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  <title><![CDATA[PhD Defense by Bounghun Bock]]></title>
  <body><![CDATA[<h2>Percolation Theory: The complement of the infinite cluster &amp; The acceptance profile of the invasion percolation</h2>

<p>Series:&nbsp;</p>

<p>Dissertation Defense</p>

<p>Thursday, May 2, 2019 - 1:30pm</p>

<p>1.5 hours (actually 80 minutes)</p>

<p>Location:&nbsp;</p>

<p>Skiles 006</p>

<p>Speaker:&nbsp;</p>

<p>Bounghun Bock</p>

<p>,&nbsp;&nbsp;</p>

<p>Georgia Tech</p>

<p>,&nbsp;&nbsp;</p>

<p><a href="mailto:">bbock3@gatech.edu</a></p>

<p>Organizer:&nbsp;</p>

<p><a href="https://math.gatech.edu/people/bounghun-bock">Bounghun Bock</a></p>

<p>In independent bond percolation &nbsp;with parameter p, if one removes the vertices of the infinite cluster (and incident edges), for which values of p&nbsp;does the remaining graph contain an infinite cluster? Grimmett-Holroyd-Kozma used the triangle condition to show that for d &gt; 18, the set of such p&nbsp;contains values strictly larger than the percolation threshold pc. With the work of Fitzner-van der Hofstad, this has been reduced to d &gt; 10. We reprove this result by showing that for d &gt; 10&nbsp;and some p&gt;pc, there are infinite paths consisting of &quot;shielded&quot;&#39; vertices --- vertices all whose adjacent edges are closed --- which must be in the complement of the infinite cluster. Using numerical values of pc, this bound can be reduced to d &gt; 7. Our methods are elementary and do not require the triangle condition.</p>

<p>Invasion percolation is a stochastic growth model that follows a greedy algorithm. After assigning i.i.d. uniform random variables (weights) to all edges of d-dimensional space, the growth starts at the origin. At each step, we adjoin to the current cluster the edge of minimal weight from its boundary. In &#39;85, Chayes-Chayes-Newman studied the &quot;acceptance profile&quot;&#39; of the invasion: for a given p in [0,1], it is the ratio of the expected number of invaded edges until time n&nbsp;with weight in [p,p+dp]&nbsp;to the expected number of observed edges (those in the cluster or its boundary) with weight in the same interval. They showed that in all dimensions, the acceptance profile an(p)&nbsp;converges to one for p&lt;pc&nbsp;and to zero for p&gt;pc. In this paper, we consider an(p)&nbsp;at the critical point p=pc&nbsp;in two dimensions and show that it is bounded away from zero and one as n goes to infinity.</p>
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