diff --git a/Gemfile.lock b/Gemfile.lock
old mode 100755
new mode 100644
index 085f5d2..f2a4fe3
--- a/Gemfile.lock
+++ b/Gemfile.lock
@@ -1,8 +1,8 @@
 GEM
   remote: https://rubygems.org/
   specs:
-    addressable (2.7.0)
-      public_suffix (>= 2.0.2, < 5.0)
+    addressable (2.8.1)
+      public_suffix (>= 2.0.2, < 6.0)
     bibtex-ruby (5.1.4)
       latex-decode (~> 0.0)
     citeproc (1.0.10)
@@ -62,7 +62,7 @@ GEM
     namae (1.0.1)
     pathutil (0.16.2)
       forwardable-extended (~> 2.6)
-    public_suffix (4.0.5)
+    public_suffix (5.0.0)
     rb-fsevent (0.10.4)
     rb-inotify (0.10.1)
       ffi (~> 1.0)
diff --git a/assets/sgf_cv.pdf b/assets/sgf_cv.pdf
index cd2687f..100d2db 100644
Binary files a/assets/sgf_cv.pdf and b/assets/sgf_cv.pdf differ
diff --git a/index.html b/index.html
index 85139b8..77e17c8 100644
--- a/index.html
+++ b/index.html
@@ -83,7 +83,7 @@
            <br><br>
            In addition to my research, I am involved in the Hydrocephalus community by way of <a href="http://teamhydro.org">Team Hydro</a>, a non-profit organization that my family and I started to raise money and awareness for the condition in honor of my sister, <a href="http://teamhydro.org/kate-finlayson/">Kate</a>. I also do consulting work as a data scientist and deep learning engineer, with clients including tech, biotech, and pharmaceutical companies.
            <br><br>
-           For a more formal account of my academic work, see my <a href=../assets/sgf_cv.pdf"> Curriculum Vitae</a> or <a href="https://scholar.google.com/citations?user=7__yMlEAAAAJ&hl=en">Google Scholar</a>.
+           For a more formal account of my academic work, see my <a href="../assets/sgf_cv.pdf"> Curriculum Vitae</a> or <a href="https://scholar.google.com/citations?user=7__yMlEAAAAJ&hl=en">Google Scholar</a>.
         </p>
     </div><!-- table container -->
    
@@ -140,7 +140,7 @@ <h3>Subgraph Neural Networks</h3>
               <i>In Submission, 2020<br>
               <i>*Co-first, listed alphabetically<br>
               [<a class="showSingle", target="27">Abstract</a>]
-              [<a href="">Paper</a>]
+              [<a href="https://arxiv.org/abs/2006.10538">Paper</a>]
             </p>
             <div id="div27" class="abstract">
               Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications. Further, subgraph prediction tasks present several unique challenges: subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist.