Fix pagination <noupdate>

src/year1/cognition-and-neuroscience/module1/sections/_instrumental_learning.tex

@@ -539,7 +539,7 @@ Formalized goal-directed and habitual actions:
         \includegraphics[width=0.95\linewidth]{./img/human_latent_experiment2.png}
     \end{minipage}\\[1em]
 
-    \begin{minipage}{0.7\linewidth}
+    \begin{minipage}{0.6\linewidth}
         Behavioral results show that the majority of the candidates are able to make the optimal choice.
         This indicates that their behavior cannot be explained using a model-free learning theory (as learning only happens with a reward).
         A hybrid model has been proposed to model the candidates' behavior. It includes:
@@ -548,9 +548,9 @@ Formalized goal-directed and habitual actions:
             \item[State prediction error] Associated to model-based learning.
         \end{descriptionlist}
     \end{minipage}
-    \begin{minipage}{0.3\linewidth}
+    \begin{minipage}{0.4\linewidth}
         \centering
-        \includegraphics[width=\linewidth]{./img/human_latent_experiment3.png}
+        \includegraphics[width=0.7\linewidth]{./img/human_latent_experiment3.png}
     \end{minipage}\\[1em]
 
     On a neuronal level, fRMIs show that:

src/year1/cognition-and-neuroscience/module1/sections/_nervous_system.tex

@@ -58,7 +58,7 @@ There are 2 to 10 times more glia cells than neurons.\\
     \includegraphics[width=\textwidth]{./img/astrocyte.png}
 \end{minipage}\\[1em]
 
-\begin{minipage}{0.79\textwidth}
+\begin{minipage}{0.82\textwidth}
     \begin{descriptionlist}
         \item[Oligodendrocytes and Schwann cells] \marginnote{Oligodendrocytes\\Schwann cells}
             Oligodendrocytes are located in the central nervous system, while 
@@ -78,7 +78,7 @@ There are 2 to 10 times more glia cells than neurons.\\
             \end{remark}
     \end{descriptionlist}
 \end{minipage}
-\begin{minipage}{0.2\textwidth}
+\begin{minipage}{0.17\textwidth}
     \centering
     \includegraphics[width=\textwidth]{./img/insulation.png}
 \end{minipage}

src/year1/cognition-and-neuroscience/module1/sections/_pavlovian_learning.tex

@@ -109,14 +109,17 @@ There are two types of learning:
         \caption{Conditioning process}
     \end{figure}
 
-    The learned response lasts for days.
-    It can be observed that without training, the response disappears faster.
-
-    \begin{figure}[H]
-        \centering
-        \includegraphics[width=0.3\linewidth]{./img/gill_pavlovian_graph.png}
-        \caption{Withdrawal response decay}
-    \end{figure}
+    \begin{minipage}{0.55\linewidth}
+        The learned response lasts for days.
+        It can be observed that without training, the response disappears faster.
+    \end{minipage}
+    \begin{minipage}{0.4\linewidth}
+        \begin{figure}[H]
+            \centering
+            \includegraphics[width=0.6\linewidth]{./img/gill_pavlovian_graph.png}
+            \caption{Withdrawal response decay}
+        \end{figure}
+    \end{minipage}
 \end{casestudy}
 
 \begin{remark} \marginnote{Amygdala in Pavlovian learning}
@@ -445,7 +448,7 @@ There is strong evidence that the dopaminergic system is the major neural mechan
         \begin{casestudy}
             \phantom{}
             \begin{center}
-                \includegraphics[width=0.4\linewidth]{./img/dopamine_transfer_cs.png}
+                \includegraphics[width=0.38\linewidth]{./img/dopamine_transfer_cs.png}
             \end{center}
         \end{casestudy}
 
@@ -460,7 +463,7 @@ There is strong evidence that the dopaminergic system is the major neural mechan
             \end{minipage}
             \begin{minipage}{0.28\linewidth}
                 \centering
-                \includegraphics[width=0.9\linewidth]{./img/dopamine_blocking.png}
+                \includegraphics[width=0.8\linewidth]{./img/dopamine_blocking.png}
             \end{minipage}
         \end{casestudy}
 

src/year1/image-processing-and-computer-vision/module1/sections/_image_acquisition.tex

@@ -438,7 +438,7 @@ The image plane of a camera converts the received irradiance into electrical sig
     \end{itemize}
     \begin{figure}[H]
         \centering
-        \includegraphics[width=0.7\textwidth]{./img/_digitalization_quality.pdf}
+        \includegraphics[width=0.6\textwidth]{./img/_digitalization_quality.pdf}
         \caption{Sampling and quantization using fewer bits}
     \end{figure}
 \end{remark}

src/year1/image-processing-and-computer-vision/module2/sections/_architectures.tex

@@ -474,7 +474,7 @@ Network that aims to optimize computing resources.
 
                 \begin{figure}[H]
                     \centering
-                    \includegraphics[width=0.7\linewidth]{./img/_naive_inception.pdf}
+                    \includegraphics[width=0.65\linewidth]{./img/_naive_inception.pdf}
                     \caption{Naive inception module on the output of the stem layers}
                 \end{figure}
                 
@@ -489,7 +489,7 @@ Network that aims to optimize computing resources.
 
                 \begin{figure}[H]
                     \centering
-                    \includegraphics[width=0.7\linewidth]{./img/_actual_inception.pdf}
+                    \includegraphics[width=0.65\linewidth]{./img/_actual_inception.pdf}
                     \caption{Actual inception module on the output of the stem layers}
                 \end{figure}
             \end{description}

src/year1/image-processing-and-computer-vision/module2/sections/_image_formation.tex

@@ -471,14 +471,13 @@ Therefore, the complete workflow for image formation becomes the following:
 
                 \begin{figure}[H]
                     \centering
-                    \includegraphics[width=0.45\linewidth]{./img/_zhang_image_acquistion.pdf}
+                    \includegraphics[width=0.4\linewidth]{./img/_zhang_image_acquistion.pdf}
                     \caption{Example of two acquired images}
                 \end{figure}
         \end{description}
 
     \item[Initial homographies guess]
         For each image $i$, compute an initial guess of its homography $\matr{H}_i$.
-
         Due to the choice of the $z$-axis position, the perspective projection matrix and the WRF points can be simplified:
         \[ 
             \begin{split}