diff --git a/index.html b/index.html
index 35aa1ec..8c6e7bb 100644
--- a/index.html
+++ b/index.html
@@ -47,7 +47,7 @@
           <tr style="padding:0px">
             <td style="padding:2.5%;width:63%;vertical-align:middle">
               <p style="text-align:center">
-                <name>Yue Liu</name>
+                <name>Xiao He</name>
               </p>
 
               <p style="text-align:justify">Xiao He (何潇) is a master student at degree China University of Geosciences, </a>.
@@ -61,7 +61,6 @@
 <!--                 <a href="https://scholar.google.com/citations?user=5tfpu3MAAAAJ&hl=zh-CN/">Google Scholar</a> &nbsp/&nbsp -->
 <!--                 <a href="https://twitter.com/lyq_q/">Twitter</a> &nbsp/&nbsp -->
                 <a href="https://github.com/hexiao0275/">Github</a> &nbsp/&nbsp
-<!--                 <a href="images/GF.png">GF</a> -->
               </p>
             </td>
             <td style="padding:2.5%;width:40%;max-width:40%">
@@ -75,7 +74,8 @@
               <heading>News</heading>
               <ul class="projects-box" id="projects-box">
 	      <li><b>[<font color="red">2024.07</font>]</b> One paper have been accepted by <em>ACM MM</em> 2024.</li>
-              <li><b>[<font color="red">2022.12</font>]</b> I won China National Scholarship for graduate students.</li>
+        <li><b>[<font color="red">2024.06</font>]</b> One paper have been accepted by <em>IEEE JBHI</em> 2024.</li>
+              <li><b>[<font color="red">2023.10</font>]</b> I won China National Scholarship for graduate students.</li>
 		<li><b>[<font color="red">2023.07</font>]</b> Two papers have been accepted by <em>ACM MM</em> 2024 (Oral).</li>
 	      <li><b>[<font color="red">2023.07</font>]</b> One paper has been accepted by <em>IEEE T-GRS</em>.</li>
                   <p class="projects-show" id="projects-show"><span class="projects-show-text" id="projects-show-text">More</span></p>
@@ -102,402 +102,108 @@
 
 <!--        paper_list-->
         <table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
-				                <!--------------------------------------------------------------------------------------------------------------------------->
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/ELCRec.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2401.05975.pdf">
-                <papertitle>End-to-end Learnable Clustering for Intent Learning in Recommendation</papertitle>
-              </a>
-              <br>
-                <strong>Yue Liu</strong>,
-                Shihao Zhu,
-                <a href="https://junxia97.github.io/">J. Xia</a>,
-                Y. Ma,
-                J. Ma,
-                Wenliang Zhong,
-                G. Zhang,
-                K. Zhang,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>
-              <br>
-              <em>arXiv</em>, 2024
-              <br>
-              <a href="https://arxiv.org/pdf/2401.05975.pdf">Paper</a>
-              /
-              <a href="">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-<!--                  We propose an intent learning method named ELCRec via the end-to-end learnable clustering and cluster-assisted contrastive learning for recommendation. Both the results on open benchmarks and industrial engine demonstrate the superiority and effectiveness.-->
-                  We propose an intent learning method termed ELCRec, which leverages end-to-end learnable clustering and cluster-assisted contrastive learning to improve recommendation. Both the results on open benchmarks and industrial engine demonstrate the superiority.
-<!--                Our method exhibits superior performance and effectiveness, as demonstrated by experiments on open benchmarks and an industrial engine.-->
-
-              </p>
-            </td>
-          </tr>
-<!--------------------------------------------------------------------------------------------------------------------------->
-                <!--------------------------------------------------------------------------------------------------------------------------->
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/TGC.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2305.10738">
-                <papertitle>Deep Temporal Graph Clustering</papertitle>
-              </a>
-              <br>
-                <a href="https://mgithubl.github.io/">Meng Liu</a>,
-                <strong>Yue Liu</strong>,
-                K. Liang,
-                S. Wang,
-                S. Zhou,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>
-
-              <br>
-              <em>ICLR</em>, 2024
-              <br>
-              <a href="https://arxiv.org/pdf/2305.10738">Paper</a>
-              /
-              <a href="https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                  We aim to extend deep graph clustering to temporal graphs, which are more practical in real-world scenarios. We propose a general framework TGC by clustering distribution assignment and adjacency reconstruction.
-
-              </p>
-            </td>
-          </tr>
 <!--------------------------------------------------------------------------------------------------------------------------->
 
-        <!--------------------------------------------------------------------------------------------------------------------------->
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/code_LLMs.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/abs/2309.16298">
-                <papertitle>At Which Training Stage Does Code Data Help LLM Reasoning?</papertitle>
-              </a>
-              <br>
-                <a href="https://yingweima2022.github.io/">Yingwei Ma</a>*,
-                <strong>Yue Liu</strong>*,
-                Y. Yu,
-                Y. Jiang,
-                C. Wang,
-                S. Li
-              <br>
-              <em>ICLR (<font color="red">Spotlight</font>)</em>, 2024
-              <br>
-              <a href="https://arxiv.org/abs/2309.16298">Paper</a>
-              /
-              <a href="https://github.com/yingweima2022/CodeLLM">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                  We explore at which training stage can code data help LLMs reasoning. The extensive experiments and insights deepen the understanding of LLMs' reasoning capability and the corresponding applications, e.g., scientific question answering, legal support, etc.
-
-
-
-<!--                  We propose a plug-and-play knowledge graph contrastive learning method named KGE-SymCL by mining the symmetrical structure information in knowledge graphs.-->
-              </p>
-            </td>
-          </tr>
+<tr>
+  <td style="padding:20px;width:25%;vertical-align:middle">
+    <div>
+      <img src='images/stgcl.png' width="160">
+    </div>
+  </td>
+  <td style="padding:20px;width:75%;vertical-align:middle">
+  <a href="https://github.com/hexiao0275/">
+    <papertitle>Heterogeneous Graph Guided Contrastive Learning for Spatially Resolved Transcriptomics Data</papertitle>
+  </a>
+  <br>
+  <strong>Xiao He</strong>,
+  Chang Tang,
+  Xinwang Liu,
+  Chuankun Li,
+  Shan An,
+  Zhenglai Li
+  <br>
+  <em>ACM MM (<font color="red">Oral</font>)</em>, 2023
+  <br>
+  <a href="https://github.com/hexiao0275/">Paper</a>
+  /
+  <a href="https://github.com/hexiao0275/">Code</a>
+  <p></p>
+  <p style="text-align:justify">
+    We propose stGCL.
+  </p>
+</td>
+</tr>
 
-                        <!--------------------------------------------------------------------------------------------------------------------------->
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/RGC.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2308.06827">
-                <papertitle>Reinforcement Graph Clustering with Unknown Cluster Number</papertitle>
-              </a>
-              <br>
-                <strong>Yue Liu</strong>,
-                <a href="https://liangke23.github.io/">Ke Liang</a>,
-                <a href="https://junxia97.github.io/">Jun Xia</a>,
-                <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-                <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-                <a href="https://mgithubl.github.io/">Meng Liu</a>,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>,
-                <a href="https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl=zh-CN&oi=ao">Stan Z. Li</a>
-              <br>
-              <em>ACM MM</em>, 2023
-              <br>
-              <a href="https://arxiv.org/pdf/2308.06827">Paper</a>
-              /
-              <a href="https://github.com/yueliu1999/RGC">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                  We propose RGC by determining the cluster number in deep graph clustering methods via the reinforcement learning.
-              </p>
-            </td>
-          </tr>
-<!--------------------------------------------------------------------------------------------------------------------------->
 
-<!--------------------------------------------------------------------------------------------------------------------------->
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/liu_KGE-SymCL.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2211.10738.pdf">
-                <papertitle>Knowledge Graph Contrastive Learning based on Relation-Symmetrical Structure</papertitle>
-              </a>
-              <br>
-                <a href="https://liangke23.github.io/">Ke Liang</a>*,
-                <strong>Yue Liu</strong>*,
-                <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-                <a href="https://wxtu.github.io/">W. Tu</a>,
-                Y. Wen,
-                <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-                X. Dong,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>
-              <br>
-              <em>IEEE T-KDE (<font color="red">ESI Highly Cited Paper</font>)</em>, 2023
-              <br>
-              <a href="https://arxiv.org/pdf/2211.10738.pdf">Paper</a>
-              /
-              <a href="">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                  We propose a plug-and-play knowledge graph contrastive learning method named KGE-SymCL by mining the symmetrical structure information in knowledge graphs.
-              </p>
-            </td>
-          </tr>
-<!--------------------------------------------------------------------------------------------------------------------------->
 
-            <tr>
-<!-- 		     bgcolor="#ffffd0" -->
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/liu_Dink-Net.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2305.18405.pdf">
-                <papertitle>Dink-Net: Neural Clustering on Large Graphs</papertitle>
-              </a>
-              <br>
-                <strong>Yue Liu</strong>,
-                <a href="https://liangke23.github.io/">K. Liang</a>,
-                <a href="https://junxia97.github.io/">Jun Xia</a>,
-                <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-                <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>,
-                <a href="https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl=zh-CN&oi=ao">Stan Z. Li</a>
-              <br>
-              <em>ICML</em>, 2023
-              <br>
-              <a href="https://arxiv.org/pdf/2305.18405.pdf">Paper</a>
-              /
-              <a href="">Project Page</a>
-                /
-              <a href="https://github.com/yueliu1999/Dink-Net">Code</a>
 
-              <p></p>
-              <p style="text-align:justify">
-                  We analyze drawbacks of the exising deep graph clustering methods and scale deep graph clustering to large-scale graphs. The proposed shrink and dilation loss functions optimize clustering distribution adversarially, allowing batch training without performance dropping.
-              </p>
-            </td>
-          </tr>
 <!--------------------------------------------------------------------------------------------------------------------------->
 
-            <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/liu_SCGC.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="">
-                <papertitle>Simple Contrastive Graph Clustering</papertitle>
-              </a>
-              <br>
-                <strong>Yue Liu</strong>,
-                <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-                <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-                <a href="https://xinwangliu.github.io/">Xinwang Liu</a>,
-                <a href="https://wangsiwei2010.github.io/">S. Wang</a>,
-                <a href="https://liangke23.github.io/">K. Liang</a>,
-                <a href="https://wxtu.github.io/">W. Tu</a>,
-                <a href="https://liliangnudt.github.io/">L. Li</a>,
-
-              <br>
-              <em>IEEE T-NNLS</em>, 2023
-              <br>
-              <a href="">Paper</a>
-              /
-              <a href="https://github.com/yueliu1999/SCGC">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                  We propose to replace the complicated and consuming graph data augmentations by designing the parameter un-shared siamese encoders and perurbing the node embeddings.
-
-              </p>
-            </td>
-          </tr>
-
-<!--------------------------------------------------------------------------------------------------------------------------->
 
             <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/mole-bert.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://openreview.net/forum?id=jevY-DtiZTR">
-                <papertitle>Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules</papertitle>
-              </a>
-              <br>
-                <a href="https://junxia97.github.io/">Jun Xia</a>,
-                C. Zhao,
-                B. Hu,
-                Z. Gao,
-                C. Tan,
-                <strong>Yue Liu</strong>,
-                S. Li,
-                <a href="https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl=zh-CN&oi=ao">Stan Z. Li</a>
-              <br>
-              <em>ICLR</em>, 2023
-              <br>
-              <a href="https://openreview.net/forum?id=jevY-DtiZTR">Paper</a>
-              /
-              <a href="https://github.com/junxia97/Mole-BERT">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                The negative transfer in molecular graph pre-training are analyzed. To alleviate this issue, we first enlarge the atom vocabulary size and then develop two novel pre-training strategies at node and graph level.
-              </p>
-            </td>
-          </tr>
-
-<!--------------------------------------------------------------------------------------------------------------------------->
-            <tr >
-<!-- 		    bgcolor="#ffffd0" -->
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/liu_HSAN.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/abs/2212.08665">
-                <papertitle>Hard Sample Aware Network for Contrastive Deep Graph Clustering</papertitle>
-              </a>
-              <br>
-              <strong>Yue Liu</strong>,
-              <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-              <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-              <a href="https://xinwangliu.github.io/">X. Liu</a>,
-              Z. Wang,
-              <a href="https://liangke23.github.io/">K. Liang</a>,
-              <a href="https://wxtu.github.io/">W. Tu</a>,
-              L. Li,
-              J. Duan,
-              C. Chen
-              <br>
-              <em>AAAI (<font color="red">Oral & Most Influential AAAI Paper</font>)</em> (13/539) [<a href="https://www.paperdigest.org/2023/09/most-influential-aaai-papers-2023-09/">Link</a>], 2023 
-              <br>
-              <a href="https://arxiv.org/abs/2212.08665">Paper</a>
-              /
-              <a href="https://github.com/yueliu1999/HSAN">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-                We propose Hard Sample Aware Network (HSAN) to mine both the hard positive samples and hard negative samples with a comprehensive similarity measure criterion and a general dynamic sample weighing strategy.
-              </p>
-            </td>
-          </tr>
-
-
-
+                <td style="padding:20px;width:25%;vertical-align:middle">
+                  <div>
+                    <img src='images/CALNet.png' width="160">
+                  </div>
+                </td>
+                <td style="padding:20px;width:75%;vertical-align:middle">
+                <a href="https://dl.acm.org/doi/abs/10.1145/3581783.3612651">
+                  <papertitle>Multispectral Object Detection via Cross-Modal Conflict-Aware Learning</papertitle>
+                </a>
+                <br>
+                <strong>Xiao He</strong>,
+                Chang Tang,
+                Xin Zou,
+                Wei Zhang
+                <br>
+                <em>ACM MM (<font color="red">Oral</font>)</em>, 2023
+                <br>
+                <a href="https://dl.acm.org/doi/abs/10.1145/3581783.3612651">Paper</a>
+                /
+                <a href="https://github.com/hexiao0275/CALNet-Dronevehicle">Code</a>
+                <p></p>
+                <p style="text-align:justify">
+                  We propose CALNet.
+                </p>
+              </td>
+            </tr>
+                        
 
 <!--------------------------------------------------------------------------------------------------------------------------->
 
             <tr>
-<!--              -->
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/ADGC_survey.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://arxiv.org/pdf/2211.12875">
-                <papertitle>A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application</papertitle>
-              </a>
-              <br>
-              <strong>Yue Liu</strong>,
-              <a href="https://junxia97.github.io/">J. Xia</a>,
-              <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao/">S. Zhou</a>,
-              <a href="https://wangsiwei2010.github.io/">S. Wang</a>,
-              <a href="https://scholar.google.com/citations?user=3S9i5TkAAAAJ&hl=zh-CN&oi=ao">X. Guo</a>,
-              <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-              <a href="https://liangke23.github.io/">K. Liang</a>,
-              <a href="https://wxtu.github.io/">W. Tu</a>,
-              <a href="https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl=zh-CN&oi=ao">Stan Z. Li</a>,
-              <a href="https://xinwangliu.github.io/">X. Liu</a>
-              <br>
-              <em>arXiv</em>, 2022
-              <br>
-              <a href="https://arxiv.org/pdf/2211.12875">Paper</a>
-              /
-              <a href="https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering">Project Page</a>
-              <p></p>
-              <p style="text-align:justify">
-                Deep graph clustering, which aims to group the nodes in graph into disjoint clusters, has become a new hot research spot. This paper summarizes the taxonomy, challenge, and application of deep graph clustering. We hope this work will serve as a quick guide and help researchers to overcome the challenges in this field.
-              </p>
-            </td>
-          </tr>
+                <td style="padding:20px;width:25%;vertical-align:middle">
+                  <div>
+                    <img src='images/s2aDet.png' width="160">
+                  </div>
+                </td>
+                <td style="padding:20px;width:75%;vertical-align:middle">
+                <a href="https://arxiv.org/pdf/2306.08370">
+                  <papertitle>Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation</papertitle>
+                </a>
+                <br>
+                <strong>Xiao He</strong>,
+                Chang Tang,
+                Xinwang Liu,
+                Wei Zhang,
+                Kun Sun,
+                Jiangfeng Xu
+                <br>
+                <em>IEEE T-GRS </em>, 2024
+                <br>
+                <a href="https://arxiv.org/pdf/2306.08370">Paper</a>
+                /
+                <a href="https://github.com/hexiao0275/S2ADet">Code</a>
+                <p></p>
+                <p style="text-align:justify">
+                  We propose S2ADet and a News Dataset HOD3K.
+                </p>
+              </td>
+            </tr>
 
 
+<!--------------------------------------------------------------------------------------------------------------------------->
 
-          <tr >
-            <td style="padding:20px;width:25%;vertical-align:middle">
-              <div class="one">
-                <img src='images/DCRN.png' width="160">
-              </div>
-            </td>
-            <td style="padding:20px;width:75%;vertical-align:middle">
-              <a href="https://www.aaai.org/AAAI22Papers/AAAI-5928.LiuY.pdf">
-                <papertitle>Deep Graph Clustering via Dual Correlation Reduction</papertitle>
-              </a>
-              <br>
-              <strong>Yue Liu</strong>*,
-<!--              Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu-->
-              <a href="https://wxtu.github.io/">Wenxuan Tu</a>*,
-              <a href="https://scholar.google.com/citations?user=p9Se8kYAAAAJ&hl=zh-CN&oi=ao">S. Zhou</a>,
-              <a href="https://xinwangliu.github.io/">X. Liu</a>,
-              L. Song,
-              <a href="https://xihongyang1999.github.io/">X. Yang</a>,
-              E. Zhu
-              <br>
-              <em>AAAI</em>, 2022
-              <br>
-              <a href="https://www.aaai.org/AAAI22Papers/AAAI-5928.LiuY.pdf">Paper</a>
-              /
-              <a href="https://github.com/yueliu1999/DCRN">Code</a>
-              <p></p>
-              <p style="text-align:justify">
-              We propose a self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) to address the representation collapse issue by reducing information correlation in both sample and feature levels.
-              </p>
-            </td>
-          </tr>
 <!--------------------------------------------------------------------------------------------------------------------------->
 
 <!--        paper_list-->
@@ -512,13 +218,8 @@
 
         <table width="100%" align="center" border="0" cellpadding="20"><tbody>
             <ul>
-            <li>Visiting Student @ <a href="https://en.westlake.edu.cn/">Westlake University</a>, working with <a href="https://junxia97.github.io/">Jun Xia</a>, supervised by Prof. <a href="https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl=zh-CN&oi=ao">Stan Z. Li</a></li></li>
-            <li>Recommendation Algorithm Engineer Internship @ <a href="https://www.antgroup.com/en">Ant Group Co., Ltd.</a></li></li>
-            <li>Financial Risk Control Algorithm Engineer Internship @ <a href="https://www.meituan.com/">Meituan Co., Ltd.</a></li></li>
-            <li>Master Student @ <a href="https://english.nudt.edu.cn/">National University of Defence Technology</a>, supervised by Prof. <a href="https://xinwangliu.github.io/">Xinwang Liu</a></li>
-            <li>3D Vision Algorithm Engineer Internship @ <a href="https://speedbot.ca/">SpeedBot Robotics Co., Ltd.</a>, supervised by Prof. <a href="https://kevinkaixu.net/">Kai Xu</a></li>
-            <li>Bachelor of Engineering @ <a href="http://english.neu.edu.cn/">Northeastern University at Qinhuangdao</a>, supervised by Prof. Miao Fang</li>
-            </ul>
+            <li>Student Intern at Shanghai Artificial Intelligence Laboratory, 2024 </li>
+        </ul>
         </table>
 
 
@@ -532,15 +233,11 @@
 
         <table width="100%" align="center" border="0" cellpadding="20"><tbody>
             <ul>
-            <li>Reviewer for ICML'24, ICLR'24, NeurIPS'23/24, AAAI'23/24/25</li>
-            <li>Reviewer for CVPR'24</li>
-            <li>Reviewer for EMNLP'23</li>
-            <li>Reviewer for KDD'24/25, WWW'24, CIKM'23/24, WSDM'23, IEEE T-KDE</li>
-	    <li>Reviewer for ACM MM'23/24, IEEE T-MM</li>
-            <li>Reviewer for PRCV'22/23, IEEE/CAA JAS, IEEE T-NNLS, Pattern Recognition</li>
-<!--             <li>Reviewer for PRCV 2022</li> -->
-<!--             <li>Reviewer for NeurIPS 2023</li> -->
-<!--             <li>Reviewer for ACM MM 2023</li> -->
+            <li>Reviewer for ACMMM'23/24</li>
+            <!-- <li>Reviewer for CVPR'24</li> -->
+	    <li>Reviewer for IEEE TIP, IEEE TCYB, IEEE TMM</li>
+            <li>Reviewer for Information Fusion, IEEE T-NNLS, Pattern Recognition</li>
+            <li>Reviewer for IEEE TCSVT, IEEE TGRS</li>
             </ul>
         </table>
 
@@ -554,16 +251,8 @@
         </tbody></table>
         <table width="100%" align="center" border="0" cellpadding="20"><tbody>
             <ul>
-            <li>Excellent Graduated Graduate Student of Hunan Province. [<a href="">PDF</a>]</li>
-            <li>China National Scholarship for Graduate Student. [<a href="images/National Scholarship 2022.pdf">PDF</a>]</li>
-            <li>Scholarship, NUDT. [<a href="images/Scholarship-NUDT.pdf">PDF</a>]</li>
-            <li>Excellent Graduated Graduate Student of Hebei Province. [<a href="images/Outstanding graduate.pdf">PDF</a>]</li>
-            <li>Recommendation for admission to NUDT. [<a href="images/Recommendation.pdf">PDF</a>]</li>
-            <li>China National Scholarship for Undergraduate Student. [<a href="images/National Scholarship 2020.pdf">PDF</a>]</li>
-            <li>Meritorious Winner, Interdisciplinary Contest in Modeling (ICM). [<a href="images/ICM.pdf">PDF</a>]</li>
-            <li>First Prize, Hebei Mathematics Competition for College Students (HMC). [<a href="images/HMC.pdf">PDF</a>]</li>
-            <li>Scholarship, NEUQ. [<a href="images/Scholarship.pdf">PDF</a>]</li>
-            </ul>
+            <li>China National Scholarship for Graduate Student.</li>
+         </ul>
         </table>