Updates from Overleaf

infeasibilitygliding.tex

@@ -18,7 +18,7 @@ \section{Exploiting Compositional Graph Representation} \label{infglide:sec:expl
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.7\textwidth]{infeasibilitygliding/InfeasibilityGliding_Full.png}
+    \includegraphics[width=0.7\textwidth]{infeasibilitygliding/InfeasibilityGliding_Full.jpeg}
     \caption{Feasibility map over compositional tetrahedron (3-simplex) formed by all combinations of Ti50 Zr50, Hf95 Ti5, Mo33 Nb33 Ta33, Mo80 Nb10 W10 discretized at 12 divisions per dimension. The positions in the 7-component elemental space obtained from \texttt{nimplex}, described in Chapter \ref{chap:nimplex}, were used to run \texttt{pycalphad} \cite{Otis2017Pycalphad:Python} evaluations and constrained by limiting phases present at equilibrium at 1000K to single or many solid solution phases. Roughly half of the compositions are infeasible, with most of them forming a single large region.}
     \label{infeasibilitygliding:fig:fullcomputation}
 \end{figure}
@@ -45,7 +45,7 @@ \subsection{Unbiased Exploration Searches} \label{infglide:ssec:unbiasedexplore}
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.7\textwidth]{infeasibilitygliding/InfeasibilityGliding_Glide.png}
+    \includegraphics[width=0.7\textwidth]{infeasibilitygliding/InfeasibilityGliding_Glide.jpeg}
     \caption{The same problem as in Figure \ref{infeasibilitygliding:fig:fullcomputation} solved by iteratively exploring all feasible paths in the compositional graph in a depth-first approach, which can be started from one or multiple points, and terminated once the goal is reached or once all of the feasible space is explored.}
     \label{infeasibilitygliding:fig:glide}
 \end{figure}

nimplex.tex

@@ -53,7 +53,7 @@ \subsection{Path Planning in Functionally Graded Materials} \label{nimplex:ssec:
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.38\textwidth]{nimplex/Spaces.png}
+    \includegraphics[width=0.38\textwidth]{nimplex/Spaces.jpeg}
     \caption{Three available compositions existing in a quaternary (d=4) compositional space forming a ternary (d=3) compositional space which can be attained with them; sampled with a uniform grid with 24 divisions. The hexagonal tiling emerges based on the distance metric in 2-simplex and would become rhombic dodecahedral in 3-simplex.} 
     \label{nimplex:fig:fgmspaces}
 \end{figure}
@@ -390,7 +390,7 @@ \subsection{Simplex Graph Complexes} \label{nimplex:ssec:complexes}
     \end{adjustbox}
     \hspace{6pt}
     \begin{adjustbox}{valign=c}
-        \includegraphics[width=0.5\textwidth]{nimplex/GraphComplex1Trim.png}
+        \includegraphics[width=0.5\textwidth]{nimplex/GraphComplex1Trim.jpeg}
     \end{adjustbox}
     \caption{Graph Complex Example \#1 depicting a problem space where 2 ternary systems can be connected through 6 different binary paths.} 
     \label{nimplex:fig:graphcomplex1}
@@ -409,7 +409,7 @@ \subsection{Simplex Graph Complexes} \label{nimplex:ssec:complexes}
     \end{adjustbox}
     \hspace{6pt}
     \begin{adjustbox}{valign=c}
-        \includegraphics[width=0.57\textwidth]{nimplex/GraphComplex2.png}
+        \includegraphics[width=0.57\textwidth]{nimplex/GraphComplex2.jpeg}
     \end{adjustbox}
     \caption{Graph Complex Example \#2 depicting a problem where 3 choices (D/E/F) can be made to traverse from ABC to G through dual ternary systems containing B. Vertices were spread in 3D to depict three possible ABC to G paths, which would exactly overlap in a plane.} 
     \label{nimplex:fig:graphcomplex2}
@@ -424,7 +424,7 @@ \subsection{Simplex Graph Complexes} \label{nimplex:ssec:complexes}
     \end{adjustbox}
     \hspace{6pt}
     \begin{adjustbox}{valign=c}
-        \includegraphics[width=0.6\textwidth]{nimplex/GraphComplex3.png}
+        \includegraphics[width=0.64\textwidth]{nimplex/GraphComplex3.jpeg}
     \end{adjustbox}
     \caption{Graph Complex Example \#3 depicting the possibility of competing paths, including cycles.} 
     \label{nimplex:fig:graphcomplex3}

pathplanning.tex

@@ -16,7 +16,7 @@ \section{Shortest Path Planning} \label{pathplan:sec:shortest}
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_Feasible.png}
+    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_Feasible.jpeg}
     \caption{The subgraph of feasible space extracted from the full 3-simplex (tetrahedral) graph in Figure \ref{infeasibilitygliding:fig:glide} constructed by gliding around an infeasible region of compositional space. The red path overlaid over directional edges indicates the optimal (least number of transitions) path was identified by the common Dijkstra's algorithm \cite{Dijkstra1959AGraphs}.}
     \label{pathplan:fig:shortestpath}
 \end{figure}
@@ -35,7 +35,7 @@ \subsection{Stretching the Space} \label{pathplan:ssec:gradientstretch}
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradient.png}
+    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradient.jpeg}
     \caption{The graph from Figure \ref{pathplan:fig:shortestpath} stretched through distance increases from penalizing high magnitude of gradient in a property value. The graph is approximately relaxed through spring-type energy minimization performed in \texttt{Wolfram} Language. The shortest path is still equally optimal in terms of the number of steps, but the selection has been biased towards the low-gradient region.}
     \label{pathplan:fig:lowgradient}
 \end{figure}
@@ -48,8 +48,8 @@ \subsection{Non-Linear Penalties Escaping Embedding} \label{pathplan:ssec:gradie
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradientSquared.png}
-    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradientSquaredColored.png}
+    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradientSquared.jpeg}
+    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_LowGradientSquaredColored.jpeg}
     \caption{Application of a highly non-linear (squared) property gradient magnitude penalty to the inter-node distances causing (top) unrelaxable (figure shows a local minimum) spatial arrangement of graph nodes, which can be visualized (bottom) through the color encoding of property field with path forming "switchbacks" akin to mountain roads that minimize sharp gradients at the cost of 3 additional steps.}
     \label{pathplan:fig:lowgradientsquared}
 \end{figure}
@@ -60,7 +60,7 @@ \section{Property Value Min/Maximization} \label{pathplan:sec:minmax}
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_HighRMSAD.png}
+    \includegraphics[width=0.9\textwidth]{pathplanning/InfeasibilityGliding_HighRMSAD.jpeg}
     \caption{A selection of an optimal path, similar to one in Figure \ref{pathplan:fig:shortestpath} but biased towards high property value regions (green) by penalizing going to lower property regions.}
     \label{pathplan:fig:highrmsad}
 \end{figure}