text:
%!TEX root = ../intro.tex
%******************************
%	 Other applications of scRNAseq
%*****************************

\section{General applications of scRNA-Seq in biology}

The following section outlines the applications of scRNA-Seq to a broad spectrum of biological systems.
Whole transcriptomic read-outs of individual cells allowed the in-depth characterisation of embryonic development, haematopoiesis, immune responses, the detection of rare cell types and have led to new insights into disease progression including cancer development.

\subsection{Atlas-type approaches}

Until recently, scRNA-Seq technologies generated transcriptomes of less than one thousand cells to study cell type heterogeneity, allele-specific expression or pseudo-temporal trajectories in cellular systems \citep{Kolodziejczyk2015review}.
With the increased scalability of scRNA-Seq technologies, cellular composition of whole tissues and organisms can be assayed. The largest of these so called "expression atlases" to date is the 10X Genomics\textsuperscript{\textregistered}{} brain dataset comprising 1.3 million cells from embryonic mice.
It was generated using 133 libraries sequenced on 11 Illumina HiSeq\textsuperscript{\textregistered}{} 4000 flowcells \citep{10XGenomics2017}.
This experiment has been performed to exemplify the applicability of the commercial 10X Genomics platform to generate more than 10 billion transcriptomes of individual cells across the human body as envisioned by the Human Cell Atlas Consortium \citep{Regev2017}.\\

So far, transcriptional atlases that comprise thousands of cells include the mouse cell atlas, a thymus organogenesis atlas, an ageing lung atlas and the full characterisation of cell types in \textit{C. elegans}.
For example, Microwell-Seq was developed to capture more than 400,000 cells covering all mouse organs.
This analysis reveals rare cell types such as 2-cell-stage like mESCs and allows the construction of a cross-tissue correlation network \cite{Han2018}.
Similarly, the \emph{Tabula Muris} aimed at detecting all major cell type across 20 organs of the mouse.
Here, the Tabula Muris Consortium used droplet-based 3'-end scRNA-Seq and FACS-based full length transcript analysis to generate (i) a broad atlas and (ii) an in-depth characterisation of each tissue \citep{Quake2018}. Cao \emph{et al.}, 2017 generated more than 40,000 cells from the L2 stage \emph{C. elegans} using sci-RNA-Seq and identified nineteen distinct cell types and seven mixed cell types.
Furthermore, this atlas allows the dissection of neuronal cell types that split across seven clusters \citep{Cao2017}.
To study thymus development, Kernfeld \emph{et al.}, 2018 generated around 25,000 transcriptomes of individual cells from the embryonic thymus at E12.5, E13.5, E14.5, E15.5, E16.5, E17.5, E18.5, and P0.
This experimental set-up resolves the temporal development of immune cell types such as T cells, myeolid cells, natural killer cells, innate lymphoid cells, and $\gamma{}\delta{}$ T cells as well as thymic epithelial cells \citep{Kernfeld2018}.
Finally, to study the effect of ageing on a whole tissue, Angelidis \emph{et al.}, 2018 isolated 14,000 cells from lungs of young and old animals and found (i) an increase in transcriptional noise during ageing and (ii) altered transcriptional profiles of alveolar macrophages and type 2 pneumocytes \citep{Angelidis2018}.\\

The following paragraphs summarise scRNA-Seq applications that are aimed at more targeted analysis of regulatory processes.

\subsection{Developmental biology}

Over the last few years, the development of new scRNA-Seq technologies and algorithms to perform data analysis uncovered driving factors in development and cell fate decisions \citep{Griffiths2018}.
An early study of mouse embryonic development identified that transcriptional differences between the two cells in the 2-cell stage embryo increase from the zygote to late 2-cell stage embryos.
This is caused by an initial partitioning "error" where transcripts are unevenly distributed between the daughter cells.
Later on, these differences are strengthened by the onset of transcription coupled to transcriptional noise \citep{Piras2014, Shi2015a}.
A reproducible distribution of transcripts in the first cell division was also detected by Biase \emph{et al.}, 2014 \cite{Biase2014}.
These biases between cells at the 2-cell stage propagate to form transcription biases at the 4-cell pushing cells towards forming the pluripotent inner cell mass or the extra-embryonic trophoectoderm \citep{Goolam2016, Shi2015a}.
To obtain a more complete view on gastrulation in the mouse, Scialdone \emph{et al.} captured cells from the epiblast at E6.5 and mesodermal cells at E7.0, E7.5 and E7.75.
The authors also sampled cells from \emph{Tal1} knock-out animals and showed that this transcription factor is a key regulator for blood develeopment \citep{Scialdone2016}. \\

This year, large-scale scRNA-Seq studies profiled organogenesis in the mouse and zebrafish.
Ibarra-Soria \emph{et al.}, 2018 sampled more than 20,000 cells from E8.25 embryos following gastrulation and identified 20 major cell types including different mesoderm lineages, neural progenitor cells, blood, gut and extra-embryonic cells.
They further used this data to dissect gut formation and to find oscillating expression patterns during somitogenesis \citep{Ibarra-Soria2018}.
Similarly, inDrop and Drop-Seq approaches were used to generate $\sim$7000 cells from \gls{Dmelanogaster} embryos at the onset of gastrulation \citep{Karaiskos2017} or to generate more than 90,000 cells from the zebrafish embryo during the first day of development \citep{Wagner2018}.\\

In the last two years, experimental procedures were developed to track cells across multiple divisions termed "lineages".
For this, the genome editing tool: \gls{CRISPR}/\gls{Cas9} \citep{Jinek2012}, was used to introduce so called "scars" at specific DNA sequences.
In bacteria, the CRISPR/Cas system is used to degrade invasive DNA which involves a CRISPR RNA that recognises the invading DNA and a Cas protein for degradation.
For genome editing purposes, the \gls{CRISPR}/\gls{Cas9} uses \glspl{sgRNA} to specific genomic sites and induces \gls{DSB}.
Upon repair, insertions or deletion mutations are introduced that render a specific gene non-functional \citep{Zhang2014c}.
The first approach to use the \gls{CRISPR}/\gls{Cas9} for scarring: \gls{GESTALT}, inserted an array of 10 \gls{CRISPR}/\gls{Cas9} with variable specificity into the genome of individual cells.
Upon the expression of the Cas9 protein and the \gls{sgRNA}, random scars are introduced into the genomic array.
After days of growth, genomic DNA was harvested and the array was sequenced to construct the relationship between individual cells \textbf{(Fig.~\ref{fig0:CRISPR})} \citep{McKenna2016}.
This technology has been extended as a scRNA-Seq approach to capture the RNA together with the expressed \gls{CRISPR}/\gls{Cas9} array that allows cell type assessment alongside lineage detection.
This approach also includes a heat shock inducible system to start the scarring at later stages of development \citep{Raj2018}.
Similar approaches used multiplexed smFISH read-outs to infer lineage relationship between individual cells \citep{Frieda2017} or tranposase-based insertion of a random 20mer sequence into the genome \citep{Wagner2018}.\\

One current challenge, especially in the field of developmental biology, is to obtain spatially-resolved whole-transcriptome read-outs of individual cells.
The imaging technologies introduced above, MERFISH and SeqFISH, are capable of capturing single RNA molecules of thousands of genes across thousands of cells.
Early approaches in the field of spatial transcriptomics employed spatial gene expression atlases to map isolated single cells back into the tissue of origin \citep{Achim2015, Satija2015a}.
A similar approach has recently been used to spatially locate cells isolated form the \gls{Dmelanogaster} embryo \citep{Karaiskos2017}.
Moreover, Tomo-Seq was developed to sequence RNA extracted from slices of the zebrafish embryos.
RNA was extracted from each slice into a tube and barcoded prior to sequencing.
Matched histology and mathematical modelling was used to reconstruct the spatial expression patterns across the embryo \citep{Junker2014a}.

\begin{figure}[!h]
\centering
\includegraphics[width=\textwidth]{Fig_17.png}
\caption[CRISPR/Cas9-scarring for lineage tracing]{\textbf{CRISPR scarring for lineage tracing.}\\
A cassette of 10 CRISPR targets is inserted into the genome.
Upon Cas9 expression, random insertions and deletions are added to these cassettes.
Single-cell DNA or RNA sequencing allows detecting the scars and therefore cell lineage reconstruction.}
\label{fig0:CRISPR}
\end{figure}

\subsection{Cell type evolution}

Evolutionary biology is a research field that less frequently uses scRNA-Seq approaches to understand the evolutionary origin of cell types.
To do this, cell compositions of non-model organisms such as \gls{Pdumerilii} (annelid), \gls{Nvectensis} (cniderian), \gls{Aqueenslandica} (sponge), \gls{Mleidyi} (ctenophore) and \gls{Tadhaerens} (placozoan) are compared.
All of these organisms (except annelids) are non-bilaterians and therefore evolutionary older than mouse and humans.
As an example and part of an early project, we used the developing larva of \textit{P. dumerilii} to study diversification of cell types in early bilaterian evolution.
We detected cells from the apical neuroectoderm, the midgut, striated musculatrue, ciliated cells and non-apical blastopore cells.
By assessing the transcriptional distance between these cell types, we formulated a hypothesis of related cell type families that originate from an ancesteral cell type and are conserved during evolution \citep{Achim2018}.  \\

Seb\'e{}-Pedr\'o{}s \emph{et al.}, 2018 generated a single-cell expression atlas of adult and larval \textit{N. vectensis} using MARS-Seq.
This dataset allowed the dissection of neuronal diversification and transcription factor regulatory programmes in this early sister group of bilaterians \citep{Sebe-Pedros2018}.
The authors furthermore generated similar atlases of \textit{A. queenslandica}, \textit{M. leidyi} and \textit{T. adhaerens} and performed cross-species gene module analysis after cell type identification.
Co-regulation of cell type-specific gene modules strongly diverged between the species except for a few house keeping modules.
Moreover, regulatory TF modules appear to be cell type and species-specific \citep{Sebe-Pedros2018a}.
These studies highlight scRNA-Seq as a powerful tool to study inter-species relationships of cell types in non-model organisms.

\subsection{Immunology}

The immune system has been extensively studied using scRNA-Seq to detect activation responses and to dissect the transcriptional heterogenity among immune cells \citep{Proserpio2015, Satija2014}.
White blood cells are broadly grouped into cells of the innate and adaptive immune system.
Innate immune cells (\gls{DC}, mast cells, macrophages, basophils, neutrophils, \gls{NK} cells, eosinophils) are fast responders that represent the first line of defence upon infection.
The adaptive immune system (B cells, CD4\plus{} and CD8\plus{} T cells) responds more slowly but installs an antigenic specificity and immune memory after infection.
\gls{NK} T cells and \textgamma{}\textdelta{} T cells are cytotoxic lymphocytes that show both innate and adaptive characteristics \textbf{(Fig.~\ref{fig0:immune_system})} \citep{Dranoff2004}.

\begin{figure}[!h]
\centering
\includegraphics[width=0.9\textwidth]{Fig_18.png}
\caption[Cell types of the adaptive and innate immune system]{\textbf{Cell types of the adaptive and innate immune system.}}
\label{fig0:immune_system}
\end{figure}

An unbiased approach analysed $\sim$65,000 human \glspl{PBMC} and identified the major innate and adaptive immune cell types.
The authors further used droplet-based scRNA-Seq to study bone marrow mononuclear cells after haematopoietic stem cell transplant in an \gls{AML} patient.
The technology allowed the distinction between host and donor cells and the detection of residual AML cells in the host \citep{Zheng2017}. \\

More targeted approaches focused on either cells of the innate or adaptive immune system.

\subsubsection{scRNA-Seq to study innate immunity}

Villani \emph{et al.}, 2017 used plate-based scRNA-Seq to dissect the \gls{DC} and monocyte compartment of human \glspl{PBMC}.
In general, DCs can be subdivided into CD11C\plus{} conventional DCs (CD141\plus{} and CD1C\plus{}) that activate CD4\plus{} and CD8\plus{} and interferon producing, plasmacytoid DCs.
Monocytes were classically subdivided into CD14\plus{} and CD16\plus{} cells.
After analysing more than 2,400 DCs and monocytes, the authors expanded DCs to consist of 6 groups and detected conventional DC progenitor cells.
Furthermore, they detected two new groups of uncharacterised monocytes \citep{Villani2017}.
Shalek \emph{et al.}, 2014 used the Fluidigm C1 system to generate transcriptomes of more than 1700 primary mouse bone marrow derived DCs to study their activation response during \gls{LPS} stimulation.
Within one hour of activation, early responding cells up-regulate \gls{Ifn}\textbeta{} and support the activation of surrounding cells via paracrine signalling.
By isolating activated cells in individual chambers, the authors showed a decrease in the total number of activated cells after 4h stimulation with LPS.
Furthermore, activated cells need autocrine stimulation to fully activate and Ifn\textbeta{} secretion during the first hour of activation is the crucial trigger for homogeneous DC activation \citep{Shalek2014}.
Bj\"o{}rklund \emph{et al.}, 2016 performed targeted scRNA-Seq of Lin\textsuperscript{-}CD127\plus{} \glspl{ILC} and NK cells from tonsil tissue of adult humans using the SmartSeq2 protocol.
They firstly identified the three major lineages of ILCs (ILC1, ILC2 and ILC3) and further assessed heterogeneity within the ILC3 population.
Dissecting this rare cell type allows a deeper understanding of immune regulation in humans \citep{Bjorklund2016}.

\subsubsection{scRNA-Seq to study adaptive immunity}

The majority of scRNA-Seq studies focused on dissecting heterogeneity in the CD4\plus{} or CD8\plus{} T cell compartment to understand adaptive immunity.
For example, Proserpio \emph{et al.}, 2016 studied CD4\plus{} Th2 cell differentiation after \textit{Nippostrongylus brasiliensis} infection.
5 days after infection, three major cell states can be detected: (i) activated cells, (ii) proliferating cells and (iii) Th2 cytokine expressing cells.
Furthermore, more differentiated, cytokine-producing cells show higher proliferation which was validated by an \emph{in vivo} model of Th1 differentiation upon malaria infection \citep{Proserpio2016}.
A follow-up study characterised the time course of malaria infection by sampling labelled CD4\plus{} T cells 2, 3, 4 and 7 days after infection. Gaussian process modelling of expression changes over the differentiation time course revealed a bifurcation between the Th1 and \gls{Tfh} cell lineage.
Identifying such branching dynamics during cell differentiation allows the characterisation of decision-making molecules and underlying regulatory mechanisms \citep{Lonnberg2017}. \\

Prior to differentiation and to trigger activation responses, the \gls{TCR} on T cells recognises peptides bound to \gls{MHCI} molecules on an antigen-presenting cell (e.g.~DC). Upon peptide recognition and co-activator stimulation, cytoskeletal rearrangements, metabolic changes and transcription-factor activity lead to the proliferation and effector differentiation of T cells \citep{Richard2018}.
Richard \emph{et al.}, 2018 studied the effects of reduced TCR-peptide affinity and discovered that CD8\plus{} cells show heterogeneous response patterns to stimulation with low-affinity peptides.
Nevertheless, once activated, CD8\plus{} T cells reach similar activation stages compared to cells activated with high-affinity peptides \citep{Richard2018}.
Similar to the differentiation time course experiments using CD4\plus{} T cells, Kakaradov \emph{et al.}, 2017 tracked expression changes over the differentiation course of CD8\plus{} T cells after lymphocytic choriomeningitis virus infection.
The authors detected strong divergence during the first division of CD8\plus{} T cells after infection which is strengthened by epigenetic silencing of genes associated to memory formation \citep{Kakaradov2017}.\\

The development of scRNA-Seq technologies has also driven the expansion of tools to analyse single-cell transcriptome data.
Key algorithms for T cell and B cell analysis include the reconstruction of the TCR and \gls{BCR}.
Constructing the TCR of individual cells allows the identification of clonal expansion of CD4\plus{} T cells upon \textit{Salmonella} infection \citep{Stubbington2016}.
Similarly, the BCR can be reconstructed to detect clonal heterogeneity in B cell populations \citep{Canzar2017, Wu2018}

\subsection{Tissue function and disease}

One example for scRNA-Seq applications to study tissue functions is the work by Sten Linnarsson's lab that aims at dissecting the complexity of the mammalian brain.
In an early study, Zeisel \emph{et al.}, 2015 sequenced $\sim$ 3000 cells from the mouse somatosensory cortex and hippocampal \gls{CA}1 region and identified a variety of interneurons, S1 pyramidal neurons, CA1 pyramidal neurons, mural cells, endothelial cells, microglia cells, ependymal cells, astrocytes and oligodendrocytes \citep{Zeisel2015}.
More recently, H\"a{}ring \emph{et al.}, 2018 studied sensory neurons in the dorsal horn of mice and detected multiple sub-groups of \gls{GABA}ergic and glutamatergic neurons \citep{Haring2018}.
A large-scale study of more than 500,000 cells of the mouse nervous system generated an atlas comprising all major neuronal and non-neuronal cell types. For this, cells from anatomical units of the brain as well as cells from the spinal cord, dorsal root ganglia, sympathetic ganglion and the enteric nervous system were sampled \citep{Zeisel2018}.
On a smaller scale, Davie \emph{et al.}, 2018 generated an atlas of 57,000 cells from the \gls{Dmelanogaster} brain from young and old animals \cite{Davie2018}.\\

Bach \emph{et al.}, 2017 used droplet-based scRNA-Seq to dissect the development of the adult mammary gland.
By sampling cells from the mammary gland at different time points (8 weeks virgin, 14.5 days gestation, 6 days lactation and 11 days post involution), the authors were able to reconstruct differentiation processes including the development of hormone-sensing and secretory cells \citep{Bach2017}.
To study the spatial expression pattern of the liver, Halpern \emph{et al.}, 2017 mapped scRNA-Seq profiles onto 9 layers around the central vein.
This approach allows the detection of novel gene expression patterns that correlated with the distance of the cell layer to the central vein \citep{Halpern2017}.
Young \emph{et al.}, 2018 generated 70,000 single-cell transcriptomes from human renal tumours as well as healthy foetal, pediatric, and adult kidneys.
With this data, the authors linked childhood Wilms tumour to abnormal foetal cells and identified precursor cells for adult tumours \citep{Young2018}. \\

Extending this, scRNA-Seq was used to study malignant tumours where full characterisation is hindered by complex cellular heterogeneity.
Early work by Patel \emph{et al.}, 2014 identified strong heterogeneity within glioblastomas of five patients related to oncogenic signalling, proliferation, immune response, and hypoxia.
Furthermore, within each tumour, cells are classified as different glioblastoma sub-types which potentially complicates treatment strategies \citep{Patel2014}.
More recently, scRNA-Seq has been performed to dissect the multicellular ecosystems of metastatic melanoma \citep{Tirosh2016} and head and neck cancer \citep{Puram2017}.
In both studies, malignant cells clustered patient-dependently and non-malignant cells clustered based on their cell type.
These studies highlight the extreme transcriptional heterogeneity within and between tumours which complicates standard therapies. \\

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text:
\chapter{Results}

\import{.}{dataset}
\import{.}{model_performances}
\import{.}{training}
\import{.}{confusion_matrices}

ext: tex

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text:
\section{Setup}
\frame{\tableofcontents[currentsection, hideothersubsections]}

\begin{frame}
\frametitle{Experiment Setup: Tasks}

\begin{figure}
    \centering
    \includegraphics[scale=0.3]{fig_1}
\end{figure}

\begin{itemize}
  \item 20 simulated physics task, including:
  \begin{itemize}
    \item gripperRandom:
    Agent must use an arm with gripper appendage to grasp an object and manuver the object, which
    are initialized in random locations
    % \item reacher3daRandomTarget:
    % Agent is required to move a 7-DOF human-like arm from random starting locations to random target positions.
    \item reacherObstacle:
    Agent is required to move a 5-DOF arm around an obstacle to a randomized target position.
  \end{itemize}
  \item features, both:
    \begin{itemize}
    \item a low-dimensional state description (such as joint angles and positions)
    \item high-dimensional renderings of the environment
  \end{itemize}
\end{itemize}

\end{frame}

\begin{frame}
\frametitle{Experiment Setup: Tasks}

\begin{figure}
    \centering
    \includegraphics[scale=0.25]{task_dim}
\end{figure}

\end{frame}

\begin{frame}
\frametitle{Experiment Setup: Baseline, Assumptions}

Assumption:
\begin{itemize}
  \item env is MDP, the environment is fully-observed
\end{itemize}

Baseline:
\begin{itemize}
  \item 2 types:
  \begin{itemize}
    \item \textbf{naive policy:} the mean return from a naive policy which samples actions from
    a uniform distribution over the valid action space;
    \item \textbf{iLQG~\cite{Todorov2005}}: a planning based solver with full access to
    the underlying physical model and its derivatives.
  \end{itemize}
  \item normalize baseline scores so that
    \begin{itemize}
    \item \textbf{naive policy}: has a mean score of 0
    \item \textbf{iLQG}: has a mean score of 1
  \end{itemize}
\end{itemize}

\end{frame}

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\documentclass[]{article}

\usepackage{pythontex}
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% PythonTeX Bug Fix % % % % % % % % % % % % % % % % % % % % % % % %
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% pytexbug fix for context in customcode.
\makeatletter
\renewenvironment{pythontexcustomcode}[2][begin]{%
	\VerbatimEnvironment
	\Depythontex{env:pythontexcustomcode:om:n}%
	\ifstrequal{#1}{begin}{}{%
		\ifstrequal{#1}{end}{}{\PackageError{\pytx@packagename}%
			{Invalid optional argument for pythontexcustomcode}{}
		}%
	}%
	\xdef\pytx@type{CC:#2:#1}%
	\edef\pytx@cmd{code}%
	% PATCH \def\pytx@context{}%
	\pytx@SetContext
	% END PATCH
	\def\pytx@group{none}%
	\pytx@BeginCodeEnv[none]}%
{\end{VerbatimOut}%
\setcounter{FancyVerbLine}{\value{pytx@FancyVerbLineTemp}}%
\stepcounter{\pytx@counter}%
}%
\makeatother
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %

\usepackage{graphicx}
\usepackage{pgf}
\usepackage{float}
\usepackage{import}
\usepackage[margin=3cm,noheadfoot]{geometry}

%opening
\title{An Example of Astropy and PythonTeX Integration}
\author{Stuart Mumford}

\setpythontexcontext{figurewidth=\the\columnwidth}
\newcommand{\includepgf}[1]{\IfFileExists{#1}{\input{#1}}{}}

\begin{document}
\begin{pythontexcustomcode}{py}
import texfigure
texfigure.configure_latex_plots(pytex)

pytex.formatter = texfigure.repr_latex_formatter

import matplotlib.pyplot as plt
\end{pythontexcustomcode}
\maketitle

\section{Basic PythonTeX}

The basic PythonTeX environments are \verb|\begin{pycode}| and \verb|\py|. \verb|pycode| is a block environment for including Python code, \verb|\py| is a inline output environment, for displaying output.

A simple example of both is to create a variable in a \verb|pycode| environment and then inline it in the text using \verb|\py|:

\begin{pyverbatim}
\begin{pycode}
import astropy.units as u

myquantity = 100 * u.kg/u.m**2
\end{pycode}
\end{pyverbatim}

\begin{pycode}
import astropy.units as u

myquantity = 100 * u.kg/u.m**2
\end{pycode}

You can then include this variable in a sentence using \verb~\py|myquantity|~. The density of Astropy is \py|myquantity|.

\section{Configuring PythonTeX}

It is possible to execute python code in PythonTeX that affects all environments throughout the document. This is commonly used for global imports and configuration.
It can be achieved using the \verb|\begin{pythontexcustomcode}| environment.
This block is especially useful for configuring matplotlib to use settings compatible with PythonTeX.
The matplotlib pgf backend can be configured to use LaTeX for typesetting, and the fonts configured to match that of your LaTeX document.
A default configuration is provided in the texfigure package, it can be used by including the following code in the preamble of your document:

\begin{verbatim}
% Inform Python about the desired figure width figurewidth
\setpythontexcontext{figurewidth=\the\columnwidth}
\newcommand{\includepgf}[1]{\IfFileExists{#1}{\input{#1}}{}}

\begin{document}
% Configure Python + matplotlib
\begin{pythontexcustomcode}{py}
	import texfigure
	texfigure.configure_latex_plots(pytex)
\end{pythontexcustomcode}
\end{verbatim}

This configuration can be used to generate a plot:

\begin{pyblock}
from sunpy.data.sample import AIA_171_IMAGE
import sunpy.map

aiamap = sunpy.map.Map(AIA_171_IMAGE)

fig = plt.figure()
im = aiamap.plot()

fig.tight_layout(pad=3)
fig.savefig('myplot.pgf')
\end{pyblock}

which can be included with the normal LaTeX figure environment:\footnote{The pgf package is required.}

\begin{verbatim}
\begin{figure}[H]
	\includepgf{myplot}
\end{figure}
\end{verbatim}

\begin{figure}[H]
	\includepgf{myplot.pgf}
\end{figure}

\section{Figure Manager}

The figure manager is the primary component of PythonTeX, it makes it easier to
generate and work with figures generated in PythonTeX code blocks from within a
LaTeX document.

A figure manager does a number of things:

\begin{enumerate}
\item Tracks data files in a given directory.
\item Adds python files in a given directory to the Python Path.
\item Saves figures from any know figure type.
\item Tracks figures allowing them to be recalled later.
\item Returns MultiFigure objects containing any number of already tracked
  Figures.
\end{enumerate}

The purpose of a Manager object is to deal with the paths to your data, code and
figures so that you don't have to. It also abstracts away the saving of the
figures for you so that you can pass any known figure object to the Manager and
it will save it in the correct directory and return you a Figure object.

I pulled this out of my Thesis repo this morning!!

\begin{pycode}[chapter2]
from texfigure import Manager
ch2 = Manager(pytex, './')
\end{pycode}

So, as I pointed out yesterday, SunPy is awesome! It makes graphs and everything.

\begin{pycode}[chapter2]
from sunpy.data.sample import AIA_171_IMAGE
import sunpy.map

aiamap = sunpy.map.Map(AIA_171_IMAGE)

fig = plt.figure()
im = aiamap.plot()
aiamap.draw_grid()

lfig = ch2.save_figure('sunpy1', fig)
lfig.caption = r"EUV image of the million degree solar corona, taken with the Atmospheric Imaging Assembly on the NASA Solar Dynamics Observatory."
\end{pycode}

\py[chapter2]|ch2.get_figure('sunpy1')|

You can now reference the figure above, \ref{fig:sunpy1}.

\end{document}

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text:
\section{Introduction}
Neural Networks have become popular choices for complex computation tasks like image recognition \cite{Howard2017MobileNetsEC}, image generation \cite{Goodfellow2014GenerativeAN}, speech processing \cite{Zhao2017RecurrentCN}, and question answering \cite{Seo2017BidirectionalAF} and ranking \cite{nogueira2019multi}. These networks have grown to hundred billions of parameters \cite{Brown2020LanguageMA} which require specialized hardware like clusters of GPUs and FPGAs in order to infer on unseen data.  In the world of Natural Language Processing (NLP) and information retrieval (IR), neural language model's adaptability and performance has made them popular model despite their size. In order to ensure advances in research have practical applications many researcher have explored how large neural models can be compressed. Methods  Recent work has shown that larger networks can learn quicker \cite{Li2020TrainLT}, are more accurate and are more sample efficient \cite{Kaplan2020ScalingLF} which will likely speed up adoption and drive scaling of neural models. As scaling laws will likley drive continue model growth methods of compression like quantization, distillation and pruning, are natural explorations points.  \\
Model distillation, quantization, and pruning have emerged as successful methods to produce smaller networks from the original over-parametized network. In model distillation \cite{Ba2014DoDN} the original network is used to train a smaller network to mimic the behavior of the large network. In model quantization \cite{Han2016DeepCC} models are made smaller by  reducing the numbers of bits that represent each weight. Model Pruning \cite{LeCun1989OptimalBD} has focused on finding sub-networks in the original network by structured and unstructured pruning. In structured pruning, successful sub-networks are found by removing neurons  \cite{Wang2019StructuredPF} or larger network specific structures like attention heads \cite{Voita2019AnalyzingMS}. In unstructured pruning the successful sub-networks are found by setting individual weights to zero \cite{Kwon2019StructuredCB}. With the introduction of transformer \cite{Vaswani2017AttentionIA} a simple compression method became normal, removal of transformer layers. Since methods like BERT rely on 12 and 24 stacked encoder layers it has become common that removal of encoding layers can be used to decrease the model size. Despite a wide exploration of these methods as applied to NLP, few methods have combined all three approaches and to the best of our knowledge there has been no explicit work focused on IR.\\
In our work we explore the intersection of unstructured pruning, model distillation, and layer dropping to generate the optimal compressed models for two IR tasks: Question Answering and Passage Ranking. While previous work on compressing question answering does exist \cite{Sanh2020MovementPA}, the work does not explore removal of layers, nor explore the effect of training length on pruning schedule. Moreover, despite the popularity of deep learning scale IR datasets like MSMARCO Passage Ranking (MPR) \cite{Campos2016MSMA}, no work has explored how compress-able these models are. Using these large scale datasets we find that question answering models can be pruned to 90 percent of their original weights, 50 \% of their layers removed and still keep close to the original performance.
We believe the results of our work could motivate our future research and that of the broader IR community.
The contributions of our work are as follows:
\begin{enumerate}
    \item Our work provides a exploration of the connection between distillation, network pruning, and layer removal for information retrieval tasks.
    \item Our work provides the first application of network pruning for passage retrieval
    \item Our Work provides the first exploration of training time as a factor in network pruning.
\end{enumerate}


text:
\documentclass[]{AVSSimReportMemo}
\usepackage{AVS}

\newcommand{\ModuleName}{inertial3DSpin}
\newcommand{\subject}{Guidance Module to Perform an Inertially Constant Spin}
\newcommand{\status}{Initial Version}
\newcommand{\preparer}{H. Schaub}
\newcommand{\summary}{Generate the reference attitude trajectory for a general 3D inertial spin with a constant inertial angular velocity vector.   }

\begin{document}

\makeCover

%
%	enter the revision documentation here
%	to add more lines, copy the table entry and the \hline, and paste after the current entry.
%
\pagestyle{empty}
{\renewcommand{\arraystretch}{1.1}
\noindent
\begin{longtable}{|p{0.5in}|p{4.5in}|p{1.14in}|}
\hline
{\bfseries Rev}: & {\bfseries Change Description} & {\bfseries By} \\
\hline
Draft & initial copy & H. Schaub \\
v1.1 & fixed typo above Eq.~(4) & H. Schaub \\
v1.2 & updated the documentation now that the module outputs only the reference frame states & H. Schaub \\
\hline

\end{longtable}
}

\newpage
\setcounter{page}{1}
\pagestyle{fancy}

\tableofcontents
~\\ \hrule ~\\

\section{Introduction}
This technical note discusses the guidance mathematics to perform a constant spin about an inertially fixed axis.

\section{Setup}
This module requires that the initial inertial attitude $\bm\sigma_{R/N}$ of the reference frame $\mathcal{R}$ relative to the inertial frame $\mathcal{N}$ be set.  Further, the inertially constant $\mathcal{R}$ frame angular velocity vector $\leftexp{N}{\bm\omega}_{R/N}$ must be defined in inertial frame components.

\section{Reference Frame Propagation}
The MRP differential kinematic equations are
\begin{equation}
	\dot{\bm\sigma}_{R/N} = \frac{1}{4} [B(\bm\sigma_{R/N})] \leftexp{R}{\bm\omega}_{R/N}
\end{equation}
where
\begin{equation}
	[B(\bm\sigma_{R/N})] = (1-\sigma_{R/N}^{2}) [I_{3\times 3}] + 2 [\tilde{\bm\sigma}_{R/N}] + 2 \bm\sigma_{R/N} \bm\sigma_{R/N}^{T}
\end{equation}
and $\sigma_{R/N} = |\bm\sigma_{R/N}|$.

If there inertially fixed reference angular velocity vector is given in inertial frame components, it must be rotated using the appropriate DCM:
\begin{equation}
	\leftexp{R}{\bm\omega_{R/N}} = [RN] \leftexp{N}{\bm\omega_{R/N}}
\end{equation}

\bibliographystyle{unsrt}
\bibliography{references}

\end{document}


text:
\documentclass{article}
\usepackage[mathics]{latexalpha2}
\usepackage{asymptote}

\title{Tests for the \textsf{latexalpha2} package}
\author{Yi Liu}

\setlength\parindent{0pt}

\begin{document}
\maketitle

\subsection*{Test 1}
Input:
\begin{verbatim}
    $$ (x+y)^3 = \wolfram{Expand[(x+y)^3]}. $$
\end{verbatim}

Output:
$$ (x+y)^3 = \wolfram{Expand[(x+y)^3]}. $$

\subsection*{Test 2}
Input:
\begin{verbatim}
    $$ \int\frac{1}{x^3+1}\,dx = \wolfram{Integrate[1/(x^3+1),x]}. $$
\end{verbatim}

Output:
$$ \int\frac{1}{x^3+1}\,dx = \wolfram{Integrate[1/(x^3+1),x]}. $$

\subsection*{Test 3}
Input:
\begin{verbatim}
    \wolframsolve{a x^2+b x+c==0}{x}
\end{verbatim}

Output:
\wolframsolve{a x^2+b x+c==0}{x}

\subsection*{Test 4}
Input:
\begin{verbatim}
    \wolframdsolve{y'[x]+y[x]==a Sin[x]}{y[x]}{x}
\end{verbatim}

Output:
\wolframdsolve{y'[x]+y[x]==a Sin[x]}{y[x]}{x}

\subsection*{Test 5}
Input:
\begin{verbatim}
    \begin{figure}
        \wolframgraphics{Plot[Sin[x]+x,{x,0,10}]}{example}
        \input{example.asy}
        \centering
        \caption{Plot of $f(x)=\sin(x)+x$}
    \end{figure}
\end{verbatim}

Output: Figure 1

\begin{figure}
    \wolframgraphics{Plot[Sin[x]+x,{x,0,10}]}{example}
    \input{example.asy}
    \centering
    \caption{Plot of $f(x)=\sin(x)+x$}
\end{figure}

\end{document}


text:
% !TEX program = latexmk
\documentclass[openany, twoside, b5paper]{book}

\usepackage[b5paper]{geometry}
\usepackage{polski}
\usepackage[polish]{babel}
\brokenpenalty=1000
\clubpenalty=1000
\widowpenalty=1000

\usepackage{microtype} % Improves spacing
\usepackage{graphicx}
\graphicspath{ {../images/} }

\usepackage{fancyhdr}
\fancyhead{}
\fancyhead[L]{\textsf{\rightmark}} % Top left header
\fancyhead[R]{\textsf{\leftmark}} % Top right header
\renewcommand{\headrulewidth}{1.4pt} % Rule under the header
\fancyfoot{}
\fancyfoot[LO,RE]{\textbf{\textsf{\thepage}}} % Bottom center footer
\renewcommand{\footrulewidth}{1.4pt} % Rule under the footer
\pagestyle{fancy} % Use the custom headers and footers throughout the document

\usepackage{libertine}

\usepackage{titlesec}
\newcommand{\sectionbreak}{\clearpage}

\usepackage{multicol}
\usepackage{dictionary}
\usepackage{enumitem}
\usepackage{setspace}
\usepackage{booktabs}
\usepackage{hyperref}

\usepackage{leipzig}
\usepackage{gb4e}\noautomath
\usepackage{typgloss}

%\setcounter{secnumdepth}{2}
%\setcounter{tocdepth}{2}

\makeglossaries
\newleipzig{an}{an}{animated}
\newleipzig{nan}{nan}{non-animated}
\newleipzig{hort}{hort}{hortative}
\newleipzig{exh}{exh}{exhortative}
\newleipzig{chr}{chr}{cohortative}
\newleipzig{opt}{opt}{optative}
\newleipzig{for}{for}{formal}
\newleipzig{ven}{ven}{venitive}
\newleipzig{dim}{dim}{diminutive}
\newleipzig{ord}{ord}{ordinal numeral}
\newleipzig{supl}{supl}{superlative}

\begin{document}

\author{Koolder mal Erlehirni}
\title{Mały słownik androyasańsko-polski}

\frontmatter

\begin{titlepage} % Suppresses displaying the page number on the title page and the subsequent page counts as page 1
    \newcommand*{\plogo}{\fbox{$\mathcal{AND}$}} % Generic dummy publisher logo

	\raggedleft % Right align the title page

	\rule{1pt}{\textheight} % Vertical line
	\hspace{0.05\textwidth} % Whitespace between the vertical line and title page text
	\parbox[b]{0.75\textwidth}{ % Paragraph box for holding the title page text, adjust the width to move the title page left or right on the page

        {\Huge\bfseries Mały słownik\\ androyasańsko-polski}
        {\large\textit{Oraz polsko-androyasański}}\\[4\baselineskip]
		{\Large\textsc{koolder mal erlehirni}} % Author name, lower case for consistent small caps

        \bigskip

        \includegraphics[width=2cm]{androyas-flag}
        \includegraphics[width=2cm]{pl-flag}

        \vspace{0.5\textheight} % Whitespace between the title block and the publisher

		{\noindent Royal Androyas Publishing~~\plogo}\\[\baselineskip] % Publisher and logo
	}

\end{titlepage}

\clearpage
\input{colophon.tex}

\tableofcontents

\mainmatter
\chapter{Wprowadzenie}
\input{introduction.tex}
\input{history.tex}
\input{description.tex}

\chapter{Słownik androyasańsko-polski}
\input{ap.tex}

\chapter{Słownik polsko-androyasański}
\input{pa.tex}

\chapter{Dodatkowe przykłady tłumaczeń}
\input{examples.tex}

\backmatter
% bibliography, glossary and index would go here.

\end{document}

max_stars_repo_name: ktos/andro

max_issues_repo_name: ktos/andro

max_forks_repo_name: ktos/andro


text:
\subsection{Value iteration}

\subsubsection{Value iteration method}

Doesn't directly calculuate policy. Doesn't require inverting matrix.

max_stars_repo_name: adamdboult/nodeHomePage

max_issues_repo_name: adamdboult/nodeHomePage

max_forks_repo_name: adamdboult/nodeHomePage


text:
\documentclass{article}
\usepackage[utf8]{inputenc}

\title{Example of an Article Class Document}
\author{John Smith}
\date{August 2019}

\usepackage{natbib}
\usepackage{graphicx}

\begin{document}

    \maketitle

    \section{Introduction}
    There is a theory which states that if ever anyone discovers exactly what the
    Universe is for and why it is here, it will instantly disappear and be replaced
    by something even more bizarre and inexplicable. There is another theory which
    states that this has already happened.

    \begin{figure}[h!]
        \centering
        \includegraphics[scale=1.7]{universe}
        \caption{The Universe}
        \label{fig:universe}
    \end{figure}

    \section{Conclusion}
    ``I always thought something was fundamentally wrong with the universe''
    \citep{adams1995hitchhiker}

    \bibliographystyle{plain}
    \bibliography{references}
\end{document}

max_stars_repo_name: ericnchen/aws-lambda-latex

max_issues_repo_name: ericnchen/aws-lambda-latex

max_forks_repo_name: ericnchen/aws-lambda-latex


text:
\chapter{The SEACO Database Format} \label{appx:seaco}

The following code segment reads a SEACO database.

\begin{verbatim}
C   --Open the database file

      NDB = 11
      OPEN (UNIT=NDB, ..., STATUS='OLD', FORM='UNFORMATTED')

C   --Read the header information

      READ (NDB) TITLE, CNAME, CDATE, CTIME, MNAME, MDATE, MTIME
C      --TITLE - the title of the database (CHARACTER*80)
C      --CNAME - the program which created the database (CHARACTER*8)
C      --CDATE, CTIME - the date (DD-MM-YY) and time (HH:MM:SS)
C      --   the database was created (both CHARACTER*8)
C      --MNAME - the program which last modified the database
C      --   (CHARACTER*8)
C      --MDATE, MTIME - the date (DD-MM-YY) and time (HH:MM:SS)
C      --   the database was last modified (both CHARACTER*8)

      READ (NDB) NDIM, NUMNP, NUMEL, NLINK, NUMMAT,
     &   NVARNP, NVAREL, NVARGL, IBLKNV, IBLKEV, IPACK
C      --NDIM - the number of coordinates per node
C      --NUMNP - the number of nodes
C      --NUMEL - the number of elements
C      --NLINK - the number of nodes per element
C      --NUMMAT - the number of materials
C      --NVARNP - the number of nodal variables
C      --NVAREL - the number of element variables
C      --NVARGL - the number of global variables
C      --IBLKNV - the nodal variable blocking flag, must be 0
C      --IBLKEV - the element variable blocking flag, must be 0
C      --IPACK  - the packed data flag, must be 1
\end{verbatim}
\newpage
\begin{verbatim}
C   --Read the coordinate and variable names

      IF (NDIM .GT. 0) THEN
         READ (NDB) (NAMECO(I), I=1,NDIM)
C         --NAMECO - the names of the coordinates (CHARACTER*8)
      END IF

      IF (NVARNP .GT. 0) THEN
         READ (NDB) (NAMENV(I), I=1,NVARNP)
C         --NAMENV - the names of the nodal variables (CHARACTER*8)
      END IF
      IF (NVAREL .GT. 0) THEN
         READ (NDB) (NAMEEV(I), I=1,NVAREL)
C         --NAMEEV - the names of the element variables (CHARACTER*8)
      END IF
      IF (NVARGL .GT. 0) THEN
         READ (NDB) (NAMEGV(I), I=1,NVARGL)
C         --NAMEGV - the names of the global variables (CHARACTER*8)
      END IF

C   --Read the coordinate, connectivity, and material arrays

      IF (NDIM .GT. 0) THEN
         READ (NDB) ((CORD(I,INP), INP=1,NUMNP), I=1,NDIM)
C         --CORD - the coordinates for each node
      END IF

      IF (NUMEL .GT. 0) THEN
         DO 100 IEL = 1, NUMEL
            READ (NDB) (LINK(I,IEL), I=1,NLINK)
C            --LINK - the connectivity array (nodes of each element)
  100    CONTINUE
      END IF

      IF (NUMMAT .GT. 1) THEN
         READ (NDB) (MAT(IEL), IEL=1,NUMEL)
C         --MAT - the material for each element
      END IF
\end{verbatim}
\newpage
\begin{verbatim}
C   --Read the time steps

  110 CONTINUE

         READ (NDB, END=900) TIME
C         --TIME - the current time step time

         IF (NVARNP .GT. 0) THEN
            DO 120 NV = 1, NVARNP
               READ (NDB) (VARNP(NV,INP), INP=1,NUMNP)
C               --VARNP - the nodal variables at each node
C               --   for the current time step
  120       CONTINUE
         END IF

         IF (NVAREL .GT. 0) THEN
            DO 130 NV = 1, NVAREL
               READ (NDB) (VAREL(NV,IEL), IEL=1,NUMEL)
C               --VAREL - the element variables at each element
C               --   for the current time step
  130       CONTINUE
         END IF

         IF (NVARGL .GT. 0) THEN
            READ (NDB) (GLOBL(NV), NV=1,NVARGL)
C            --GLOBL - the global variables for the current time step
         END IF

C      --Handle time step data
         ...

      GOTO 110

  900 CONTINUE
C   --Handle end of file on database
      ...
\end{verbatim}

max_stars_repo_name: jschueller/seacas

max_issues_repo_name: jschueller/seacas

max_forks_repo_name: jschueller/seacas


text:
\section{Conclusions} \label{sec:Conclusion}

Although not with as many iterations as originally planned, the main goal of
the project has been achieved -- that is, to deliver a functional, simple
expenditure analyser. The techniques used to develop it should allow for the
code to be easily extended and refactored as appropriate, without requiring too
much of a rewrite.

The Scala language's multi-paradigm features allowed for most of the variables
in the code to be made immutable where appropriate, while still ensuring that
the mutable aspects inherited from other languages worked as expected. The
functional aspects of the language made it easy for the developer to work well
with the immutability, and the flexibility of the data structures also allowed
for the focus to be placed on writing good abstractions, while trusting that
the language's own implementation of its data structures, especially its
collections (\cite[][Ch.~24]{odersky2016scala}), would maximise efficiency (as
much as a possible).

Regarding the author's perception of \emph{software development best
practices}, the advantages of following an iterative cycle were definitely
visible, especially those of trying to model the system before beginning the
implementation using an appropriate language (in this case, UML). The models in
chapter \ref{sec:AnalysisAndDesign} gave very good visibility of possible
`pitfalls', such as too much responsibility being assigned to an entity, or
which level of abstraction should be used for each domain aspect being
modelled. This allowed for them to be redesigned more easily than would be
possible if no planning had taken place.

The benefits of analysis and design patterns were also noticed, and due to
these techniques and the SOLID principles (which, although not applied
perfectly, seem to have been used up to a reasonable extent), the code can now
be extended more easily, and more features can be added with minimum impact
being made to existing functionality. This shows, in the author's opinion, the
value of these techniques for software development, and why they are considered
best practices.


text:
\chapter{Strategy development manual}\label{appendix:devman}

To create a new strategy, a Java class must be developed. The Java class must
be public and implement the \code{app.library.ExecutableStrategy} interface and
it must be placed in a file called \code{<ClassName>.java}. The class must be
placed in the default package.

If you want to use Eclipse IDE to develop a strategy, just create a new project
and add the \code{Server} project as a dependency. This will allow the IDE to
provide functionalities such as code completion and error checking. Place all
the strategies you develop in the default package.

The base skeleton for a strategy can be found in \code{strategies/Skel.java}.
The content is shown in~\lstref{lst:strategyskel}.

Various examples of strategies can be found in the \code{strategies} folder.

\lstinputlisting[language=java, style=javacode, label={lst:strategyskel},
caption={Base skeleton for a strategy.}]{Skel.java}


text:
%!TEX root=../labNotes.tex

\subsection*{09 September 2014}

\begin{enumerate}
	\item MA104 cell split --- Passage 60, from Passage 59, Flask A
		\begin{enumerate}
			\item Aspirated cell culture medium
			\item Rinsed cells in $10$mL 1x PBS; aspirated PBS
			\item Rinsed cells in $2$mL $0.05$\% trypsin; aspirated trypsin
			\item Bathed cells in $2$mL $0.05$\% trypsin
			\item Incubated cells at $37^{\circ}$C until all cells detached from flask
			\item Added $18$mL complete M199 to flask
			\item Added to $3$ T150 flasks $15$mL complete M199/$10$mL cell mix; $20$mL complete M199/$5$mL cell mix; and $20$mL complete M199/$5$mL cell mix. Respectively, flasks A, B, and C
			\item Gently shook flasks to distribute cells evenly
			\item Incubated at $37^{\circ}$C
		\end{enumerate}
\end{enumerate}

\subsection*{12 September 2014}

\begin{enumerate}
	\item Plated MA104 cells --- Passage 60, Flask A
		\begin{enumerate}
			\item Aspirated cell culture medium
			\item Rinsed cells in $10$mL 1x PBS; aspirated PBS
			\item Rinsed cells in $5$mL $0.05$\% trypsin; aspirated trypsin
			\item Bathed cells in $5$mL $0.05$\% trypsin
			\item Incubated cells at $37^{\circ}$C until all cells detached from flask
			\item Added $15$mL complete M199 to flask
			\item Took cell count by combining $10\mu$L cell mixture with $10\mu$L trypan blue:

				\begin{align*}
				\text{[cells]} &= \frac{3.91\e{5}\text{ cells}}{1\text{mL}} \\
				\frac{\text{cells}}{\text{flask}} &= \frac{3.91\e{5}\text{ cells}}{1\text{mL}} \cdot 20\text{mL} &= \frac{7.62\e{6}\text{ cells}}{20\text{mL}}\\
				\frac{\text{cells}}{10\text{mL cell mix}} &= \frac{7.62\e{6}\text{ cells}}{20\text{mL}}\cdot \frac{1}{2} &= \frac{3.81\e{6}\text{ cells}}{10\text{mL}}\\
				\frac{\text{cells}}{75\text{mL vial}} &= \frac{3.81\e{6}\text{ cells}}{75\text{mL}} &= \frac{5.08\e{4}\text{ cells}}{\text{mL}}\\
				\frac{\text{cells}}{3\text{mL well}} &= \frac{5.08\e{4}\text{ cells}}{\text{mL}} \cdot 3\text{mL} &= \frac{1.52\e{5}\text{ cells}}{\text{well}}\\
				\end{align*}
			\item Added $65$mL complete M199 and $10$mL cell mixture to $125$mL conical vial for final volume of $75$mL
			\item Transferred $3$mL solution to each well of 4 6-well plates
			\item Spread cells evenly by shaking
			\item Incubated at $37^{\circ}$C
		\end{enumerate}
\end{enumerate}

max_stars_repo_name: faulconbridge/Graduate-Work

max_issues_repo_name: faulconbridge/Graduate-Work

max_forks_repo_name: faulconbridge/Graduate-Work


text:
% -----------------------------------------------------------------------------
%                                     HEADER
% -----------------------------------------------------------------------------
\documentclass[a4paper, 10pt]{article}
\usepackage{jheppub}
\usepackage[T1]{fontenc}
\usepackage{colortbl,xcolor,float}
\definecolor{orange}{rgb}{1,0.5,0}
% -----------------------------------------------------------------------------
%                                   COVER PAGE
% -----------------------------------------------------------------------------
\title{{\includegraphics[scale=.4]{logo.eps}}\ The LaTeX report}

\author{Generated by elijahsheridan on 22 March 2020, 23:37:48}

\abstract{
  This report has been generated automatically
  by {\sc MadAnalysis} 5.\\$~$\\
  Please cite:\\
  \begin{quote}
    \textbf{E.~Conte, B.~Fuks and G.~Serret},\\
    \textit{MadAnalysis 5, A User-Friendly
    Framework for Collider Phenomenology},\\
    Comput. Phys. Commun. {\bf 184} (2013) 222-256,\\
    arXiv:1206.1599 [hep-ph].\\
  \end{quote}
  To contact us:\\
  \begin{quote}
    \textbf{http://madanalysis.irmp.ucl.ac.be}\\
    \textbf{ma5team@iphc.cnrs.fr}\\
  \end{quote}
}

% -----------------------------------------------------------------------------
%                                 BEGIN DOCUMENT
% -----------------------------------------------------------------------------
\begin{document}
\maketitle
\flushbottom

% -----------------------------------------------------------------------------
%                                 SECTION Setup
% -----------------------------------------------------------------------------
\newpage
\section{ Setup}

\subsection{ Command history}

\texttt{ma5>\# set directory where running "./\-bin/\-ma5"; set lumi; define the signal significance\\
}
\texttt{ }\texttt{ }\texttt{ma5>set main.currentdir = /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data \# need to change this directory path --> exit and type "pwd" to get the path\\
}
\texttt{ }\texttt{ }\texttt{ma5>set main.lumi = 150.0\\
}
\texttt{ }\texttt{ }\texttt{ma5>set main.SBratio = 'S/\-sqrt(S+B)'\\
}
\texttt{ }\texttt{ }\texttt{ma5>\# import samples --> change the path to the LHE file\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-axion\_signal/\-axion\_signal\_gurrola\_cuts\_1MeV.lhe.gz as signal\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_0\_100\_merged.lhe.gz as bg\_vbf\_0\_100\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_100\_200\_merged.lhe.gz as bg\_vbf\_100\_200\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_200\_400\_merged.lhe.gz as bg\_vbf\_200\_400\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_400\_600\_merged.lhe.gz as bg\_vbf\_400\_600\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_600\_800\_merged.lhe.gz as bg\_vbf\_600\_800\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_800\_1200\_merged.lhe.gz as bg\_vbf\_800\_1200\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_1200\_1600\_merged.lhe.gz as bg\_vbf\_1200\_1600\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_1600\_inf\_merged.lhe.gz as bg\_vbf\_1600\_inf\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_0\_100\_merged.lhe.gz as bg\_dip\_0\_100\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_100\_200\_merged.lhe.gz as bg\_dip\_100\_200\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_200\_400\_merged.lhe.gz as bg\_dip\_200\_400\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_400\_600\_merged.lhe.gz as bg\_dip\_400\_600\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_600\_800\_merged.lhe.gz as bg\_dip\_600\_800\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_800\_1200\_merged.lhe.gz as bg\_dip\_800\_1200\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_1200\_1600\_merged.lhe.gz as bg\_dip\_1200\_1600\\
}
\texttt{ }\texttt{ }\texttt{ma5>import /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_1600\_inf\_merged.lhe.gz as bg\_dip\_1600\_inf\\
}
\texttt{ }\texttt{ }\texttt{ma5>\# define bg and signal samples\\
}
\texttt{ }\texttt{ }\texttt{ma5>set signal.type = signal\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_0\_100.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_100\_200.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_200\_400.type  = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_400\_600.type  = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_600\_800.type  = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_800\_1200.type  = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_1200\_1600.type  = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_vbf\_1600\_inf.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_0\_100.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_100\_200.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_200\_400.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_400\_600.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_600\_800.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_800\_1200.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_1200\_1600.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>set bg\_dip\_1600\_inf.type = background\\
}
\texttt{ }\texttt{ }\texttt{ma5>\# define weights for the samples\\
}
\texttt{ }\texttt{ }\texttt{ma5>\#set sample\_1.weight = 1\\
}
\texttt{ }\texttt{ }\texttt{ma5>\#set sample\_2.weight = 1\\
}
\texttt{ }\texttt{ }\texttt{ma5>\# a jet can be from a light quark or b quark\\
}
\texttt{ }\texttt{ }\texttt{ma5>define jets = j\\
}
\texttt{ }\texttt{ }\texttt{ma5>define e = e+ e-\\
}
\texttt{ }\texttt{ }\texttt{ma5>define mu = mu+ mu-\\
}
\texttt{ }\texttt{ }\texttt{ma5>define ta = ta+ ta-\\
}
\texttt{ }\texttt{ }\texttt{ma5>define lept = e mu ta\\
}
\texttt{ }\texttt{ }\texttt{ma5>\# reduce contribution from V+Zp ==> jj+Zp\\
}
\texttt{ }\texttt{ }\texttt{ma5>select sdETA(jets[1] jets[2]) > 2.6 and M(jets[1] jets[2]) > 1250\\
}
\texttt{ }\texttt{ }\texttt{ma5>submit lum\_probe\_150\_loose\\
}
\texttt{ }\texttt{ }\subsection{ Configuration}

\begin{itemize}
  \item MadAnalysis version 1.6.33 (2017/\-11/\-20).
   \item Histograms given for an integrated luminosity of \textcolor{blue}{150.0}\textcolor{blue}{ fb}$^{\textcolor{blue}{-1}}$\textcolor{blue}{.}
\textcolor{blue}{}
\end{itemize}
% -----------------------------------------------------------------------------
%                                SECTION Datasets
% -----------------------------------------------------------------------------
\newpage
\section{ Datasets}

\subsection{ signal}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{signal}  events.
   \item Generated events: \textcolor{blue}{1000000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{15352}\textcolor{blue}{ +/\-- }\textcolor{blue}{5 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.015 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-axion\_signal/\-axion\_signal\_gurrola\_cuts\_1MeV.lhe.gz}& {\cellcolor{white}          1000000}& {\cellcolor{white}          0.102 @ 0.028\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_0\_100}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1000000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{45563}\textcolor{blue}{ +/\-- }\textcolor{blue}{87 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.046 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_0\_100\_merged.lhe.gz}& {\cellcolor{white}          1000000}& {\cellcolor{white}          0.304 @ 0.19\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_100\_200}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{965662 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{36357}\textcolor{blue}{ +/\-- }\textcolor{blue}{63 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.038 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_100\_200\_merged.lhe.gz}& {\cellcolor{white}          965662}& {\cellcolor{white}          0.242 @ 0.17\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_200\_400}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{984165 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{20299}\textcolor{blue}{ +/\-- }\textcolor{blue}{41 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.021 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_200\_400\_merged.lhe.gz}& {\cellcolor{white}          984165}& {\cellcolor{white}          0.135 @ 0.2\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_400\_600}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1000000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{3700}\textcolor{blue}{ +/\-- }\textcolor{blue}{6 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.0037 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_400\_600\_merged.lhe.gz}& {\cellcolor{white}          1000000}& {\cellcolor{white}          0.0247 @ 0.14\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_600\_800}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1000000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{945}\textcolor{blue}{ +/\-- }\textcolor{blue}{2 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.00094 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_600\_800\_merged.lhe.gz}& {\cellcolor{white}          1000000}& {\cellcolor{white}          0.0063 @ 0.13\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_800\_1200}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{400839 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{430}\textcolor{blue}{ +/\-- }\textcolor{blue}{1 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.0011 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_800\_1200\_merged.lhe.gz}& {\cellcolor{white}          400839}& {\cellcolor{white}          0.00287 @ 0.16\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_1200\_1600}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{953803 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{77}\textcolor{blue}{ +/\-- }\textcolor{blue}{1 }  events.
   \item Ratio (event weight): \textcolor{blue}{8.1e-05 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_1200\_1600\_merged.lhe.gz}& {\cellcolor{white}          953803}& {\cellcolor{white}          0.000515 @ 0.16\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_vbf\_1600\_inf}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{270148 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{28}\textcolor{blue}{ +/\-- }\textcolor{blue}{1 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.0001 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-vbf\_diphoton\_background\_data/\-merged\_lhe/\-vbf\_diphoton\_background\_ht\_1600\_inf\_merged.lhe.gz}& {\cellcolor{white}          270148}& {\cellcolor{white}          0.000191 @ 0.11\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_0\_100}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{10165677}\textcolor{blue}{ +/\-- }\textcolor{blue}{17300 }  events.
   \item\textcolor{red}{Ratio (event weight): }\textcolor{red}{9.8 }\textcolor{red}{ - warning: please generate more events (weight larger than 1)!}
\textcolor{red}{}
\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_0\_100\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          67.8 @ 0.17\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_100\_200}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{4107610}\textcolor{blue}{ +/\-- }\textcolor{blue}{5727 }  events.
   \item\textcolor{red}{Ratio (event weight): }\textcolor{red}{3.9 }\textcolor{red}{ - warning: please generate more events (weight larger than 1)!}
\textcolor{red}{}
\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_100\_200\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          27.4 @ 0.14\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_200\_400}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{898308}\textcolor{blue}{ +/\-- }\textcolor{blue}{1552 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.86 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_200\_400\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          5.99 @ 0.17\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_400\_600}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{107995}\textcolor{blue}{ +/\-- }\textcolor{blue}{196 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.1 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_400\_600\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          0.72 @ 0.18\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_600\_800}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{662009 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{25028}\textcolor{blue}{ +/\-- }\textcolor{blue}{104 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.038 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_600\_800\_merged.lhe.gz}& {\cellcolor{white}          662009}& {\cellcolor{white}          0.167 @ 0.41\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_800\_1200}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{11033}\textcolor{blue}{ +/\-- }\textcolor{blue}{19 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.011 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_800\_1200\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          0.0736 @ 0.17\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_1200\_1600}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{337115 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{1925}\textcolor{blue}{ +/\-- }\textcolor{blue}{10 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.0057 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_1200\_1600\_merged.lhe.gz}& {\cellcolor{white}          337115}& {\cellcolor{white}          0.0128 @ 0.51\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\subsection{ bg\_dip\_1600\_inf}

\begin{itemize}
  \item Samples stored in the directory: \textcolor{blue}{/\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-optimization/\-dEta\_mmjj\_cuts\_plots} .
   \item Sample consisting of: \textcolor{blue}{background}  events.
   \item Generated events: \textcolor{blue}{1040000 }  events.
   \item Normalization to the luminosity: \textcolor{blue}{704}\textcolor{blue}{ +/\-- }\textcolor{blue}{2 }  events.
   \item Ratio (event weight): \textcolor{blue}{0.00068 } .

\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{55.0mm}|m{25.0mm}|m{30.0mm}|m{30.0mm}|}
      \hline
      {\cellcolor{yellow}         Path to the event file}& {\cellcolor{yellow}         Nr. of events}& {\cellcolor{yellow}         Cross section (pb)}& {\cellcolor{yellow}         Negative wgts (\%)}\\
      \hline
      {\cellcolor{white}          /\-Users/\-elijahsheridan/\-MG5\_aMC\_v2\_6\_5/\-axion\_data/\-diphoton\_double\_isr\_background\_data/\-merged\_lhe/\-diphoton\_double\_isr\_background\_ht\_1600\_inf\_merged.lhe.gz}& {\cellcolor{white}          1040000}& {\cellcolor{white}          0.00469 @ 0.15\%}& {\cellcolor{white}          0.0}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

% -----------------------------------------------------------------------------
%                            SECTION Histos and cuts
% -----------------------------------------------------------------------------
\newpage
\section{ Histos and cuts}

\subsection{Cut 1}

\textbf{* Cut: select sdETA ( jets[1] jets[2] ) > 2.6 and M ( jets[1] jets[2] ) > 1250.0}\\
   \begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{20.0mm}|m{27.0mm}|m{27.0mm}|m{33.0mm}|m{32.0mm}|}
      \hline
      {\cellcolor{yellow}         Dataset}& {\cellcolor{yellow}         Events kept:
          K}& {\cellcolor{yellow}         Rejected events:
          R}& {\cellcolor{yellow}         Efficiency:
          K /\- (K + R)}& {\cellcolor{yellow}         Cumul. efficiency:
          K /\- Initial}\\
      \hline
      {\cellcolor{white}         signal}& {\cellcolor{white}         3200.2 +/\-- 50.3}& {\cellcolor{white}         12152.6 +/\-- 50.4}& {\cellcolor{white}         0.20845 +/\-- 0.00328}& {\cellcolor{white}         0.20845 +/\-- 0.00328}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_0\_100}& {\cellcolor{white}         385.8 +/\-- 19.6}& {\cellcolor{white}         45177.9 +/\-- 88.1}& {\cellcolor{white}         0.008467 +/\-- 0.000429}& {\cellcolor{white}         0.008467 +/\-- 0.000429}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_100\_200}& {\cellcolor{white}         1791.8 +/\-- 41.4}& {\cellcolor{white}         34565.7 +/\-- 72.1}& {\cellcolor{white}         0.04928 +/\-- 0.00114}& {\cellcolor{white}         0.04928 +/\-- 0.00114}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_200\_400}& {\cellcolor{white}         2151.4 +/\-- 44.1}& {\cellcolor{white}         18148.4 +/\-- 57.1}& {\cellcolor{white}         0.10598 +/\-- 0.00216}& {\cellcolor{white}         0.10598 +/\-- 0.00216}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_400\_600}& {\cellcolor{white}         654.3 +/\-- 23.2}& {\cellcolor{white}         3046.4 +/\-- 23.6}& {\cellcolor{white}         0.17681 +/\-- 0.00627}& {\cellcolor{white}         0.17681 +/\-- 0.00627}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_600\_800}& {\cellcolor{white}         208.7 +/\-- 12.8}& {\cellcolor{white}         736.6 +/\-- 12.8}& {\cellcolor{white}         0.2208 +/\-- 0.0135}& {\cellcolor{white}         0.2208 +/\-- 0.0135}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_800\_1200}& {\cellcolor{white}         75.57 +/\-- 7.89}& {\cellcolor{white}         354.79 +/\-- 7.91}& {\cellcolor{white}         0.1756 +/\-- 0.0183}& {\cellcolor{white}         0.1756 +/\-- 0.0183}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_1200\_1600}& {\cellcolor{white}         8.41 +/\-- 2.74}& {\cellcolor{white}         68.83 +/\-- 2.74}& {\cellcolor{white}         0.1088 +/\-- 0.0354}& {\cellcolor{white}         0.1088 +/\-- 0.0354}\\
      \hline
      {\cellcolor{white}         bg\_vbf\_1600\_inf}& {\cellcolor{white}         1.48 +/\-- 1.18}& {\cellcolor{white}         27.24 +/\-- 1.19}& {\cellcolor{white}         0.0516 +/\-- 0.0413}& {\cellcolor{white}         0.0516 +/\-- 0.0413}\\
      \hline
      {\cellcolor{white}         bg\_dip\_0\_100}& {\cellcolor{white}         439.8 +/\-- 21.0}& {\cellcolor{white}         10165237 +/\-- 17298}& {\cellcolor{white}         4.33e-05 +/\-- 2.06e-06}& {\cellcolor{white}         4.33e-05 +/\-- 2.06e-06}\\
      \hline
      {\cellcolor{white}         bg\_dip\_100\_200}& {\cellcolor{white}         1860.2 +/\-- 43.2}& {\cellcolor{white}         4105750 +/\-- 5724}& {\cellcolor{white}         4.53e-04 +/\-- 1.05e-05}& {\cellcolor{white}         4.53e-04 +/\-- 1.05e-05}\\
      \hline
      {\cellcolor{white}         bg\_dip\_200\_400}& {\cellcolor{white}         3053.3 +/\-- 55.4}& {\cellcolor{white}         895254 +/\-- 1547}& {\cellcolor{white}         3.40e-03 +/\-- 6.14e-05}& {\cellcolor{white}         3.40e-03 +/\-- 6.14e-05}\\
      \hline
      {\cellcolor{white}         bg\_dip\_400\_600}& {\cellcolor{white}         1993.0 +/\-- 44.4}& {\cellcolor{white}         106002 +/\-- 197}& {\cellcolor{white}         0.01845 +/\-- 0.00041}& {\cellcolor{white}         0.01845 +/\-- 0.00041}\\
      \hline
      {\cellcolor{white}         bg\_dip\_600\_800}& {\cellcolor{white}         989.3 +/\-- 31.1}& {\cellcolor{white}         24039 +/\-- 104}& {\cellcolor{white}         0.03953 +/\-- 0.00123}& {\cellcolor{white}         0.03953 +/\-- 0.00123}\\
      \hline
      {\cellcolor{white}         bg\_dip\_800\_1200}& {\cellcolor{white}         362.3 +/\-- 18.7}& {\cellcolor{white}         10671.4 +/\-- 26.2}& {\cellcolor{white}         0.0328 +/\-- 0.0017}& {\cellcolor{white}         0.0328 +/\-- 0.0017}\\
      \hline
      {\cellcolor{white}         bg\_dip\_1200\_1600}& {\cellcolor{white}         40.5 +/\-- 6.3}& {\cellcolor{white}         1885.2 +/\-- 11.5}& {\cellcolor{white}         0.02101 +/\-- 0.00327}& {\cellcolor{white}         0.02101 +/\-- 0.00327}\\
      \hline
      {\cellcolor{white}         bg\_dip\_1600\_inf}& {\cellcolor{white}         7.60 +/\-- 2.74}& {\cellcolor{white}         696.58 +/\-- 2.93}& {\cellcolor{white}         0.01080 +/\-- 0.00389}& {\cellcolor{white}         0.01080 +/\-- 0.00389}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

   % -----------------------------------------------------------------------------
%                                SECTION Summary
% -----------------------------------------------------------------------------
\newpage
\section{ Summary}

\subsection{Cut-flow charts}

\begin{itemize}
  \item How to compare signal (S) and background (B): \textcolor{blue}{S/\-sqrt(S+B)} .
   \item Object definition selections are indicated in cyan.  \item Reject and select are indicated by 'REJ' and 'SEL' respectively
\end{itemize}
\begin{table}[H]
  \begin{center}
    \begin{tabular}{|m{36.0mm}|m{36.0mm}|m{36.0mm}|m{33.0mm}|}
      \hline
      {\cellcolor{yellow}        Cuts}& {\cellcolor{yellow}         Signal (S)}& {\cellcolor{yellow}         Background (B)}& {\cellcolor{yellow}         S vs B}\\
      \hline
      {\cellcolor{white}         Initial (no cut)}& {\cellcolor{white}         15352.82 +/\-- 4.23}& {\cellcolor{white}         15425686 +/\-- 18290}& {\cellcolor{white}         3.90706 +/\-- 0.00255}\\
      \hline
      {\cellcolor{white} SEL: sdETA ( jets[1] jets[2] ) > 2.6 and M ( jets[}& {\cellcolor{white}         3200.2 +/\-- 50.3}& {\cellcolor{white}         14023 +/\-- 116}& {\cellcolor{white}         24.385 +/\-- 0.358}\\
\hline
    \end{tabular}
  \end{center}
\end{table}

\end{document}