序列分类" class="headerlink" title="Time Series Classification (2) 时间序列分类"></a>Time Series Classification (2) 时间序列分类</h1><ol>
<li><strong>Inherently Interpretable Time Series Classification via Multiple Instance Learning</strong>. <a href="https://openreview.net/forum?id=xriGRsoAza">https://openreview.net/forum?id=xriGRsoAza</a></li>
<li><strong>Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data</strong>. <a href="https://openreview.net/forum?id=4VIgNuQ1pY">https://openreview.net/forum?id=4VIgNuQ1pY</a></li>
</ol>
<h1 id="Time-Series-Alignment-1-时间序列对齐"><a href="#Time-Series-Alignment-1-时间序列对齐" class="headerlink" title="Time Series Alignment (1) 时间序列对齐"></a>Time Series Alignment (1) 时间序列对齐</h1><ol>
<li><strong>Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data</strong>. <a href="https://openreview.net/forum?id=9zhHVyLY4K">https://openreview.net/forum?id=9zhHVyLY4K</a></li>
</ol>
<h1 id="Time-Series-Contrastive-Learning-4-时间序列对比学习"><a href="#Time-Series-Contrastive-Learning-4-时间序列对比学习" class="headerlink" title="Time Series Contrastive Learning (4) 时间序列对比学习"></a>Time Series Contrastive Learning (4) 时间序列对比学习</h1><ol>
<li><strong>Parametric Augmentation for Time Series Contrastive Learning</strong>. <a href="https://openreview.net/forum?id=EIPLdFy3vp">https://openreview.net/forum?id=EIPLdFy3vp</a></li>
<li><strong>Retrieval-Based Reconstruction For Time-series Contrastive Learning</strong>. <a href="https://openreview.net/forum?id=3zQo5oUvia">https://openreview.net/forum?id=3zQo5oUvia</a></li>
<li><strong>Soft Contrastive Learning for Time Series</strong>. <a href="https://openreview.net/forum?id=pAsQSWlDUf">https://openreview.net/forum?id=pAsQSWlDUf</a></li>
<li><strong>Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach</strong>. <a href="https://openreview.net/forum?id=K2c04ulKXn">https://openreview.net/forum?id=K2c04ulKXn</a></li>
</ol>
<h1 id="Traffic-Predictoin-1-交通预测"><a href="#Traffic-Predictoin-1-交通预测" class="headerlink" title="Traffic Predictoin (1) 交通预测"></a>Traffic Predictoin (1) 交通预测</h1><ol>
<li><strong>TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts</strong>. <a href="https://openreview.net/forum?id=N0nTk5BSvO">https://openreview.net/forum?id=N0nTk5BSvO</a></li>
</ol>
<h1 id="更多"><a href="#更多" class="headerlink" title="更多"></a>更多</h1><ol>
<li>时空数据挖掘论文库 <a href="https://github.com/xiepeng21/research_spatio-temporal-data-mining">Research of Spatio-Temporal Data Mining</a></li>
<li>【KDD 2023】时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/649761763">半亩方塘：【KDD 2023】时空数据挖掘论文</a></li>
<li>【ICLR 2023】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/629231015">半亩方塘：【ICLR 2023】时空数据挖掘论文</a></li>
<li>【NeurIPS 2023】时空数据挖掘论文 <a href="https://xiepeng21.cn/posts/3727adf0/">【NeurIPS 2023】时空数据挖掘论文</a></li>
<li>【ICLR 2022】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/478362107">半亩方塘：【ICLR 2022】时空数据挖掘论文</a></li>
<li>【ICML 2022】时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/548907678">半亩方塘：【ICML 2022】时空数据挖掘论文</a></li>
<li>【IJCAI 2022】时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/547781911">半亩方塘：【IJCAI 2022】时空数据挖掘论文</a></li>
<li>【KDD 2022】时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/547262551">半亩方塘：【KDD 2022】时空数据挖掘论文</a></li>
<li>【WSDM 2022】时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/478363400">半亩方塘：【WSDM 2022】时空数据挖掘论文</a></li>
<li>【AAAI 2022】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/478357750">半亩方塘：【AAAI 2022】时空数据挖掘论文</a></li>
<li>近3年用于时空数据挖掘的图神经网络论文（2018-2021）<a href="https://zhuanlan.zhihu.com/p/420738345">半亩方塘：【图神经网络】近3年用于时空数据挖掘的图神经网络论文（2018-2021）</a></li>
<li>【KDD 2021】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/423342733">半亩方塘：【KDD 2021 】时空数据挖掘论文</a></li>
<li>【IJCAI 2021】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/423323595">半亩方塘：【IJCAI 2021 】时空数据挖掘论文</a></li>
<li>【WWW 2021】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/440291388">半亩方塘：【WWW 2021 】时空数据挖掘论文</a></li>
<li>【ACM SIGSPATIAL 2021】 时空数据挖掘论文 <a href="https://zhuanlan.zhihu.com/p/442419220">半亩方塘：【ACM SIGSPATIAL 2021】时空数据挖掘论文</a></li>
<li>【时空数据挖掘】推开窗，看一看 <a href="https://zhuanlan.zhihu.com/p/573936494">半亩方塘：【时空数据挖掘】推开窗，看一看</a></li>
<li>【时空数据挖掘】 - 层次体系构建 <a href="https://zhuanlan.zhihu.com/p/435895312">半亩方塘：【时空数据挖掘】 - 层次体系构建</a></li>
<li>【时空数据挖掘】- 任务 <a href="https://zhuanlan.zhihu.com/p/426898203">半亩方塘：【时空数据挖掘】- 任务</a></li>
<li>【时空数据挖掘】- 方法 <a href="https://zhuanlan.zhihu.com/p/435890647">半亩方塘：【时空数据挖掘】- 方法</a></li>
<li>【时空数据挖掘】- 数据 <a href="https://zhuanlan.zhihu.com/p/435882797">半亩方塘：【时空数据挖掘】- 数据</a></li>
</ol>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>时空数据挖掘</tag>
<tag>ICLR 2024</tag>
</tags>
</entry>
<entry>
<title>【WSDM 2022】时空数据挖掘论文</title>
<url>/posts/e5ed55f3/</url>
<content><![CDATA[<p>对WSDM 2022的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，并进行任务分类。</p>
<span id="more"></span>
<p>WSDM 2022 完整录用论文列表：<a href="https://www.wsdm-conference.org/2022/accepted-papers/">https://www.wsdm-conference.org/2022/accepted-papers/</a></p>
<h3 id="Human-mobility-prediction"><a href="#Human-mobility-prediction" class="headerlink" title="Human mobility prediction"></a>Human mobility prediction</h3><p><a href="https://dl.acm.org/doi/10.1145/3488560.3498438">RLMob: Deep Reinforcement Learning for Successive Mobility Prediction</a>. Ziyan Luo, Congcong Miao</p>
<h3 id="Precipitation-forecasting"><a href="#Precipitation-forecasting" class="headerlink" title="Precipitation forecasting"></a>Precipitation forecasting</h3><p><a href="https://dl.acm.org/doi/10.1145/3488560.3498448">A New Class of Polynomial Activation Functions of Deep Learning for Precipitation Forecasting</a>. Jiachuan Wang, Lei Chen, Charles Wang Wai Ng</p>
<h3 id="Route-recommendation"><a href="#Route-recommendation" class="headerlink" title="Route recommendation"></a>Route recommendation</h3><p><a href="https://dl.acm.org/doi/10.1145/3488560.3498512">Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining</a>. Zhan Wang, Zhaohui Peng, Senzhang Wang, Qiao Song</p>
<h3 id="Passenger-demand-prediction"><a href="#Passenger-demand-prediction" class="headerlink" title="Passenger demand prediction"></a>Passenger demand prediction</h3><p><a href="https://dl.acm.org/doi/10.1145/3488560.3498394">CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction</a>. Yudong Zhang, Binwu Wang, Ziyang Shan, Zhengyang Zhou, Yang Wang</p>
<h3 id="Urban-flow-prediction"><a href="#Urban-flow-prediction" class="headerlink" title="Urban flow prediction"></a>Urban flow prediction</h3><p><a href="https://dl.acm.org/doi/10.1145/3488560.3498444">ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction</a>. Liang Zhao, Min Gao, Zongwei Wang</p>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>时空数据挖掘</tag>
<tag>WSDM 2022</tag>
</tags>
</entry>
<entry>
<title>【NeurIPS 2023】时空数据挖掘论文</title>
<url>/posts/3727adf0/</url>
<content><![CDATA[<p>对NeurIPS 2023的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，如下。</p>
<span id="more"></span>
<p>NeurIPS 2023论文录用列表： <a href="https://openreview.net/group?id=NeurIPS.cc/2023/Conference">https://openreview.net/group?id=NeurIPS.cc/2023/Conference</a></p>
<ul>
<li>Automatic Integration for Spatiotemporal Neural Point Processes. Zihao Zhou, Rose Yu. <a href="https://neurips.cc/virtual/2023/poster/72359">paper</a></li>
<li>Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective. Zhiding Liu, Mingyue Cheng, Zhi Li, Zhenya Huang, Qi Liu, Yanhu Xie, Enhong Chen. <a href="https://neurips.cc/virtual/2023/poster/72816">paper</a></li>
<li>BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis. Zelin Ni, Hang Yu, Shizhan Liu, Jianguo Li , Weiyao Lin. <a href="https://neurips.cc/virtual/2023/poster/69976">paper</a></li>
<li>BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series. Andrea Nascetti, Ritu Yadav, Kirill Brodt, Qixun Qu, Hongwei Fan, Yuri Shendryk, Isha Shah, Christine Chung. <a href="https://neurips.cc/virtual/2023/poster/73499">paper</a></li>
<li>Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis. Yuanshao Zhu, Yongchao Ye, Ying Wu, Xiangyu Zhao, James Yu. <a href="https://neurips.cc/virtual/2023/poster/73469">paper</a></li>
<li>Contrast Everything: Multi-Granularity Representation Learning for Medical Time-Series. Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang. <a href="https://neurips.cc/virtual/2023/poster/70272">paper</a></li>
<li>ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling. Yuqi Chen, Kan Ren, Yansen Wang, Yuchen Fang, Weiwei Sun, Dongsheng Li. <a href="https://neurips.cc/virtual/2023/poster/71304">paper</a></li>
<li>Causal Discovery from Subsampled Time Series with Proxy Variables. Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang. <a href="https://neurips.cc/virtual/2023/poster/70936">paper</a></li>
<li>Causal Discovery in Semi-Stationary Time Series. Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan Rossi, Murat Kocaoglu. <a href="https://neurips.cc/virtual/2023/poster/71016">paper</a></li>
<li>CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement. Qihe Huang, Lei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, Yang Wang. <a href="https://neurips.cc/virtual/2023/poster/70010">paper</a></li>
<li>Conformal Prediction for Time Series with Modern Hopfield Networks. Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter. <a href="https://neurips.cc/virtual/2023/poster/72007">paper</a></li>
<li>Conformal Scorecasting: Anticipatory Uncertainty Quantification for Distribution Shift in Time Series. Anastasios Angelopoulos, Ryan Tibshirani, Emmanuel Candes. <a href="https://neurips.cc/virtual/2023/poster/69896">paper</a></li>
<li>DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model. Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James Yu. <a href="https://neurips.cc/virtual/2023/poster/69935">paper</a></li>
<li>DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting. Salva Rühling Cachay, Bo Zhao, Hailey James, Rose Yu. <a href="https://neurips.cc/virtual/2023/poster/71410">paper</a></li>
<li>Drift doesn’t Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection. Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Xingyu Wang, Zirui Zhuang, Jianxin Liao. <a href="https://neurips.cc/virtual/2023/poster/71195">paper</a><a href="https://github.com/ForestsKing/D3R">code</a></li>
<li>Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment. Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann. <a href="https://neurips.cc/virtual/2023/poster/73036">paper</a></li>
<li>Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics. Liming Wu, Zhichao Hou, Jirui Yuan, Yu Rong, Wenbing Huang. <a href="https://neurips.cc/virtual/2023/poster/72921">paper</a></li>
<li>Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency. Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik. <a href="https://neurips.cc/virtual/2023/poster/69958">paper</a></li>
<li>Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics. Koen Minartz, Yoeri Poels, Simon Koop, Vlado Menkovski. <a href="https://neurips.cc/virtual/2023/poster/72442">paper</a></li>
<li>Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling. Sachin Chauhan, Zeel Bharatkumar Patel, Sayan Ranu, Rijurekha Sen, Nipun Batra. <a href="https://neurips.cc/virtual/2023/poster/73476">paper</a></li>
<li>Frequency-domain MLPs are More Effective Learners in Time Series Forecasting. Kun Yi, Qi Zhang, Wei Fan, Hui He, Pengyang Wang, Shoujin Wang, Ning An, Defu Lian, Longbing Cao, Zhendong Niu. <a href="https://neurips.cc/virtual/2023/poster/70726">paper</a></li>
<li>FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space. Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu, Ruijie Wang, Jinyang Li, Suhas Diggavi, Mani Srivastava, Tarek Abdelzaher. <a href="https://neurips.cc/virtual/2023/poster/70617">paper</a></li>
<li>FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective. Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, Zhendong Niu. <a href="https://neurips.cc/virtual/2023/poster/71159">paper</a></li>
<li>Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning. Berken Utku Demirel, Christian Holz. <a href="https://neurips.cc/virtual/2023/poster/71014">paper</a><a href="https://github.com/eth-siplab/Finding_Order_in_Chaos">code</a></li>
<li>Generative Pre-Training of Spatio-Temporal Graph Neural Networks. Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang. <a href="https://neurips.cc/virtual/2023/poster/70508">paper</a></li>
<li>Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. Yixuan Zhang, Quyu Kong, Feng Zhou. <a href="https://neurips.cc/virtual/2023/poster/71268">paper</a></li>
<li>Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors. Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long. <a href="https://neurips.cc/virtual/2023/poster/72562">paper</a><a href="https://github.com/thuml/Koopa">code</a></li>
<li>LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann. <a href="https://neurips.cc/virtual/2023/poster/73480">paper</a><a href="https://github.com/liuxu77/LargeST">code</a></li>
<li>Large Language Models Are Zero Shot Time Series Forecasters. Marc Finzi, Nate Gruver, Shikai Qiu, Andrew Wilson. <a href="https://neurips.cc/virtual/2023/poster/70543">paper</a></li>
<li>MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection. Junho Song, Keonwoo Kim, Jeonglyul Oh, Sungzoon Cho. <a href="https://neurips.cc/virtual/2023/poster/71519">paper</a><a href="https://github.com/gunny97/MEMTO">code</a></li>
<li>Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction. Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao, Jeffrey H Lang, Duane Boning. <a href="https://neurips.cc/virtual/2023/poster/70582">paper</a><a href="https://github.com/andrewlai61616/NPSR">code</a></li>
<li>OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning. Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z. Li. <a href="https://neurips.cc/virtual/2023/poster/73674">paper</a><a href="https://github.com/chengtan9907/OpenSTL">code</a></li>
<li>OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling. Yifan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan. <a href="https://neurips.cc/virtual/2023/poster/71725">paper</a><a href="https://github.com/yfzhang114/OneNet">code</a></li>
<li>One Fits All: Power General Time Series Analysis by Pretrained LM. Tian Zhou, Peisong Niu, xue wang, Liang Sun, Rong Jin. <a href="https://neurips.cc/virtual/2023/poster/70856">paper</a></li>
<li>On the Constrained Time-Series Generation Problem. Andrea Coletta, Sriram Gopalakrishnan, Daniel Borrajo, Svitlana Vyetrenko. <a href="https://neurips.cc/virtual/2023/poster/72006">paper</a></li>
<li>Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting. Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang (Bernie) Wang. <a href="https://neurips.cc/virtual/2023/poster/70377">paper</a></li>
<li>SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang. <a href="https://neurips.cc/virtual/2023/poster/70628">paper</a></li>
<li>Sparse Graph Learning from Spatiotemporal Time Series. Andrea Cini, Daniele Zambon, Cesare Alippi. <a href="https://neurips.cc/virtual/2023/poster/73924">paper</a></li>
<li>Sparse Deep Learning for Time Series Data: Theory and Applications. Mingxuan Zhang, Yan Sun, Faming Liang. <a href="https://neurips.cc/virtual/2023/poster/72629">paper</a></li>
<li>Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels. Zhen Liu, ma peitian, Dongliang Chen, Wenbin Pei, Qianli Ma. <a href="https://neurips.cc/virtual/2023/poster/72608">paper</a><a href="https://github.com/qianlima-lab/Scale-teaching">code</a></li>
<li>SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long. <a href="https://neurips.cc/virtual/2023/poster/70829">paper</a><a href="https://github.com/thuml/SimMTM">code</a></li>
<li>Taming Local Effects in Graph-based Spatiotemporal Forecasting. Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi. <a href="https://neurips.cc/virtual/2023/poster/70034">paper</a></li>
<li>Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings. Giovanni De Felice, John Goulermas, Vladimir Gusev. <a href="https://neurips.cc/virtual/2023/poster/71521">paper</a></li>
<li>Time Series as Images: Vision Transformer for Irregularly Sampled Time Series. Zekun Li, Shiyang Li, Xifeng Yan. <a href="https://neurips.cc/virtual/2023/poster/71219">paper</a></li>
<li>UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction. Yansong Ning, Hao Liu, Hao Wang, Zhenyu Zeng, Hui Xiong. <a href="https://neurips.cc/virtual/2023/poster/73440">paper</a><a href="https://github.com/usail-hkust/UUKG">code</a></li>
<li>What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context? Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio. <a href="https://neurips.cc/virtual/2023/poster/70031">paper</a><a href="https://github.com/gitbooo/CrossViVit">code</a></li>
<li>WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting. Yuxin Jia, Youfang Lin, Xinyan Hao, Yan Lin, Shengnan Guo, Huaiyu Wan. <a href="https://neurips.cc/virtual/2023/poster/69972">paper</a><a href="https://github.com/Water2sea/WITRAN">code</a></li>
<li>WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction. Sebastian Gerard, Yu Zhao, Josephine Sullivan. <a href="https://neurips.cc/virtual/2023/poster/73593">paper</a></li>
</ul>
<h1 id="发现更多"><a href="#发现更多" class="headerlink" title="发现更多"></a>发现更多</h1><ol>
<li><a href="https://www.zhihu.com/column/c_1437144001987792896">时空数据挖掘</a> </li>
<li><a href="https://github.com/xiepeng21/research_spatio-temporal-data-mining">时空数据挖掘论文库</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/573936494">半亩方塘：【时空数据挖掘】推开窗，看一看</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/435895312">半亩方塘：【时空数据挖掘】 - 层次体系构建</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/426898203">半亩方塘：【时空数据挖掘】- 任务</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/435890647">半亩方塘：【时空数据挖掘】- 方法</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/435882797">半亩方塘：【时空数据挖掘】- 数据</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/649761763">【KDD 2023】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/629231015">【ICLR 2023】 时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/478362107">半亩方塘：【ICLR 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/548907678">半亩方塘：【ICML 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/547781911">半亩方塘：【IJCAI 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/547262551">半亩方塘：【KDD 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/478363400">半亩方塘：【WSDM 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/478357750">半亩方塘：【AAAI 2022】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/420738345">半亩方塘：【图神经网络】近3年用于时空数据挖掘的图神经网络论文（2018-2021）</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/423342733">半亩方塘：【KDD 2021 】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/423323595">半亩方塘：【IJCAI 2021 】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/440291388">半亩方塘：【WWW 2021 】时空数据挖掘论文</a></li>
<li><a href="https://zhuanlan.zhihu.com/p/442419220">半亩方塘：【ACM SIGSPATIAL 2021】时空数据挖掘论文</a></li>
</ol>
<h1 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h1><ol>
<li>NeurIPS 2023 时间序列(Time Series)论文总结 <a href="https://zhuanlan.zhihu.com/p/659088918">https://zhuanlan.zhihu.com/p/659088918</a></li>
<li>NeurIPS 2023 时空数据论文总结 <a href="https://zhuanlan.zhihu.com/p/659050798">https://zhuanlan.zhihu.com/p/659050798</a></li>
<li>时空数据顶会论文总结 <a href="https://www.zhihu.com/column/c_1683852314232885248">https://www.zhihu.com/column/c_1683852314232885248</a></li>
</ol>
]]></content>
<categories>
<category>论文阅读</category>
</categories>
<tags>
<tag>时空数据挖掘</tag>
<tag>NeurIPS 2023</tag>
</tags>
</entry>
<entry>
<title>Ubuntu 16.04将cuda 10.1降到cuda10.0</title>
<url>/posts/8517e2d2/</url>
<content><![CDATA[<p>最近因为跑程序，需要用到cuda的版本为10.0，但是机器的cuda版本为10.1，所以就将其版本进行了降低。</p>
<span id="more"></span>
<h5 id="卸载cuda-10-1"><a href="#卸载cuda-10-1" class="headerlink" title="卸载cuda 10.1"></a>卸载cuda 10.1</h5><figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">sudo <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda-10.1/</span>bin/uninstall_cuda_10.<span class="hljs-number">1</span>.pl<br>sudo apt --purge remove nvidia*<br></code></pre></td></tr></table></figure>
<h5 id="下载cuda和cudnn"><a href="#下载cuda和cudnn" class="headerlink" title="下载cuda和cudnn"></a>下载cuda和cudnn</h5><p>在官网下载cuda10.0和cudnn7.3.1，两者的版本一定要匹配。<br>cuda和cudnn匹配列表：<br><a href="https://developer.nvidia.com/rdp/cudnn-archive#a-collapse731-10">https://developer.nvidia.com/rdp/cudnn-archive#a-collapse731-10</a></p>
<p>cuda10.0下载地址（选择runfile的文件下载，安装比较方便）：<br><a href="https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=runfilelocal">https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=runfilelocal</a></p>
<p>cudnn7.3.1下载地址：<br><a href="https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.3.1/prod/10.0_2018927/cudnn-10.0-linux-x64-v7.3.1.20">https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.3.1/prod/10.0_2018927/cudnn-10.0-linux-x64-v7.3.1.20</a></p>
<h5 id="安装cuda"><a href="#安装cuda" class="headerlink" title="安装cuda"></a>安装cuda</h5><p><strong>1. 禁用nouveau driver</strong></p>
<figure class="highlight 1c"><table><tr><td class="code"><pre><code class="hljs 1c">lsmod <span class="hljs-string">| grep nouveau</span><br></code></pre></td></tr></table></figure>
<p>如果没有结果输出，则代表禁用掉了。如果有内容输出，则代表没有禁用掉。需要执行如下操作，</p>
<p>1）在/etc/modprobe.d中创建文件blacklist-nouveau.conf,</p>
<figure class="highlight bash"><table><tr><td class="code"><pre><code class="hljs bash"><span class="hljs-built_in">cd</span> /etc/modprobe.d<br>sudo <span class="hljs-built_in">touch</span> blacklist-nouveau.conf<br></code></pre></td></tr></table></figure>
<p>2）在文件blacklist-nouveau.conf中输入如下内容，</p>
<figure class="highlight apache"><table><tr><td class="code"><pre><code class="hljs apache"><span class="hljs-attribute">blacklist</span> nouveau<br><span class="hljs-attribute">options</span> nouveau modeset=<span class="hljs-number">0</span><br></code></pre></td></tr></table></figure>
<p>保存并退出后，执行如下命令，</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">sudo update-initramfs -u</span><br></code></pre></td></tr></table></figure>
<p>3）执行命令<code>lsmod | grep nouveau</code> 检查是否禁用成功。</p>
<p><strong>2. 进入文本模式</strong><br>按Ctrl+Alt+F1，进入文本模式，执行如下命令，关闭图形界面，</p>
<figure class="highlight arduino"><table><tr><td class="code"><pre><code class="hljs arduino">sudo service lightdm stop<br></code></pre></td></tr></table></figure>
<p>然后进入cuda的安装包所在文件夹，执行如下命令：</p>
<figure class="highlight apache"><table><tr><td class="code"><pre><code class="hljs apache"><span class="hljs-attribute">sudo</span> sh cuda_10.<span class="hljs-number">0</span>.<span class="hljs-number">130</span>_410.<span class="hljs-number">48</span>_linux.run<br></code></pre></td></tr></table></figure>
<p>输入上述命令后，会显示用户许可证信息，按空格键直到显示为100%，然后输入accept。</p>
<p>在这个过程中，会遇到如下选项，NVIDIA Accelerated Graphics Driver: 选择n;<br>openGL（有时不会出现）: 选择n;<br>其他：都选择y；<br>安装路径可以选择默认：直接回车。</p>
<p>接着，输入命令<code>sudo service lightdm start</code>, 启动图形化界面，按住Alt+Ctrl+F7,返回图形化登录界面，输入账号密码后登录。 或者输入命令<code>sudo reboot</code>，重启回到图形界面。</p>
<p><strong>3. 环境配置</strong><br>输入如下命令，打开/etc/profile文件，</p>
<figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">vim <span class="hljs-regexp">/etc/</span>profile<br></code></pre></td></tr></table></figure>
<p>在文件中输入如下内容，</p>
<figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">export PATH=<span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda-10.0/</span>bin<br>export LD_LIBRARY_PATH=<span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda-10.0/</span>lib64<br></code></pre></td></tr></table></figure>
<p>然后，保存并退出文件。接着，执行如下命令，使配置生效，</p>
<figure class="highlight gradle"><table><tr><td class="code"><pre><code class="hljs gradle"><span class="hljs-keyword">source</span> <span class="hljs-regexp">/etc/</span>profile<br></code></pre></td></tr></table></figure>
<p><strong>4. 检查cuda是否安装成功</strong><br>输入如下命令，查看nvidia驱动的版本：</p>
<figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">cat <span class="hljs-regexp">/proc/</span>driver<span class="hljs-regexp">/nvidia/</span>version<br></code></pre></td></tr></table></figure>
<p>我的版本为410.48。<br>输入如下命令，查看CUDA Toolkit的版本：</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">nvcc -V</span><br></code></pre></td></tr></table></figure>
<p>我的版本是V10.0.130。<br>至此，cuda安装成功，接下来安装cudnn。</p>
<h5 id="安装cudnn"><a href="#安装cudnn" class="headerlink" title="安装cudnn"></a>安装cudnn</h5><p><strong>1. 解压cudnn安装包</strong></p>
<figure class="highlight apache"><table><tr><td class="code"><pre><code class="hljs apache"><span class="hljs-attribute">tar</span> -xvf cudnn-<span class="hljs-number">10</span>.<span class="hljs-number">0</span>-linux-x64-v<span class="hljs-number">7.3.1.20</span>.tgz<br></code></pre></td></tr></table></figure>
<p><strong>2. 配置cudnn</strong><br>在当前目录输入依次执行如下命令，</p>
<figure class="highlight awk"><table><tr><td class="code"><pre><code class="hljs awk">sudo cp cuda<span class="hljs-regexp">/include/</span>cudnn.h <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda/i</span>nclude/<br>sudo cp cuda<span class="hljs-regexp">/lib64/</span>libcudnn* <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda/</span>lib64/<br>sudo chmod a+r <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda/i</span>nclude/cudnn.h<br>sudo chmod a+r <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda/</span>lib64/libcudnn*<br></code></pre></td></tr></table></figure>
<p><strong>3. 查看cudnn版本</strong><br>输入如下命令，查看版本，</p>
<figure class="highlight gradle"><table><tr><td class="code"><pre><code class="hljs gradle">cat <span class="hljs-regexp">/usr/</span>local<span class="hljs-regexp">/cuda/i</span>nclude/cudnn.h | <span class="hljs-keyword">grep</span> CUDNN_MAJOR -A <span class="hljs-number">2</span><br></code></pre></td></tr></table></figure>
<p>如果有对应的CUDNN_MAJOR输出，则代表安装成功。</p>
<h5 id="检查cuda和cudnn是否都安装成功"><a href="#检查cuda和cudnn是否都安装成功" class="headerlink" title="检查cuda和cudnn是否都安装成功"></a>检查cuda和cudnn是否都安装成功</h5><p>输入如下命令，</p>
<figure class="highlight ebnf"><table><tr><td class="code"><pre><code class="hljs ebnf"><span class="hljs-attribute">modprobe nvidia</span><br><span class="hljs-attribute">nvidia-smi</span><br></code></pre></td></tr></table></figure>
<p>如果出现GPU的信息列表，则说明安装成功。</p>
<h5 id="常见错误"><a href="#常见错误" class="headerlink" title="常见错误"></a>常见错误</h5><ol>
<li>如果出现无法进入Ubuntu系统，输入如下命令，清除安装的驱动，<figure class="highlight routeros"><table><tr><td class="code"><pre><code class="hljs routeros">sudo apt-<span class="hljs-built_in">get</span> purge nvidia*<br></code></pre></td></tr></table></figure>
然后再输入命令<code>sudo reboot</code>重启，如果问题依然没有解决，按照上述步骤，重新安装cuda。</li>
</ol>
<h5 id="参考资料"><a href="#参考资料" class="headerlink" title="参考资料"></a>参考资料</h5><ol>
<li><a href="https://www.pythonf.cn/read/110763">https://www.pythonf.cn/read/110763</a></li>
<li><a href="https://www.jianshu.com/p/89f1ab962d75">https://www.jianshu.com/p/89f1ab962d75</a></li>
<li><a href="https://blog.csdn.net/weixin_42279044/article/details/83181686">https://blog.csdn.net/weixin_42279044/article/details/83181686</a></li>
<li><a href="https://blog.csdn.net/lihe4151021/article/details/90237681">https://blog.csdn.net/lihe4151021/article/details/90237681</a></li>
<li><a href="https://blog.csdn.net/qq_45049586/article/details/104582468">https://blog.csdn.net/qq_45049586/article/details/104582468</a></li>
<li><a href="https://blog.csdn.net/qq_41915226/article/details/103051602">https://blog.csdn.net/qq_41915226/article/details/103051602</a></li>
</ol>
]]></content>
<categories>
<category>经验技巧</category>
</categories>
<tags>
<tag>CUDA</tag>
<tag>GPU</tag>
</tags>
</entry>
<entry>
<title>【书山有路-10】好情绪养成手册</title>
<url>/posts/5bf6f313/</url>
<content><![CDATA[<p>我们一起来读<a href="https://book.douban.com/subject/36407267/">《好情绪养成手册》</a>这本书吧。</p>
<span id="more"></span>
<p><img src="/../images/hqxycsc.jpg" alt="好情绪养成手册"></p>
<p>好情绪如何养成？情绪、休息、运动、睡眠、饮食，共同构筑健康大厦。让我们一起走进这本书📖吧。</p>
<p>这是一本值得多次阅读的自我接纳，情绪疗愈手册，与《<a href="https://xiepeng21.cn/posts/70e570a9/">少有人走的路</a>》一书一样，作者的【英】索菲·莫特的写作态度很真诚，心理学知识和经验也很丰富，给出的建议，实操性很好，而且很用心地在写作，值得推荐。</p>
<p>情绪就像奔流不息的河流，偶尔波涛汹涌，有时细水长流，没法压抑，只能疏导，推荐这本书，爱自己多一点，睡得更香一些。</p>
<p>精彩片段：</p>
<ul>
<li><p>让树木加速成长的法宝不是主人对它指手画脚，而是主人对它的悉心照料。你的成长也是一样。————让·哈迪（Jenn Hardy）博士</p>
</li>
<li><p>我们内在的批评常常会贬低我们的价值，指出我们犯下的所有错误，但它从来不会为我们提供有用的建议，也不提供改进或提升的方法。</p>
</li>
</ul>
<p>欢迎在留言区，说说你对情绪的理解，阅读这本书给你带来的感触。也可以推荐一些，最近你看过的有意思，有收获的书籍、电影、音乐📚，我们一起成长！</p>
<hr>
<p>欢迎各位提出建议、问题，我们一起交流、学习、成长。</p>
<p>“问渠那得清如许？为有源头活水来” ヾ(◍°∇°◍)ﾉﾞ</p>
<p>——— 我在半亩方塘等你 ^_^</p>
]]></content>
<categories>
<category>随笔感悟</category>
</categories>
<tags>
<tag>书山有路</tag>
<tag>读书笔记</tag>
<tag>情绪</tag>
<tag>心理学</tag>
</tags>
</entry>
<entry>
<title>【书山有路-13】少有人走的路8：寻找石头</title>
<url>/posts/559bb2b0/</url>
<content><![CDATA[<p>我们一起来读<a href="https://book.douban.com/subject/35232776/">《少有人走的路8: 寻找石头》</a>这本书吧。这本书是《少有人走的路》系列的第8本，也是这个系列的收官之作，最后一本书。讲的主要内容是作者对于金钱、婚姻、子女、健康与死亡的终极思考，没有标准答案，这是一个无尽的探索之旅，每个人给出的答案都不太可能会是一样的。</p>
<span id="more"></span>
<p><img src="/../images/xzst.jpg" alt="少有人走的路8: 寻找石头"></p>
<p>这本书与<a href="https://xiepeng21.cn/posts/70e570a9/">《少有人走的路：心智成熟的旅程》</a>、<a href="https://xiepeng21.cn/posts/117e144d/">《少有人走的路2:勇敢地面对谎言》</a>、<a href="https://xiepeng21.cn/posts/ca5ef243/">《少有人走的路3：与心灵对话》</a>、<a href="https://xiepeng21.cn/posts/2cb15dfa/">《少有人走的路4:在焦虑的年代获得精神的成长》</a>、<a href="https://xiepeng21.cn/posts/7a6e1cf9/">《少有人走的路5：不一样的鼓声》</a>、<a href="https://xiepeng21.cn/posts/fd28cad8/">《少有人走的路6：真诚是生命的药》</a>与<a href="https://xiepeng21.cn/posts/76208d8e/">《少有人走的路7：靠窗的床》</a>共同形成了《少有人走的路》系列。</p>
<p>以下内容是书中打动我，给我留下深刻印象的一些摘录和感悟。也欢迎大家在留言区留下那些触动你的摘录以及心得感悟。</p>
<p>踏上生命的旅程，寻找石头，不仅仅是一颗颗新奇的小石头，还可以是一个个不至于让人滑倒的踏脚石。自己是一个爱好石头的人，寻找石头的过程，就是让自己的好奇心在大千世界延伸的一种方式。</p>
<p>石头无声，但它们本身，便已说尽世间万象。</p>
<p>卡尔·荣格把人类邪恶的根源归结为“拒绝面对阴影”，在这里，“阴影”被定义为我们自身的一部分，它包含那些我们不愿意具有的特质。我们不仅会企图对他人掩藏这部分自己，也对自己掩藏，我们不断试图将这部分的自己清扫至意识的地毯之下，如同藏起污物一样。而荣格所说的“拒绝”，指的是一种主动抵制的心理，远比我们平时被动抵制批评时的心理还要强烈。</p>
<p>虽然我对很多事情不再确信无疑，但正是这些不确定和混乱，让我变得更安之若素，甚至开始感到享受。</p>
<hr>
<p>即将走到年尾，也将迎来2025年。</p>
<p>过去的一年，看了一些书，记录一下，也希望新的一年，与书相伴，不断成长。</p>
<ol>
<li>《<a href="https://book.douban.com/subject/35196138/">穿越小径分岔的花园</a>》：太过严格地归属于任何学科，会限制个人的知识独立性。</li>
<li>《<a href="https://book.douban.com/subject/36579580/">每个人都有自己的职场优势</a>》：找到优势，不是成为更好的自己，而是更好地成为自己。</li>
<li>《<a href="https://book.douban.com/subject/36491854/">意识机器</a>》：机器会有意识吗？意识和智能有什么关系？这本书会带领你解开这些问题的面纱。</li>
<li>《<a href="https://book.douban.com/subject/26769136/">为什么长大？</a>》：这本书回答了一些孩子们会问而大多数大人认为已经解答了的问题。比如怎么找到意义？怎么创造自己的生活？</li>
<li>《<a href="https://book.douban.com/subject/34887257/">活法</a>》：命运与因果交织，秉承善良，以利他之心在世间做人做事。</li>
<li>《<a href="https://book.douban.com/subject/23780225/">能断</a>》：凡所有相，皆是虚妄。</li>
<li>《<a href="https://book.douban.com/subject/36407267/">好情绪养成手册</a>》：一本值得多次阅读的自我接纳，情绪疗愈手册。</li>
<li>《<a href="https://book.douban.com/subject/27084650/">人生很短，做一个有趣的人</a>》：这本书是我的床头读物，每晚看一点，不知不觉，就看完了。</li>
<li>《<a href="https://book.douban.com/subject/20424526/">邓小平时代</a>》：既是设计师，也是实干家。</li>
<li>《<a href="https://book.douban.com/subject/30175059/">李光耀观天下</a>》：有大眼光、大格局，搞建设、谋发展的实干家。</li>
<li>《<a href="https://book.douban.com/subject/35899532/">有解：高效解决问题的关键7步</a>》：从战略和战术层面讲如何解决问题的书，实操性很强。</li>
<li>《<a href="https://book.douban.com/subject/24257486/">局外人</a>》：当外在环境不适合正常人生存的时候，正常反倒变成了不正常。重视自己的感受和情绪，相信自己的判断，勇敢地活出自己，自己就是自己的代言人。</li>
<li>《<a href="https://book.douban.com/subject/36357804/">为什么伟大不能被计划</a>》：如果我们能明确实现目标的踏脚石（Stepping Stone），那么规划是明智的；而当面对未知的领域时，不设定目标的创新才是关键所在。</li>
<li>《<a href="https://book.douban.com/subject/30297006/">我是我认识的最聪明的人</a>》：第一次看生物物理学家的自传，伊瓦尔·贾埃弗是个有趣的人，他的人生也挺波澜起伏的，很有故事性。</li>
<li>《<a href="https://book.douban.com/subject/36336313/">深度关系</a>》：从『建立信任』到『彼此成就』，愿我们都能在深度关系中更加了解自己，与他人一起成长。</li>
</ol>
<hr>
<p>欢迎各位提出建议、问题，我们一起交流、学习、成长。</p>
<p>“问渠那得清如许？为有源头活水来” ヾ(◍°∇°◍)ﾉﾞ</p>
<p>——— 我在半亩方塘等你 ^_^</p>
]]></content>
<categories>
<category>随笔感悟</category>
</categories>
<tags>
<tag>书山有路</tag>
<tag>读书笔记</tag>
<tag>少有人走的路</tag>
</tags>
</entry>
<entry>
<title>【书山有路-15】思考的艺术</title>
<url>/posts/1ad8d428/</url>
<content><![CDATA[<p>我们一起来读<a href="https://book.douban.com/subject/20480879/">《思考的艺术》</a>这本书吧。这本书从<em>了解思考</em>，<em>创造力</em>，<em>批判性</em>以及<em>沟通想法</em>四个方面进行介绍。这是一本系统讲述思考方法的书籍。这本书将教会我们如何思考，从依赖其他人的思想中解放出来，并帮助我们形成自己的思想和明智的想法。</p>
<span id="more"></span>
<p><img src="/../images/skdys.jpg" alt="思考的艺术"></p>
<h2 id="思考与决策的重要性"><a href="#思考与决策的重要性" class="headerlink" title="思考与决策的重要性"></a>思考与决策的重要性</h2><p>如何分析、解决<strong>问题</strong>，做出更好的<strong>决策</strong>，与我们的<strong>思考</strong>紧密相关。</p>
<p>通过<strong>表达、阐述问题及观点</strong>，<strong>调查问题</strong>，<strong>产生思想</strong>这三个阶段，可以获得创新性的思考。</p>
<h2 id="高效能思考者的特点"><a href="#高效能思考者的特点" class="headerlink" title="高效能思考者的特点"></a>高效能思考者的特点</h2><p>高效能的思考者不会只<strong>评判</strong>他人的努力而自己不采取<strong>行动</strong>，他们解决问题、做出决定、在争议中选择立场。</p>
<p>哲学家<strong>叔本华</strong>说：每个人都把自己视野的极限当作世界的极限。</p>
<h2 id="解决问题的知识和能力"><a href="#解决问题的知识和能力" class="headerlink" title="解决问题的知识和能力"></a>解决问题的知识和能力</h2><p>成功地分析争议和解决问题需要<strong>事实性知识</strong>，即要熟悉问题产生的历史背景并且理解与问题有关的原则和概念。</p>
<p>如果你想要成为一位优秀的<strong>问题解决者</strong>，就不仅需要具备<strong>事实性知识</strong>，还需要具备<strong>缜密的思维</strong>。</p>
<h2 id="组织中的挑战与机遇"><a href="#组织中的挑战与机遇" class="headerlink" title="组织中的挑战与机遇"></a>组织中的挑战与机遇</h2><p>即便是运行良好的组织，也会经历财富的迅速变化，被迫<strong>精简业务</strong>和<strong>裁员</strong>。</p>
<p>迎接挑战、抓住全球经济带来的机遇需要<strong>创造性</strong>和<strong>评判性思考</strong>技巧。</p>
<h2 id="大脑功能与思考"><a href="#大脑功能与思考" class="headerlink" title="大脑功能与思考"></a>大脑功能与思考</h2><p>大脑<strong>右半球</strong>主要掌握非语言、符号化和直觉反应，而<strong>左半球</strong>则掌握语言的使用、逻辑推理、分析和执行连续性任务。</p>
<p>Roger W. Sperry（罗杰 W. 斯佩里）在他的诺贝尔奖获奖演说中提到：在正常情况下，大脑两个半球是紧密联合成<strong>一个整体</strong>进行工作的，而非一个大脑半球在工作而另一个大脑半球却处在空闲状态。</p>
<h2 id="创造性思维的优势"><a href="#创造性思维的优势" class="headerlink" title="创造性思维的优势"></a>创造性思维的优势</h2><p>好的思考者会比差的思考者产生更多的、更好的想法，他们在思考时能够熟练使用各种各样的创造技术，这使得他们能够更容易<strong>发现问题</strong>。他们能够测试自己的第一印象，做出重要的区分，并且在有<strong>证据</strong>的基础上得出结论，而不仅仅依靠自己的<strong>直觉</strong>。</p>
<h2 id="个性化需求与专注力"><a href="#个性化需求与专注力" class="headerlink" title="个性化需求与专注力"></a>个性化需求与专注力</h2><p>没有两个人的<strong>需求</strong>是完全相同的，对一个人有用的东西未必对另一个人有用。</p>
<p>保持全神贯注意味着当我们的思维游离时，我们能够将<strong>注意力</strong>转向我们的<strong>思考目的</strong>和<strong>问题</strong>。</p>
<hr>
<p>欢迎各位提出建议、问题，我们一起交流、学习、成长。</p>
<p>“问渠那得清如许？为有源头活水来” ヾ(◍°∇°◍)ﾉﾞ</p>
<p>——— 我在半亩方塘等你 ^_^</p>
]]></content>
<categories>
<category>随笔感悟</category>
</categories>
<tags>
<tag>书山有路</tag>
<tag>读书笔记</tag>
<tag>思考的艺术</tag>
</tags>
</entry>
<entry>
<title>【书山有路-11】少有人走的路6：真诚是生命的药</title>
<url>/posts/fd28cad8/</url>
<content><![CDATA[<p>我们一起来读<a href="https://book.douban.com/subject/30428396/">《少有人走的路6: 真诚是生命的药》</a>这本书吧。这本书是《少有人走的路》系列的第6本，讲的主要内容是真诚对人际关系的作用，涉及到家庭教育、婚姻关系、职业等多个方面，恰好分享一本我最近刚读完的一本<a href="https://book.douban.com/subject/36336313/">《深度关系》</a>，这本书给出了很多案例，介绍了不同的关系以及如何维护良好的关系，加深彼此了解，修复破损的关系，一本实操性很强的书，值得一读。</p>
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