decapitalize all instances of Notebook and fix notebook number typo (addresses #171)

Author: cwehmeyer

manuscript/manuscript.tex

@@ -256,7 +256,7 @@ \subsection{Hidden Markov state models}
 The estimation of an MSM requires the dynamics between microstates to be Markovian.
 However, in case of a poor dimension reduction and/or discretization or short trajectories,
 we cannot anticipate this to be the case.
-We illustrate this point in Notebook~07.
+We illustrate this point in notebook~07.
 
 An alternative, which is much less sensitive to poor discretization,
 is to estimate a hidden Markov model (HMM)~\cite{hmm-baum-welch-alg,hmm-tutorial,jhp-spectral-rate-theory,noe-proj-hid-msm,bhmm-preprint}.
@@ -279,7 +279,7 @@ \subsection{Hidden Markov state models}
 
 An HMM estimation always yields a model with a small number of (hidden) states
 where each state is considered to be metastable and,
-thus, the number of hidden states is a new hyper-parameter which needs to be chosen carefully (see Notebook~07).
+thus, the number of hidden states is a new hyper-parameter which needs to be chosen carefully (see notebook~07).
 As the HMMs---like MSMs---approximate the full phase-space dynamics,
 we can similarly compute the metastable kinetics, apply TPT, visualize the network, and obtain physical observables.
 
@@ -375,10 +375,10 @@ \subsection{Feature selection}
 Here, we utilize the VAMP-2 score, which maximizes the kinetic variance contained in the features~\cite{kinetic-maps}.
 We should always evaluate the score in a cross-validated manner to ensure that we neither include too few features (under-fitting) or too many features (over-fitting)~\cite{gmrq,vamp-preprint}.
 To choose among three different molecular features reflecting protein structure,
-we compute the (cross-validated) VAMP-2 score (Notebook 00).
+we compute the (cross-validated) VAMP-2 score (notebook 00).
 Although we cannot MSM optimize lag times with a variational score\cite{husic2017note}, such as VAMP-2,
 it is important to ensure that properties that we optimize are robust as a function of lag time. 
-Consequently, we compute the VAMP-2 score at several lag times (Notebook 00). 
+Consequently, we compute the VAMP-2 score at several lag times (notebook 00). 
 We find that the relative rankings of the different molecular features are highly robust as a function of lag time. 
 We show one example of this ranking and the absolute VAMP-2 scores for lag time~$0.5$~ns in Fig.~\ref{fig:io-to-tica}b. 
 We find that backbone torsions contain more kinetic variance than the backbone heavy atom positions or the distances between them (Fig.~\ref{fig:io-to-tica}b).
@@ -584,7 +584,7 @@ \subsection{Modeling large systems}
 This problem may be mitigated by choosing a more specific set of features.
 
 Additional technical challenges for large systems include high demands on memory and computation time;
-we explain how to deal with those in the tutorials (Notebook 00 and 02).
+we explain how to deal with those in the tutorials (notebooks~00 and~01).
 
 More details on how to model complex systems with the techniques presented here are described, e.g., by~\cite{plattner_protein_2015,plattner_complete_2017}.