diff --git a/documents/write-math-ba-paper/write-math-ba-paper.bib b/documents/write-math-ba-paper/write-math-ba-paper.bib
index 3782ef1..20055c1 100644
--- a/documents/write-math-ba-paper/write-math-ba-paper.bib
+++ b/documents/write-math-ba-paper/write-math-ba-paper.bib
@@ -1,4 +1,4 @@
-% This file was created with JabRef 2.10b2.
+% This file was created with JabRef 2.10.
 % Encoding: UTF-8
 
 
@@ -1080,6 +1080,24 @@
   Url                      = {http://isl.anthropomatik.kit.edu/cmu-kit/downloads/CP_1996_01_A_Fast_Search_Technique_for_Large_Vocabulary_On-Line_Handwriting_Recognition.pdf}
 }
 
+@InProceedings{Manke1995,
+  Title                    = {{NPen}++: A writer independent, large vocabulary on-line cursive handwriting recognition system},
+  Author                   = {Stefan Manke AND Michael Finke AND Alex Waibel},
+  Booktitle                = {Proceedings of the Third International Conference on Document Analysis and Recognition},
+  Year                     = {1995},
+  Month                    = {Aug},
+  Pages                    = {403-408 vol.1},
+  Volume                   = {1},
+
+  __markedentry            = {[moose:6]},
+  Abstract                 = {In this paper we describe the NPen++ system for writer independent on-line handwriting recognition. This recognizer needs no training for a particular writer and can recognize any common writing style (cursive, hand-printed, or a mixture of both). The neural network architecture, which was originally proposed for continuous speech recognition tasks, and the preprocessing techniques of NPen++ are designed to make heavy use of the dynamic writing information, i.e. the temporal sequence of data points recorded on an LCD tablet or digitizer. We present results for the writer independent recognition of isolated words. Tested on different dictionary sizes from 1,000 up to 100,000 words, recognition rates range from 98.0% for the 1,000 word dictionary to 91.4% on a 20,000 word dictionary and 82.9% for the 100,000 word dictionary. No language models are used to achieve these results},
+  Doi                      = {10.1109/ICDAR.1995.599023},
+  Keywords                 = {handwriting recognition;image recognition;neural net architecture;LCD tablet;NPen++;common writing style;data points;digitizer;large vocabulary online cursive handwriting recognition system;neural network architecture;preprocessing techniques;temporal sequence;Character recognition;Computer science;Dictionaries;Handwriting recognition;Neural networks;Optical character recognition software;Shape;Speech recognition;Vocabulary;Writing},
+  Owner                    = {moose},
+  Timestamp                = {2014.12.26},
+  Url                      = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=599023&tag=1}
+}
+
 @InProceedings{Manke94,
   Title                    = {Combining Bitmaps with Dynamic Writing Information for On-Line Handwriting Recognition},
   Author                   = {Stefan Manke and Michael Finke and Alex Waibel},
@@ -1512,6 +1530,24 @@
   Url                      = {http://dx.doi.org/10.1109/34.57669}
 }
 
+@Misc{Thoma:2014,
+  Title                    = {On-line Recognition of Handwritten Mathematical Symbols},
+
+  Author                   = {Martin Thoma},
+  Month                    = nov,
+  Year                     = {2014},
+
+  Address                  = {Karlsruhe, Germany},
+  Keywords                 = {handwriting recognition; on-line; machine learning;
+ artificial neural networks; mathematics; classification;
+ supervised learning; MLP; multilayer perceptrons; hwrt;
+ write-math},
+  School                   = {Karlsruhe Institute of Technology},
+  Timestamp                = {2014.06.07},
+  Type                     = {{B.S. Thesis}},
+  Url                      = {http://martin-thoma.com/write-math}
+}
+
 @InProceedings{Thombre,
   Title                    = {Neural Network Approach to Recognize Online Handwriting Script},
   Author                   = {Deepali Thombre and Toran Verma},
@@ -1692,20 +1728,3 @@
   Url                      = {http://www1.icsi.berkeley.edu/Speech/faq/nn-train.html}
 }
 
-@Misc{Thoma:2014,
-    Title     = {On-line Recognition of Handwritten Mathematical Symbols},
-
-    Author    = {Martin Thoma},
-    Month     = nov,
-    Year      = {2014},
-    School    = "Karlsruhe Institute of Technology",
-    Address   = "Karlsruhe, Germany",
-    Type      = "{B.S. Thesis}",
-
-    Keywords  = {handwriting recognition; on-line; machine learning;
-                 artificial neural networks; mathematics; classification;
-                 supervised learning; MLP; multilayer perceptrons; hwrt;
-                 write-math},
-    Timestamp = {2014.06.07},
-    Url       = {http://martin-thoma.com/write-math}
-}
\ No newline at end of file
diff --git a/documents/write-math-ba-paper/write-math-ba-paper.pdf b/documents/write-math-ba-paper/write-math-ba-paper.pdf
index 8ea1f38..d977312 100644
Binary files a/documents/write-math-ba-paper/write-math-ba-paper.pdf and b/documents/write-math-ba-paper/write-math-ba-paper.pdf differ
diff --git a/documents/write-math-ba-paper/write-math-ba-paper.tex b/documents/write-math-ba-paper/write-math-ba-paper.tex
index 08a6951..8c251c9 100644
--- a/documents/write-math-ba-paper/write-math-ba-paper.tex
+++ b/documents/write-math-ba-paper/write-math-ba-paper.tex
@@ -18,7 +18,7 @@
 \makeglossaries
 
 \title{On-line Recognition of Handwritten Mathematical Symbols}
-\author{Martin Thoma}
+\author{Martin Thoma and Kevin Kilgour}
 
 \hypersetup{ 
   pdfauthor   = {Martin Thoma}, 
@@ -197,7 +197,7 @@ and a stroke-global feature.
 
 The simplest local feature is the coordinate of the point itself. Speed,
 curvature and a local small-resolution bitmap around the point, which was
-introduced by Manke, Finke and Waibel in~\cite{Manke94}, are other local
+introduced by Manke, Finke and Waibel in~\cite{Manke1995}, are other local
 features.
 
 \subsection{Multilayer Perceptrons}\label{sec:mlp-training}