From 6f67d0fed3f36b2ca503bc817eb0844a2ad7cf92 Mon Sep 17 00:00:00 2001
From: Sebastian Will <swill@csail.mit.edu>
Date: Thu, 16 Sep 2021 16:14:54 +0200
Subject: [PATCH] "typos"

---
 infrared-bookchapter.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/infrared-bookchapter.tex b/infrared-bookchapter.tex
index 47c991d..8684f29 100644
--- a/infrared-bookchapter.tex
+++ b/infrared-bookchapter.tex
@@ -251,7 +251,7 @@ This notebook and other tutorials and example notebooks are provided at...
 \section{Methods}
 
 Note that all example code can be run from a provided IPython notebook, available for download from
-Infrared's Gitlab repository (\url{https://gitlab.inria.fr/amibio/Infrared/-/tree/v1.0/Doc/bookchapter.ipynb}).
+Infrared's Gitlab repository (\url{https://gitlab.inria.fr/amibio/Infrared/-/tree/v1.0/Doc/bookchapter-tutorial.ipynb}).
 
 \subsection{Elementary use of \Infrared---A simple design model}
 
@@ -521,7 +521,7 @@ Finally, since the computation requires to enumerate all possible sub-assign\-me
     \label{fig:dependency-graph}
 \end{figure}
 
-\paragraph{Targeting specific properties.} For targeting very specific properties like certain \GC content and energies of the target structures, \Infrared utilizes the just described sampling engine to iteratively sample from a multi-dimensional Boltzmann distribution, evaluate the generated distribution w.r.t.{} the target properties of the single targeted features and update their weights. By suitable updpates of the weights, it is possible to shift the distribution towards the targeted feature values and increase the probability to satisfy these targets (within the given tolerance). During this entire learning procedure, \Infrared returns samples inside of the tolerance range and rejects all others. In this way, \Infrared implements a variant of multi-dimensional Boltzmann sampling~\cite{Bodini2010}, which let's it solve the kind of complex constraints set are set by targeting certain tolerance ranges for features (which are composed from 'local' functions).
+\paragraph{Targeting specific properties.} For targeting very specific properties like certain \GC content and energies of the target structures, \Infrared utilizes the just described sampling engine to iteratively sample from a multi-dimensional Boltzmann distribution, evaluate the generated distribution w.r.t.{} the target properties of the single targeted features and update their weights. By suitable updates of the weights, it is possible to shift the distribution towards the targeted feature values and increase the probability to satisfy these targets (within the given tolerance). During this entire learning procedure, \Infrared returns samples inside of the tolerance range and rejects all others. In this way, \Infrared implements a variant of multi-dimensional Boltzmann sampling~\cite{Bodini2010}, which let's it solve the kind of complex constraints set are set by targeting certain tolerance ranges for features (which are composed from 'local' functions).
 
 As a consequence of this entire mechanism, the sampling efficiency in \Infrared is a result of the complexity of the constraint network as well as the (in)dependence of the targeted features and the demanded tolerances. For practical applications of \Infrared it is thus generally advantageous to be aware of the properties of the solving strategy and the resulting dependencies between these factors. This is especially important, since the framework easily allows modeling extremely hard problems, while (as we demonstrate) it is useful for a wide range of applications in practice. Its specific properties make the system attractive for a variety of complex design applications but as well intrinsically influence its applicability. 
 
-- 
GitLab