diff --git a/src/year1/cognition-and-neuroscience/module1/sections/_instrumental_learning.tex b/src/year1/cognition-and-neuroscience/module1/sections/_instrumental_learning.tex index c66e97b..dec5330 100644 --- a/src/year1/cognition-and-neuroscience/module1/sections/_instrumental_learning.tex +++ b/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: diff --git a/src/year1/cognition-and-neuroscience/module1/sections/_nervous_system.tex b/src/year1/cognition-and-neuroscience/module1/sections/_nervous_system.tex index d30d2e0..a43664f 100644 --- a/src/year1/cognition-and-neuroscience/module1/sections/_nervous_system.tex +++ b/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} diff --git a/src/year1/cognition-and-neuroscience/module1/sections/_pavlovian_learning.tex b/src/year1/cognition-and-neuroscience/module1/sections/_pavlovian_learning.tex index 1c62999..8564d3f 100644 --- a/src/year1/cognition-and-neuroscience/module1/sections/_pavlovian_learning.tex +++ b/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} diff --git a/src/year1/image-processing-and-computer-vision/module1/sections/_image_acquisition.tex b/src/year1/image-processing-and-computer-vision/module1/sections/_image_acquisition.tex index e8c663a..b15a0cf 100644 --- a/src/year1/image-processing-and-computer-vision/module1/sections/_image_acquisition.tex +++ b/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} diff --git a/src/year1/image-processing-and-computer-vision/module2/sections/_architectures.tex b/src/year1/image-processing-and-computer-vision/module2/sections/_architectures.tex index 7cc56e8..2d56ca0 100644 --- a/src/year1/image-processing-and-computer-vision/module2/sections/_architectures.tex +++ b/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} diff --git a/src/year1/image-processing-and-computer-vision/module2/sections/_image_formation.tex b/src/year1/image-processing-and-computer-vision/module2/sections/_image_formation.tex index a141cb6..803c9b4 100644 --- a/src/year1/image-processing-and-computer-vision/module2/sections/_image_formation.tex +++ b/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}