From fa3270795787f3a05870209b73d2478b57fc7f92 Mon Sep 17 00:00:00 2001 From: Alex Blank Date: Wed, 10 Sep 2025 11:24:21 +0200 Subject: [PATCH] fixes --- thesis/sections/appendix.tex | 8 ++++---- thesis/sections/methodology.tex | 18 +++++++++++++----- thesis/sections/results.tex | 6 +++--- 3 files changed, 20 insertions(+), 12 deletions(-) diff --git a/thesis/sections/appendix.tex b/thesis/sections/appendix.tex index 08fb30a..5863284 100644 --- a/thesis/sections/appendix.tex +++ b/thesis/sections/appendix.tex @@ -21,7 +21,7 @@ and after-OV (0.0833) all occurring at 20 days. The \textbf{Convolutional Transformer} performs best overall at 40 days and best before ovulation at 160 days (MSE 0.0286). See Table~\ref{tab:ovover_windows_compact_mse} for a summary. -\begin{table}[t] +\begin{table}[htbp] \small \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt} @@ -68,7 +68,7 @@ with lowest before-OV MSE at 12/day (0.0255) and after-OV MSE at 288/day (0.0578 Results are summarized in Table~\ref{tab:ovover_resolution_compact_mse}; full resolution grids are in Appendix Table~\ref{tab:ov_over_results_by_resolution}. -\begin{table}[t] +\begin{table}[htbp] \small \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt} @@ -110,7 +110,7 @@ Finally, the \textbf{Convolutional Transformer} achieves its lowest overall and Table~\ref{tab:ovover_params_compact_mse} summarizes these parameter-dependent results; full comparisons are included in Appendix Tables~\ref{tab:ov_over_results_by_model_parameters_lstm}–\ref{tab:ov_over_results_by_model_parameters_conv_transformer}. -\begin{table}[t] +\begin{table}[htbp] \scriptsize \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt} @@ -198,7 +198,7 @@ As before, all learned models outperform the baselines by a wide margin. The full table with MSE and MAE for all models can be found in the appendix, Table~\ref{tab:regular_vs_irregular_ov_over_results}. -\section{Extra Figures}\label{app:figs} +\section{Extra Tables}\label{app:tabs} \begin{landscape} \begin{table} diff --git a/thesis/sections/methodology.tex b/thesis/sections/methodology.tex index 6817c66..a90f09f 100644 --- a/thesis/sections/methodology.tex +++ b/thesis/sections/methodology.tex @@ -286,8 +286,8 @@ Then each input token at time \( t \in \{1, \dots, T\} \) is: The full input sequence is then represented as a matrix: \[ X = \begin{bmatrix} - x_1 \\ - x_2 \\ + x_1 \\ + x_2 \\ \vdots \\ x_T \end{bmatrix} @@ -525,11 +525,11 @@ and whether incorporating data from previous cycles improves learning or introdu Window length & Historical context in days (input window size) & 10, 20, 40, 80, 160 \\ \bottomrule \end{tabular} - \caption{Input-related hyperparameters for LSTM and Transformer models.} + \caption{\parbox{\linewidth}{Input-related hyperparameters for LSTM and Transformer models.}} \label{tab:input_hyperparameters_basic} \end{table} -\begin{table}[ht] +\begin{table}[htbp] \centering \begin{tabular}{l>{\raggedright\arraybackslash}p{0.45\linewidth}>{\raggedright\arraybackslash}p{0.3\linewidth}} \toprule @@ -897,4 +897,12 @@ Explicit fairness evaluations and diverse validation cohorts are essential befor work is a medical device company, which underscores the need to guard against commercial bias. Results should be independently validated, and any translation into clinical or consumer use must be preceded by prospective, peer-reviewed trials. -Without such validation, deploying fertility prediction tools risks undermining trust and causing harm. \\ +Without such validation, deploying fertility prediction tools risks undermining trust and causing harm. + + +\section{Code Availability}\label{sec:code_availability} +The complete source code and LaTeX files used for this thesis are available at:\\ +\small +\url{https://gitlab.com/blankinator/temperature-based-fertility-prediction-thesis}\\ +\normalsize +The repository is archived under the release v1.1-thesis-final. \ No newline at end of file diff --git a/thesis/sections/results.tex b/thesis/sections/results.tex index c926c61..5ec9e8a 100644 --- a/thesis/sections/results.tex +++ b/thesis/sections/results.tex @@ -45,7 +45,7 @@ The \textbf{Convolutional Transformer} achieves the lowest overall MSE (0.0041) A summary of best-performing window lengths per architecture is shown in Table~\ref{tab:fertility_windows_compact_mse}; full results including MAE are provided in Appendix Table~\ref{tab:fertility_results_by_window_length}. -\begin{table}[t] +\begin{table}[htbp] \small \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt} @@ -100,7 +100,7 @@ though higher resolutions can be advantageous for detecting short-term fertile-d Results are summarized in Table~\ref{tab:fertility_resolution_compact_mse}; full metrics are in Appendix Table~\ref{tab:fertility_results_by_window_resolution}. -\begin{table}[t] +\begin{table}[htbp] \small \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt} @@ -148,7 +148,7 @@ Detailed comparisons are shown in Table~\ref{tab:fertility_params_compact_mse}, with full results in Appendix Tables~\ref{tab:fertility_results_by_model_parameters_lstm}–\ref{tab:fertility_results_by_model_parameters_conv_transformer}. -\begin{table}[t] +\begin{table}[htbp] \scriptsize \renewcommand{\arraystretch}{1.15} \setlength{\tabcolsep}{6pt}