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symbols.tex
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\glsxtrnewsymbol[description={surface parallel momentum in the x-direction}]{kx}{\ensuremath{k_x}}
\glsxtrnewsymbol[description={surface parallel momentum in the y-direction}]{ky}{\ensuremath{k_y}}
\glsxtrnewsymbol[description={surface perpendiclar momentum component}]{kz}{\ensuremath{k_z}}
\glsxtrnewsymbol[description={fermi level energy}]{EF}{\ensuremath{E_F}}
\glsxtrnewsymbol[description={energy of the emitted electron}]{E}{\ensuremath{E}}
\glsxtrnewsymbol[description={pump-probe time}]{tpp}{\ensuremath{t_{pp}}}
\glsxtrnewsymbol[description={intensity}]{I}{\ensuremath{I}}
% Define datasets GrIr, NiW, WSe2, GdW
\glsxtrnewsymbol[description={Graphene on Iridium(111)}]{GrIr}{\ensuremath{\mathrm{Gr/Ir(111)}}}
\glsxtrnewsymbol[description={Nickel on Tungsten(110)}]{NiW}{\ensuremath{\mathrm{Ni/W(110)}}}
\glsxtrnewsymbol[description={Tungsten Diselenide}]{WSe2}{\ensuremath{\mathrm{WSe}_2}}
\glsxtrnewsymbol[description={Gadolinium on Tungsten(110)}]{GdW}{\ensuremath{\mathrm{Gd/W(110)}}}
% True and Noisy image
\glsxtrnewsymbol[description={latent clean (true inaccessible) image}]{Y}{\ensuremath{Y}}
\glsxtrnewsymbol[description={noisy/corrupted/incomplete image}]{X}{\ensuremath{X}}
\glsxtrnewsymbol[description={denoised/restored/reconstructed image}]{y_hat}{\ensuremath{\hat{Y}}}
% single slice and window averaged
\glsxtrnewsymbol[description={number of slices summed along an axis}]{winsize}{\ensuremath{w}}
%Total observation time
\glsxtrnewsymbol[description={total observation time/acquisition time}]{total_time}{\ensuremath{T}}
% time interval
\glsxtrnewsymbol[description={time interval}]{time_interval}{\ensuremath{\Delta t}}
% Number of counts
\glsxtrnewsymbol[description={number of observations/electron counts}]{ncounts}{\ensuremath{n_{\mathrm{count}}}}
% \glsxtrnewsymbol[description={window averaging along an axis, forming a 2D image e.g. average over 15 slices as $X_{15}$, and single slice image as $X_1$}]{Xw}{\ensuremath{X_w}}
% BM3D sigma for noise level
\glsxtrnewsymbol[description={noise level parameter for BM3D denoising.}]{sigma}{\ensuremath{\sigma}}
% Poisson noise
\glsxtrnewsymbol[description={Poisson noise present in imaging processes.}]{poisson}{\ensuremath{\lambda}}
% Symbols List for Deep Learning
% Learning rate
% \glsxtrnewsymbol[description={learning rate, controls the step size of gradient descent.}]{lr}{\ensuremath{\eta}}
% Weight vector
\glsxtrnewsymbol[description={weight vector of a learner.}]{wvec}{\ensuremath{\mathbf{w}}}
% Bias term
% \glsxtrnewsymbol[description={bias term of a learner.}]{bias}{\ensuremath{b}}
% Loss function
\glsxtrnewsymbol[description={loss function, a measure of prediction error.}]{loss}{\ensuremath{\ell}}
% % Input vector
% \glsxtrnewsymbol[description={Input feature vector to the model.}]{xvec}{\ensuremath{\mathbf{x}}}
% % Output vector
% \glsxtrnewsymbol[description={Output vector or prediction of the model.}]{yhat}{\ensuremath{\hat{\mathbf{y}}}}
% % True output
% \glsxtrnewsymbol[description={True output label or ground truth.}]{ytrue}{\ensuremath{\mathbf{y}}}
% % Weight matrix
% \glsxtrnewsymbol[description={Weight matrix in a neural network.}]{wmat}{\ensuremath{\mathbf{W}}}
% % Model parameters (weights and biases)
% \glsxtrnewsymbol[description={Model parameters, typically referring to weights and biases.}]{params}{\ensuremath{\theta}}
% Hypothesis class
\glsxtrnewsymbol[description={set of functions accessible to the learner}]{hypo}{\ensuremath{\mathcal{H}}}
% Generalization error
\glsxtrnewsymbol[description={generalization error, the expected error on unseen data.}]{generr}{\ensuremath{\mathcal{R}}}
% Train error
\glsxtrnewsymbol[description={training error/empirical risk.}]{trainerr}{\ensuremath{\mathcal{L}}}
% hypothesis
\glsxtrnewsymbol[description={hypothesis of a model.}]{hypothesis}{\ensuremath{h}}
% detector coordinates
% \glsxtrnewsymbol[description={detector x coordinate, that maps to \gls{kx}}]{x}{\ensuremath{{x}}}
% \glsxtrnewsymbol[description={detector y coordinate, that maps to \gls{ky}}]{y}{\ensuremath{{y}}}
\glsxtrnewsymbol[description={detector time-of-flight coordinate, that maps to \gls{E}}]{tof}{\ensuremath{{t_{tof}}}}
% L2
\glsxtrnewsymbol[description={L2 norm}]{l2}{\ensuremath{L_2}}