diff --git a/index.html b/index.html
index 9ff83a6..86b0b45 100644
--- a/index.html
+++ b/index.html
@@ -148,11 +148,12 @@
     <ol>
         <li>Arbitraging on Decentralized Exchanges (with <a class="authorlink" href="https://sites.google.com/site/xuedonghepage/home"
                 target="_blank">Xuedong He</a> and <a class="authorlink" href="https://hk.linkedin.com/in/yutian-zhou-555870189" target="_blank">Yutian Zhou</a>).<br>
-            Working Paper. <span class="links">[<a class="paperlink" href="" onclick="toggleAbstract('abs_arbDEX');return false">Abstract</a>]</span><br>
+            Working Paper. <span class="links">[<a class="paperlink" href="" onclick="toggleAbstract('abs_arbDEX');return false">Abstract</a>|<a class="paperlink"
+                href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5347630" target="_blank">SSRN</a>]</span><br>
             <div class="fade-in" style="display:none" id="abs_arbDEX">
                 <hr>
-                Decentralized exchanges (DEXs) are alternative venues to centralized exchanges to trade
-                    cryptocurrencies (CEXs) and have become increasingly popular. An arbitrage opportunity arises when
+                Decentralized exchanges (DEXs) are alternative venues to centralized exchanges (CEXs) for trading
+                    cryptocurrencies and have become increasingly popular. An arbitrage opportunity arises when
                     the exchange rate of two cryptocurrencies in a DEX differs from that in a CEX. Arbitrageurs can then
                     trade on the DEX and CEX to make a profit. Trading on the DEX incurs a gas fee, which determines the
                     priority of the trade being executed. We study a gas-fee competition game between two arbitrageurs
@@ -322,16 +323,17 @@
                     effects of three key features of market microstructure --- market tightness, market depth, and
                     finite market resilience --- on the investor's decision. By employing a Bachelier process to model
                     the dynamic of the fundamental value of the asset and assuming CARA-type utility for the investor,
-                    we manage to obtain the investor's optimal dynamic trading strategy in closed form by solving the
+                    we obtain the investor's optimal dynamic trading strategy in closed form by solving the
                     resulting high-dimensional singular control problem. Furthermore, we extend the model to incorporate
                     return-predicting signals and utilize an asymptotic expansion approach to derive approximate optimal
                     trading strategies. The theoretical and numerical results emphasize the vital role of patience.
                     Specifically, rather than dispersing small trades continuously over time as advocated by the
                     existing literature, our findings suggest that investors should strategically time their trading
-                    activities to align with the aim portfolio in the presence of market resilience. To quantify this
+                    activities jointly based on market liquidity and market signal. To quantify this
                     timing decision, we introduce a patience index that enables investors to strike a balance among
                     various competing goals, including achieving currently optimal risk exposure, incorporating signals
-                    about future predictions, and minimizing trading costs, by leveraging market resilience.
+                    about future predictions, and minimizing trading costs, by leveraging market resilience. We also
+                    demonstrate how to implement our patient trading strategy using real-life market data.
             </div>
         </li><br>