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@@ -173,19 +173,19 @@ <h2 class="title is-3">Abstract</h2>
 <section class="section hero is-small">
   <div class="container is-max-desktop">
     <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths"><!-- Your image here -->
+      <div class="column is-four-fifths">
         <div class="content has-text-justified">
         <!-- Your image here -->
         <img src="static/images/cover.png" alt="Function Encoder training diagram."/>
-        <h2 class="subtitle">
+        <p>
           Our algorithm consists of an offline learning phase and an online execution phase.
           During the offline phase, we learn a set of basis functions that span the space of dynamics.
           This space of dynamics can arise due to unknown system parameters, such as a quadrotor with an unknown mass.
           The basis functions are learned via the function encoder algorithm, and each basis function is a neural ODE.
           During the online phase, we use the learned basis functions and a small online dataset to identify the dynamics of the system.
           This online adaptation requires zero gradient updates, and can be computed in real-time.
           Then, this model can be used for downstream tasks, such as MPC.
-        </h2>
+        </p>
         </div>
       </div>
     </div>
@@ -197,20 +197,19 @@ <h2 class="subtitle">
 
 
 <!-- MuJoCo image -->
-<section class="hero is-small is-light">
-  <div class="hero-body">
-    <div class="container">
-      <!-- Paper video. -->
-      <h2 class="title is-3">Accurate Long Horizon Predictions</h2>
-      <div class="columns is-centered has-text-centered">
-        <div class="column is-four-fifths">
+<section class="section hero is-small is-light">
+  <div class="container is-max-desktop">
+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <div class="content has-text-justified">
+          <h2 class="title is-3">Accurate Long Horizon Predictions</h2>
 
           <img src="static/images/mujoco_no_background.png" alt="MuJoCo Image" width="100%">
-          <h2 class="subtitle has-text-centered">
             We demonstrate that our learned models can accurately predict the dynamics on the MuJoCo HalfCheetah and Ant environments.
             We vary numerous environment parameters, such as lengths of the limbs and the control authority.
             Then, given a small online dataset, we attempt to predict the dynamics k steps into the future.
-            Our results indicate that we achieve both the accurate long horizon predictions of neural ODEs and the online adaptability of function encoders.
+            Our results indicate that we achieve both the accurate long horizon predictions of neural ODEs and the online adaptability of
+            function encoders.
         </div>
       </div>
     </div>
@@ -219,26 +218,20 @@ <h2 class="subtitle has-text-centered">
 <!-- End MuJoCO video -->
 
 <!-- Drone image -->
-<section class="hero is-small">
-  <div class="hero-body">
-    <div class="container">
-      <!-- Paper video. -->
-      <h2 class="title is-3">Online, Adaptive MPC</h2>
-      <div class="columns is-centered has-text-centered">
-        <div class="column is-four-fifths">
+<section class="section hero is-small">
+  <div class="container is-max-desktop">
+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <div class="content has-text-justified">
           <img src="static/images/drone_quant.png" alt="Drone Image" width="100%">
-          <h2 class="subtitle has-text-centered">
             We apply this algorithm to the MPC control of a quadrotor. The quadrotor's mass is varied every episode, and the goal is to reach prespecified coordinates.
             Our results demonstrate that our approach makes more accurate long horizon predictions in the presence
             of hidden parameters than neural ODEs alone. When measuring the slew rate of the controller, i.e. its efficiency, we observe that this improved prediction
             accuracy leads to more efficient control of the quadrotor.
-          </h2>
           <img src="static/images/drone_qual.png" alt="An example trajectory" width="100%">
-          <h2 class="subtitle has-text-centered">
             Qualitatively, this difference is visible in the trajectories of the respective controllers. Here we are plotting one trajectory from each controller
             for the same conditions. We observe that the neural ODE controller alone makes repeated corrections to its trajectory. In contrast, our approach more smoothly
             approaches the target coordinates.
-          </h2>
         </div>
       </div>
     </div>