text:
Eric Norris
Eric was born in 1965 and is the youngest son of Chuck Norris.
He started his career as a Stunt Man at a young age, then later graduated to Stunt Coordinating, 2nd Unit Directing and eventually Directing Main Unit.
Eric is also an accomplished Racing driver and actually won the 2002 Nascar Grand National West Series Championship.
Dylan Dog: Dead of Night
The Cutter
The Three Stooges (2nd unit)
The Mercy Man
I Love You Phillip Morris
Get Smart's Bruce and Lloyd Out of Control (Video)
Frostbite (Video)
Top Dog (Second unit)
Walker Texas Ranger 3: Deadly Reunion
The Mummy Lives
War Dogs (stunt driver/stunts performer)
Mike and Dave Need Wedding Dates
Independence Day: Resurgence
The Gamble
Dumb and Dumber To (stunt driver/stunt performer)
Freedom (stunt double/stunt performer)
The Hangover Part III
Gangster Squad
Movie 43 (stunt double)
Bullet to the Head (stunt driver/stunt performer)
Total Recall (stunts)
The Amazing Spider-Man (stunts - uncredited)
The Green Hornet (stunts - uncredited)
Due Date (stunt driver - uncredited) / (stunts)
The Town (stunts)
Machete (stunts)
Salt (stunt driver)
Krews (stunts)
Old Dogs (utility stunts)
Angels & Demons (stunt performer - uncredited)
12 Rounds (stunts)
The Yellow Handkerchief (stunts)
Cloverfield (utility stunts)
The Great Debaters (stunts)
Rush Hour 3 (stunts)
Redline (stunt driver)
Pride (stunts)
Premonition (stunts)
The Good German (stunts)
The Guardian (stunts)
Talladega Nights: the Ballad of Ricky Bobby (stunts)
Poseidon (stunts)
Mission: Impossible III (stunts)
Fun with Dick and Jane (stunts - uncredited)
Herbie Fully Loaded (stunts)
War of the Worlds (stunts)
Drop Dead Sexy (stunts)
After the Sunset (stunts)
National Treasure (stunts)
Highwaymen (stunts)
Peter Pan (stunt rigger - uncredited)
Pirates of the Caribbean: the Curse of the Black Pearl (stunts)
Identity (stunts)
Anger Management (stunts - uncredited)
Gods and Generals (stunt performer)
Star Trek: Nemesis (stunts)
The Rage Within (stunts)
Office Space (stunts)
The Curse of Inferno (stunts)
Shadow Conspiracy (stunts)
Top Dog (stunt player)
Blue Sky (stunts)
Hellbound (stunts)
Three of Hearts (stunts)
Army of One (stunts)
Best of the Best II (stunts)
Sidekicks (stunts)
Star Trek VI: The Undiscovered Country (stunts)
Necessary Roughness (stunts)
The Naked Gun 2½: The Smell of Fear (stunts - as Eric 'Aaarrrgggghhhh' Norris)
Beastmaster 2: Through the Portal of Time (stunts)
Delta Force 2: the Colombian Connection (stunts)
Peacemaker (stunt player)
Hero and the Terror (stunts)
Avenging Force (stunt player)
Invasion U.S.A. (stunts)
Hawaii Five-0 (22 episodes)
Mayans M.C. (3 episodes)
Magnum P.I. (1 episode)
24: Legacy (1 episode)
The Player ( 8 episodes)
CSI: Cyber (2 episodes)
Gang Related (1 episode)
Intercept (TV Movie)
Chase (7 episodes)
Prison Break (21 episodes)
Prison Break: The Final Break (Video)
Walker, Texas Ranger (187 episodes)
The President's Man (TV Movie)
Logan's War: Bound by Honor (TV Movie)
Magnum P.I. (2 seasons)
Hawaii Five-0 (3 ½ seasons)
24: Legacy (1 season)
Marvel's Most Wanted (TV Movie)
Agents of S.H.I.E.L.D. (9 episodes)
The Player (9 episodes)
Hieroglyph (TV Movie)
Sons of Anarchy (2 seasons)
Those Who Kill
Last Man Standing (5 episodes)
Entry Level (TV Movie)
Rogue (TV Movie)
The Finder (13 episodes)
Hart of Dixie (4 episodes)
Chase (18 episodes)
Night and Day (TV Movie)
Terriers (12 episodes)
Metro (TV Movie)
The Deep End (3 episodes)
Prison Break: the Road to Freedom (TV Movie documentary)
A House Divided (TV Movie)
Mammoth (TV Movie)
Walker, Texas Ranger: Trial by Fire (TV Movie)
Walker, Texas Ranger (90 episodes)
Big Time in Hollywood, FL (TV Mini Series, 1 episode)
The Last Ship (1 episode)
The Mentalist (13 episodes)
Last Resort (1 episode)
Revolution (1 episode)
Awake (3 episodes)
Criminal Minds (1 episode)
Southland (1 episode)
Trauma (1 episode)
Ghost Whisperer (1 episode)
NCIS (1 episode)
Eleventh Hour (1 episode)
CSI: Miami (1 episode)
Entourage (1 episode)
Six Feet Under (1 episode)
Star Trek: Enterprise (2 episodes)
Oz (1 episode)


text:
\section{Introduction}

In high-energy hadron--hadron collisions, the vector bosons \Wboson and \Zg are produced via quark--antiquark annihilation~\cite{Drell:1970wh}, and can be observed with very small backgrounds by using their leptonic decay modes. The vector bosons have non-zero momentum transverse to the beam direction due to the emission of quarks and gluons from the initial-state partons as well as to the intrinsic transverse momentum of the initial-state partons in the proton. Phenomenologically, the spectrum at low transverse momentum of the $Z$ boson, \Zpt{}, reconstructed through the decay into a pair of charged leptons, can be described using soft-gluon resummation~\cite{Collins:1984kg,Balazs:1997xd, Becher:2010tm, Catani:2015vma, Ebert:2016gcn, Bizon:2017rah} and non-perturbative models to account for the intrinsic transverse momentum of partons. At high \Zpt{} the spectrum can be calculated by fixed-order perturbative quantum chromodynamics (QCD) predictions~\cite{Melnikov:2006kv,Li:2012wna,Catani:2009sm,Boughezal:2015ded,Ridder:2015dxa}, and next-to-leading-order electroweak (NLO EW) effects are expected to be important~\cite{Denner:2011vu,Kallweit:2015dum,Lindert:2017olm}. Parton-shower models~\cite{Bellm:2015jjp,Sjostrand:2014zea,Bothmann:2019yzt} or resummation may be matched to fixed-order calculations to describe the full spectrum.

A precise measurement of the \Zpt{} spectrum provides an important input to the background prediction in searches for beyond the Standard Model (SM) processes, e.g.\ in the monojet signature~\cite{EXOT-2016-27}, as well as to SM precision measurements. In particular, the measurement of the mass of the \Wboson{} boson~\cite{STDM-2014-18} relies on the measurement of the \Zpt{} distribution to constrain the transverse momentum spectrum of the \Wboson{} boson, \pTW{}, since a direct measurement of the transverse momentum distribution of \Wboson{} bosons is experimentally challenging \cite{STDM-2011-15}. The \Zpt{} spectrum was measured previously in proton--proton ($pp$) collisions at the Large Hadron Collider (LHC) by the ATLAS Collaboration at centre-of-mass energies of $\sqrt{s} = 7$~\TeV{} and $8$~\TeV~\cite{STDM-2012-23, STDM-2014-12}, including several mass regions near and away from the \Zboson{}-boson resonance. Related measurements were also made by the CMS~\cite{CMS-SMP-17-010, CMS-SMP-17-002, CMS-SMP-14-012, CMS-SMP-13-013, CMS-EWK-10-010} and the LHCb~\cite{Aaij:2015gna,AbellanBeteta:2016ugk,Aaij:2016mgv} collaborations.

Compared to measurements at lower $\sqrt{s}$, \Zboson{}-boson
production at 13~\TeV\ is characterized by a smaller parton momentum
fraction of the colliding protons, leading to a different flavour
composition and a larger phase space for hard QCD radiation. A precise
measurement will test this energy dependence and play an important role in
future studies of the \Wboson{}-boson mass using the 13~\TeV{} data.

The measurement of the \Zpt{} spectrum requires a precise
understanding of the momentum scale and resolution of the leptons. To
overcome the related limitations in the low-\Zpt\ domain, the
$\phi_\eta^*$ observable was introduced~\cite{Banfi:2010cf} as an
alternative probe of \Zpt{}. It is defined as

\begin{equation*}
\phi_\eta^* = \tan\left(\frac{\pi-\Delta\phi}{2}\right) \times \sin(\theta^*_\eta)\,,
\end{equation*}

where $\Delta \phi$ is the azimuthal angle in radians between the two leptons. The angle $\theta_\eta^*$ is a measure of the scattering angle of the leptons relative to the proton beam direction in the rest frame of the dilepton system and is defined by $\cos(\theta_\eta^*) = \tanh[(\eta^- - \eta^+)/2]$, where $\eta^-$ and $\eta^+$ are the pseudorapidities\footnote{ ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the $z$-axis along the beam pipe.  The $x$-axis points from the IP to the centre of the LHC ring, and the $y$-axis
points upwards.  Cylindrical coordinates $(r,\phi)$ are used in the
transverse plane, $\phi$ being the azimuthal angle around the
$z$-axis.  The pseudorapidity is defined in terms of the polar angle
$\theta$ as $\eta = -\ln \tan(\theta/2)$. Angular distance is
measured in units of $\Delta R = \sqrt{(\Delta\eta)^{2} +
(\Delta\phi)^{2}}$.  } of the negatively and positively charged lepton, respectively. Therefore, \phiStar{} depends exclusively on the directions of the two leptons, which are measured more precisely than their momenta.

In this paper, measurements of the \Zpt{} and the \phiStar{} spectra
are presented using $pp$ collision data at $\sqs=13$~\TeV\ collected in
2015 and 2016 with the ATLAS detector, corresponding to an integrated
luminosity of \lumi. Both the dielectron and dimuon final states \Zgll ($\ell = e$ or $\mu$) are analysed in a dilepton mass window
of $\Mll = 66$--$116$~\GeV. The measurement is performed in a fiducial
phase space that is close to the detector acceptance for leptons in
transverse momentum $\pt^\ell$ and pseudorapidity
$\eta_\ell$.

\section{The ATLAS detector}
The ATLAS experiment uses a multipurpose detector~\cite{PERF-2007-01, PIX-2018-001, ATLAS-TDR-2010-19} with a cylindrical geometry and almost $4\pi$ coverage in solid angle. The collision point is surrounded by tracking detectors, collectively referred to as the inner detector (ID), followed by a superconducting solenoid providing a 2~T axial magnetic field, a calorimeter system and a muon spectrometer.
The ID provides precise measurements of charged-particle tracks in the pseudorapidity range $|\eta|<2.5$.
It consists of three subdetectors arranged in a coaxial geometry around the beam axis: a silicon pixel detector,
a silicon microstrip detector and a transition radiation tracker.

The electromagnetic calorimeter covers the region $|\eta|<3.2$ and is based on a high-granularity, lead/liquid- argon (LAr) sampling technology.
The hadronic calorimeter uses a steel/scintillator-tile detector in the region $|\eta|<1.7$ and a copper/LAr detector in the region $1.5<|\eta|<3.2$.
The forward calorimeter (FCAL)
covers the range $3.2< |\eta| < 4.9$ and also uses LAr as the active
material and copper or tungsten absorbers for the EM and hadronic sections, respectively.

The muon spectrometer (MS) consists of separate trigger and high-precision tracking chambers to measure the deflection of muons in a magnetic field generated by three large superconducting toroids arranged with an eightfold azimuthal coil symmetry around the calorimeters.
The high-precision chambers cover a range of $|\eta|<2.7$.
The muon trigger system covers the range $|\eta|<2.4$ with resistive-plate chambers in the barrel and thin-gap chambers in the endcap regions.

A two-level trigger system is used to select events in real time~\cite{TRIG-2016-01}. It consists of a hardware-based first-level trigger and a software-based high-level trigger. The latter employs algorithms similar to those used offline and is used to identify electrons and muons.
\section{Analysis methodology}

\subsection{Description of the measurements\label{sec:desc}}

The \Zboson-boson differential cross-sections are measured as a function of \Zpt{} and \phiStar{} separately for the dielectron and dimuon decay channels. Only small background contributions are expected. The results are reported within a fiducial phase space chosen to be close to the experimental acceptance defined by the lepton transverse momenta $\pt^\ell > 27$~\GeV, the absolute lepton pseudorapidity $|\eta_\ell| < 2.5$ and the dilepton invariant mass $\Mll = 66$--$116$~\GeV.

The lepton kinematics can be described at different levels regarding the effects of final-state photon radiation (QED FSR). Cross-sections at \textit{Born} level employ the lepton kinematics before QED FSR, while the \textit{bare} level is defined by leptons after emission of QED FSR. A \textit{dressed} lepton is defined by combining the bare four-momenta of each lepton with that of QED FSR photons radiated from the lepton within a cone of size $\Delta R = 0.1$ around the lepton. The results in this paper are reported at the dressed and Born levels.

The differential cross-sections in \Zpt{} and \phiStar{} are measured and their normalized spectra derived. The total systematic uncertainty of the latter is significantly reduced due to large correlations in many sources of uncertainty between the measurement bins.

\subsection{Simulated event samples}
\label{sec:datamc}

Events simulated by Monte Carlo (MC) generators are used to predict the detector response to the signal process in order to correct the data for detector inefficiencies and resolution as well as to estimate most of the background from processes other than $\Zg \to\ell\ell$ in the selected data sample.

The \Zgll signal process was generated with the \textsc{Powheg-Box V1} MC event generator~\cite{Nason:2004rx,Frixione:2007vw,Alioli:2010xd,Alioli:2008gx} at next-to-leading order in $\alphas$ interfaced to \PYTHIAV{8.186}~\cite{Sjostrand:2014zea} for the modelling of the parton shower, hadronization, and underlying
event, with parameters set according to the AZNLO
tune~\cite{STDM-2012-23}. The CT10 (NLO) set of parton distribution functions (PDF) ~\cite{Lai:2010vv} was used
for the hard-scattering processes, whereas the CTEQ6L1 PDF
set~\cite{Pumplin:2002vw} was used for the parton shower. The effect of final-state photon radiation was simulated with Photos++~v3.52~\cite{Golonka:2005pn,Davidson:2010ew}. The \textsc{EvtGen}~v1.2.0
program~\cite{EvtGen} was used to decay bottom and charm hadrons.

\POWHEG{}+\PYTHIA{}8 was also used to simulate the majority of the background processes considered. The \Ztt and the diboson processes $WW$, $WZ$ and $ZZ$~\cite{Nason:2013ydw} (requiring $m_{\ell\ell}>4$~\GeV\ for any pair of same-flavour opposite-charge leptons) used the same tune and PDF as the signal process. The \ttbar and single-top-quark~\cite{Frixione:2007nw,Re:2010bp} backgrounds to the dimuon channel were simulated with \POWHEG{}+\PYTHIA{}8 with the A14 tune~\cite{ATL-PHYS-PUB-2014-021} and the NNPDF3.0 PDF~\cite{Ball:2014uwa}, while for the dielectron channel \POWHEG{}+\PYTHIA{}6~\cite{Sjostrand:2006za} with the P2012 tune~\cite{Skands:2010ak} and CT10 PDF was used. It was found that the prediction of the \ttbar background is in very good agreement for both generators.
The photon-induced background $\gamma\gamma\rightarrow \ell\ell$ was generated with \PYTHIA{}8 using the NNPDF2.3 QED PDF~\cite{Ball:2013hta}.

The effect of multiple interactions in the same and neighbouring bunch crossings (pile-up) was modelled by overlaying the hard-scattering event with simulated minimum-bias
events generated with \PYTHIAV{8.186} using the MSTW2008LO set of PDFs~\cite{Martin:2009iq} and the A2 tune~\cite{ATL-PHYS-PUB-2012-003}.
The simulated event samples were reweighted to describe the distribution of the number of pile-up interactions in the data, and further reweighted such that the distribution of the longitudinal position of the primary $pp$ collision vertex matches that in data. The primary vertex is defined as the vertex with at least two reconstructed tracks with $\pt>0.4$ GeV and with the highest sum of squared transverse momenta of associated tracks. The \GEANT{}4 program was used to simulate the passage of particles through the ATLAS detector~\cite{Agostinelli:2002hh,SOFT-2010-01}.
The simulated events are reconstructed with the same analysis software as the data.
The reconstruction, trigger and isolation efficiencies as well as lepton momentum scale and resolution in the MC simulation are corrected to match those determined in data~\cite{PERF-2015-10,PERF-2017-01,PERF-2017-03}.

\subsection{Event selection}

Events recorded by the ATLAS detector in the years 2015 and 2016 are used in this analysis. They are required to be recorded during stable beam collisions and with the detector operating normally, leading to a data-set with a corresponding integrated luminosity of \lumi.

Candidate \Zmm{} events were recorded with triggers that require at least one isolated muon with $\pt > 20$~\GeV\ in 2015 and $\pt > 26$~\GeV\ in 2016 data. Candidate Z $\rightarrow ee$ events are triggered requiring
at least one identified electron with $\pt > 24$~\GeV\ in 2015
and $\pt > 26$~\GeV\ in 2016 data \cite{TRIG-2018-05}. In addition to the increased $\pt$ threshold, the electron also has to
satisfy isolation criteria in the 2016 data.

Muon candidates are reconstructed by combining tracks reconstructed in the inner detector with tracks reconstructed in the MS~\cite{PERF-2015-10}. They are required to have $\pt > 27$~\GeV\ and $|\eta|<2.5$ and satisfy identification criteria corresponding to the `medium' working point~\cite{PERF-2015-10}. Track quality requirements are imposed to suppress backgrounds, and the muon candidates are required to be isolated according to the `gradient' isolation criterion~\cite{PERF-2015-10}, which is \pT- and $\eta$-dependent and based on the calorimeter and track information.

Electron candidates are reconstructed from clusters of energy in the electromagnetic calorimeter matched to ID tracks~\cite{PERF-2017-01}.
They are required to have $\pt > 27$~\GeV\ and $|\eta|<2.47$ (excluding the transition regions between the barrel and the endcap electromagnetic calorimeters, $1.37<|\eta|<1.52$). Electron candidates are required to pass the `medium` identification requirement, and are also required to be isolated according to the `gradient' isolation criterion~\cite{PERF-2017-01}.

Electron and muon candidates are required to originate from the primary vertex. Thus, the significance of the track's transverse impact parameter calculated relative to the beam line, $|d_0/\sigma_{d_{0}}|$, must be smaller than 3.0 for muons and less than 5.0 for electrons. Furthermore, the longitudinal impact parameter, $z_0$ (the difference between the $z$-coordinate of the point on the track at which $d_0$ is defined and the longitudinal position of the primary vertex), is required to satisfy $|z_0 \cdot \sin(\theta)| <$ 0.5 mm.

Events are required to contain exactly two same-flavour leptons. The two leptons must be of opposite electric charge and their invariant mass must satisfy $66 < \Mll < 116$~\GeV. Table~\ref{tab:DataZleptons} shows the number of events satisfying the above selection criteria in the electron channel and the muon channel. Also given are the estimated contributions from the background sources described below in Section~\ref{sec:backgrounds}.

\begin{table}[thb]
\caption{\label{tab:DataZleptons}Selected signal candidate events in data for both decay channels as well as the expected background contributions including their total uncertainties.}
\footnotesize
\begin{tabular}{l | c | c}
\hline
& \Zgee 	& \Zgmm 	\\
\hline
Two reconstructed leptons within fiducial volume	&13\,649\,239			& 18\,162\,641				\\
\hline
Electroweak background ($Z\rightarrow\tau\tau$, $WW$, $WZ$, $ZZ$) 			&   ~$40\,000 \pm 2000$  & ~$39\,000 \pm 2000$	\\
Photon-induced	 background											&   ~~$2900 \pm 140$ 	  & ~~$4100  \pm 200$	\\
Top-quark background												&   ~$38\,000 \pm 1900$  & ~$45\,400 \pm 2200$	\\
Multijet background 													&   ~~~~$8500 \pm 4900$	   & ~~$1000 \pm 200$	\\
\hline
\end{tabular}
\centering
\end{table}

\subsection{Estimation of backgrounds}
\label{sec:backgrounds}

The backgrounds from all sources other than multijet processes are estimated using the MC samples detailed in Section~\ref{sec:datamc}.
The number and properties of the background events where one or two reconstructed lepton candidates originate from hadrons or hadron decay products,
i.e.\ multijet processes as well as $W$+jets, are estimated using the data-driven techniques described in the following for both decay channels.

In the electron channel, a multijet-dominated sample is selected from
data with two same-charge electron candidates satisfying the `loose'
identification criteria, but not the `medium'
criteria~\cite{PERF-2017-01}, i.e.\ they are more likely to be caused by misidentified jets. In the muon channel, a multijet sample
is obtained by selecting two same-charge muons. The residual
contamination from processes with prompt leptons is estimated using
the simulation and subtracted.

The normalization of the multijet template in the electron channel is determined in a fit to the distribution of the electron isolation using all event-selection criteria except those for the isolation variables. Systematic uncertainties in the normalization are estimated by varying the fit range on the electron isolation distribution.

In the muon channel, the normalization is obtained using the ratio of
number of opposite-charge dimuon events to the number of same-charge dimuon events where the muons
fail to satisfy the isolation criterion. Assuming no correlation between the
isolation of muons in multijet events and their charge, this ratio can be
applied to a control sample, defined by pairs of isolated same-charge muons passing the signal-kinematic selection, to determine the multijet contamination in the signal region. The systematic uncertainty in the estimate is
obtained by varying the isolation criterion for the muons.

The total fraction of selected data events originating from background
processes is about 0.5\% in both the electron and muon
channels. The background is dominated by contributions from diboson
and \ttbar processes. An overview of the estimated number of background events is given in Table \ref{tab:DataZleptons}, together with the corresponding total uncertainties.

Figure~\ref{fig:controlplots} shows the dilepton invariant mass and the lepton pseudorapidity distribution, for the electron and muon channels separately. The predictions are in fair agreement with the data. The impact of the residual differences between these distributions on the \Zpt{} and \phiStar{} measurements is estimated by reweighting the MC signal sample to data and then repeating the measurement procedure. Figure~\ref{fig:controlplotsUnfolding} compares the measured  \Zpt{} and \phiStar{} distributions for both channels with the signal MC predictions from \POWHEG{}+\PYTHIA{}8 using the AZNLO tune. The disagreement between the data and the predictions for large values of \Zpt{} and \phiStar{} is expected because \POWHEG{}+\PYTHIA{}8 is effectively a computation at leading-order in $\alphas$ in this region.

\begin{figure}[htbp]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_01a.pdf}
\includegraphics[width=0.495\textwidth]{fig_01b.pdf}
\includegraphics[width=0.495\textwidth]{fig_01c.pdf}
\includegraphics[width=0.495\textwidth]{fig_01d.pdf}
\caption{
\label{fig:controlplots}
The distribution of events passing the selection requirements in the electron channel (left) and muon channel (right) as a function of dilepton invariant mass \Mll (upper row) and lepton pseudorapidity $\eta$ (lower row), the latter with one entry for each lepton per event. The MC signal sample is simulated using \POWHEG{}+\PYTHIA{}8. The statistical uncertainties of the data points are generally smaller than the size of the markers. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.}
\end{center}
\end{figure}

\begin{figure}[htbp]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_02a.pdf}
\includegraphics[width=0.495\textwidth]{fig_02b.pdf}
\includegraphics[width=0.495\textwidth]{fig_02c.pdf}
\includegraphics[width=0.495\textwidth]{fig_02d.pdf}
\caption{
\label{fig:controlplotsUnfolding}
The distribution of events passing the selection requirements in the electron channel (left) and muon channel (right) as a function of dilepton transverse momentum (upper row) and \phiStar{} (lower row). The MC signal sample is simulated using \POWHEG{}+\PYTHIA{}8. The statistical uncertainties of the data points are generally smaller than the size of the markers. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.}
\end{center}
\end{figure}

\subsection{Correction for detector effects}

The production cross-section times the branching ratio for decays into
a single lepton flavour are measured in fiducial volumes as defined in
Section \ref{sec:desc}. Integrated fiducial cross-sections in the electron and muon channels
are computed following the equation

\begin{equation*}
\sigma^\mathrm{fid}_{\Zgll} = \frac{N_\mathrm{Data}-N_\mathrm{Bkg}}{C_Z \cdot L},
\end{equation*}

where $N_\mathrm{Data}$ is the number of observed signal candidates and $N_\mathrm{Bkg}$ is the number of background events expected in the selected sample. The integrated luminosity of the sample is $L = 36.1$\,\ifb. A correction for the event detection efficiency is applied with the factor $C_Z$, which is obtained from the simulation of signal events as the ratio of the sum of event weights after simulation, reconstruction and selection, to the sum of MC event weights for events satisfying the fiducial requirements. The factor $C_Z$ is affected by experimental uncertainties, described in Section \ref{sec:Systematics}, while theory and modelling uncertainties are negligible.

The differential distributions within the fiducial volume are corrected for detector effects and bin-to-bin migrations using an iterative Bayesian unfolding method~\cite{DAgostini:1994zf,DAgostini:2010,Adye:2011gm}. First, the data are corrected for events that pass the detector-level selection but not the particle-level selection. Then, the iterative Bayesian unfolding technique is used as a regularized way to correct for the detector resolution in events that pass both the detector-level and particle-level selections. The method is applied with four iterations implemented in the RooUnfold framework \cite{Adye:2011gm}. After the application of the response matrix, a final correction is applied to account for events that pass the particle-level but not detector-level selection, resulting in unfolded distributions on Born and dressed particle level. The response matrices, which connect the distributions at reconstruction and particle level, as well as the correction factors are derived using the \POWHEG{}+\PYTHIA signal MC sample.
\section{\label{sec:Systematics}Statistical and systematic uncertainties}

Uncertainties in the measurement are assessed for each aspect of the analysis, including the background subtraction, event detection efficiencies, response matrix, and unfolding method. For each uncertainty, a part of the analysis chain is varied and the entire analysis including the background subtraction is repeated.

The effect on the measurement from the size of the data and MC samples is estimated by generating pseudo-experiment variations of the respective samples. The resulting statistical uncertainties are considered as uncorrelated between bins and between channels.

Uncertainties in the scale and resolution of the electron energy scale~\cite{PERF-2017-03} and muon momentum scale~\cite{PERF-2015-10} are among the dominant uncertainties in the \Zpt{} measurement. Furthermore the uncertainties in the efficiencies to reconstruct and select leptons are considered~\cite{PERF-2017-01,PERF-2015-10,TRIG-2016-01, TRIG-2018-05}, which are separated into the efficiencies for lepton reconstruction, identification, trigger, track-to-vertex matching, and isolation. There is an additional uncertainty in the muon channel to cover charge-dependent biases in the muon momentum measurement. The majority of these experimental uncertainties are considered correlated between bins of \Zpt{} and \phiStar{}. An exception are the components of the reconstruction and identification efficiencies which have a significant statistical component due to the limited number of events in the data samples used to derive the efficiency corrections. Uncertainties related to electron or muon reconstruction and identification are always assumed to be uncorrelated with each other. They dominate the uncertainty in the fiducial cross-section measurement.

The uncertainties in the MC background estimates are obtained by independently varying the theory cross-sections used to normalize the corresponding samples and observing the effect on the final cross-section. These background uncertainties are considered correlated between bins of \Zpt{} and \phiStar{} and between the electron and muon channels. As described in Section~\ref{sec:backgrounds}, the uncertainty in the multijet background in the electron channel is obtained by changing the fit range over which the background normalization is extracted. For the muon channel, the isolation variable used in the normalization procedure is modified. The estimated multijet backgrounds are assumed to be uncorrelated between the channels.

An uncertainty is derived to cover the mis-modelling of the simulated pile-up activity following the measurement of the cross-section of inelastic $pp$ collisions~\cite{STDM-2015-05}. Also, the uncertainty in modelling the distribution of the longitudinal position of the primary vertex is considered. These uncertainties are treated as correlated between the electron channel and muon channel.

The uncertainty from the unfolding method is determined by repeating the procedure with a different simulation where the nominal particle-level spectrum is reweighted so that the simulated detector-level spectrum is in good agreement with the data.
The modified detector-level distribution is unfolded with the nominal response matrix and the difference between the result and the reweighted particle-level spectrum is taken as the bias of the unfolding method due to the choice of prior.
The closure of the unfolding procedure is also tested using the generator-level distributions of the \SHERPA{} MC sample described in Section~\ref{sec:comparepred}, where consistent results within the assigned unfolding uncertainties are found.

The uncertainty from the choice of PDF used in the signal MC generator is evaluated by reweighting the signal MC simulation to the 52 error sets of the CT10 PDF set and computing the resulting variation of the results~\cite{Lai:2010vv,Buckley:2014ana}. The differences found in this way are negligible, similar to scale-choice uncertainties. The uncertainty in the combined 2015--2016 integrated luminosity is 2.1\% \cite{ATLAS-CONF-2019-021}, obtained using the LUCID-2 detector \cite{LUCID2} for the primary luminosity measurements. This uncertainty only applies to the absolute cross-section measurements.

\begin{table}[tb]
\caption{\label{tab:CFacUncertainty}Overview of the detector efficiency correction factors, $C_Z$, for the electron and muon channels and their systematic uncertainty contributions.}
\footnotesize
\centering

\begin{tabular}{l | cc | cc}
\hline
& \multicolumn{2}{c}{Electron channel} 	& \multicolumn{2}{c}{Muon channel}	\\
\hline
& Born	& Dressed					& Born 	& Dressed			\\
\hline
$C_Z$					& $0.509\pm 0.005$	& $0.522\pm 0.005$	& $0.685\pm 0.011$	& $0.702\pm 0.011$\\
\hline
Trigger efficiencies						& \multicolumn{2}{c|}{$\pm$\,0.0004}& \multicolumn{2}{c}{$\pm$\,0.0004}	\\
Identification \& reconstruction efficiencies		& \multicolumn{2}{c|}{$\pm$\,0.0049}	& \multicolumn{2}{c}{$\pm$\,0.0102}	\\
Isolation efficiencies						& \multicolumn{2}{c|}{$\pm$\,0.0009}	& \multicolumn{2}{c}{$\pm$\,0.0029}	\\
Energy/momentum scale and resolution		& \multicolumn{2}{c|}{$\pm$\,0.0014}	& \multicolumn{2}{c}{$\pm$\,0.0010}	\\
Pile-up								& \multicolumn{2}{c|}{$\pm$\,0.0011}	& \multicolumn{2}{c}{$\pm$\,0.0019}	\\
Model uncertainties						& \multicolumn{2}{c|}{$\pm$\,0.0001}	& \multicolumn{2}{c}{$\pm$\,0.0001}	\\
\hline
\end{tabular}

\end{table}

The experimental uncertainties of $C_Z$ for the integrated cross-section
measurements in the electron and muon channels are summarized
in Table~\ref{tab:CFacUncertainty}. The electron identification
efficiency and muon reconstruction efficiency contribute a large
fraction of the total systematic uncertainty for both the integrated
and absolute differential measurements. These uncertainties are
greatly reduced for the normalized measurement of differential
distributions. A summary of the uncertainties in the normalized differential
cross-section measurements is provided in Figure~\ref{fig:systematics}
as a function of \Zpt{} and \phiStar{} for both decay channels.
The statistical uncertainties for the electron and muon channel measurements are a combination of the uncertainties due to limited data and MC statistics.
The systematic uncertainties are divided into categories and originate from lepton scales and resolutions, reconstruction and identification efficiencies, as well as the MC signal modelling in the unfolding procedure and further smaller uncertainty sources such as the subtraction of background contributions.

\begin{figure}[htbp]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_03a.pdf}
\includegraphics[width=0.495\textwidth]{fig_03b.pdf}
\includegraphics[width=0.495\textwidth]{fig_03c.pdf}
\includegraphics[width=0.495\textwidth]{fig_03d.pdf}
\caption{
\label{fig:systematics}
The systematic uncertainties for the electron channel measurement (left) and muon channel measurement (right) for the normalized \Zpt{} (upper row) and normalized \phiStar{} (lower row) unfolded results. The statistical uncertainties are a combination of the uncertainties due to limited data and MC statistics. The $\Zpt{}$ distribution is split into linear and logarithmic scales at 30~\GeV. Some uncertainties are larger than 2\% for $\Zpt{}>200$ GeV and hence cannot be displayed. The corresponding uncertainties are also summarized in Table \ref{tab:difcrosssections}.
}
\end{center}
\end{figure}

\section{Results and discussion}

\subsection{Combination}

The fiducial cross-sections measured in the \Zgee and \Zgmm channels are presented in Table \ref{tab:crosssections} including statistical, systematic and luminosity uncertainties. When correcting for the more restrictive fiducial volume definition, these results are in good agreement with the previous ATLAS measurements at 13~\TeV~\cite{STDM-2016-02}. The electron- and muon-channel cross-sections are combined using $\chi^2$ minimization, following the best linear unbiased estimator prescription (\textsc{Blue}) \cite{Lyons:1988rp, Valassi:2003mu, Nisius:2014wua}. The combination is performed on Born level, resulting in a combined cross-section of $\sigma_\mathrm{fid} (pp\rightarrow \Zgll) =$ 736.2 $\pm$ 0.2(stat) $\pm$ 6.4 (sys) $\pm$ 14.7 (lumi)~pb (Table~\ref{tab:crosssections}). There is a reduction of the uncertainty compared to individual electron- and muon-channel measurements since the dominant detector-related systematic uncertainty sources are largely uncorrelated. The uncertainties due to pile-up, physics modelling and luminosity are treated as correlated between the two decay channels.

\begin{table}[thb]
\caption{\label{tab:crosssections}Measured inclusive cross-section in the fiducial volume in the electron and muon decay channels at Born level and their combination as well as the theory prediction at NNLO in $\alphas$ using the CT14 PDF set.}
\footnotesize
\centering
\begin{tabular}{l | c | c }
\hline
Channel					&	Measured cross-section $\times$ $\mathcal{B}$(\Zgll) 		&	Predicted cross-section $\times$ $\mathcal{B}$(\Zgll) 		\\
&	(value $\pm$ stat. $\pm$ syst. $\pm$ lumi.) 					&	(value $\pm$ PDF $\pm$ $\alphas$ $\pm$ scale $\pm$ intrinsic)				\\
\hline
\Zgee		&	$738.3 \pm 0.2  \pm \hspace{0.18cm}7.7  \pm 15.5 $\,pb		&													\\
\Zgmm		&	$731.7 \pm 0.2  \pm 11.3  \pm 15.3 $\,pb	&													\\
\Zgll			& 	$736.2 \pm 0.2  \pm \hspace{0.18cm}6.4  \pm 15.5 $\,pb	&	$703^{+19}_{-24} \; ^{+6}_{-8} \; ^{+4}_{-6} \; ^{+5}_{-5}$\,pb	\cite{STDM-2016-02}	\\
\hline
\hline
\end{tabular}
\end{table}

The normalized differential cross-sections $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\Zpt{}$ and $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phiStar{}$ measured in the two decay channels as well as their combination are illustrated in Figure~\ref{fig:combined}. When building the $\chi^2$ for combination procedure, the measurement uncertainties are separated into those from bin-to-bin uncorrelated sources and those from bin-to-bin correlated sources, the latter largely reduced due to the normalization by the fiducial cross-section. The normalized differential measurements are combined at Born level following the \textsc{Blue} prescription. The resulting $\chi^2/N_\mathrm{dof} = 47/44$ for the combination for \Zpt{} and the $\chi^2/N_\mathrm{dof} = 32/36$ for \phiStar{} indicate good agreement between the two channels.\footnote{The $\chi^2/N_\mathrm{dof}$ is still good when taking into account only bins with $\Zpt{}>50$~\GeV.} The combined precision is between 0.1\% and 0.5\% for $\Zpt{} < 100$~\GeV, rising to 10\% towards the high end of the spectrum, where the overall precision is limited by the data and MC statistics. The combined results for both distributions are presented in Table~\ref{tab:difcrosssections} including statistical and bin-to-bin uncorrelated and correlated systematic uncertainties. The measurement results are reported at Born level and factors $k_\mathrm{dr}$ are given to transfer to the dressed particle level.

\begin{table}[h!]
\caption{\label{tab:difcrosssections}The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_\mathrm{dr}$ to translate from the Born particle level to the dressed particle level.}
\footnotesize
\centering
\begin{tabular}{lllll | llllll}
\hline
Bin			& $1/\sigma_\mathrm{fid}\,\times$ 		& Corr. 	& Uncorr. 	& $k_\mathrm{dr}$	& Bin	& $1/\sigma_\mathrm{fid}\,\times$ 	& Corr. 	& Uncorr. 	& $k_\mathrm{dr}$	\\
$[$\GeV$]$	& $\mathrm{d}\sigma/\mathrm{d}\Zpt{}$	& uncert. 		& uncert.		&			& 	& $\mathrm{d}\sigma/\mathrm{d}\phiStar{}$ & uncert. 		& uncert.		&			\\
& $[$1/GeV$]$					&			&			&			&		&						&			&			& 	\\
\hline
0-2		& 0.024189	& $\pm$ 0.15\%	&$\pm$ 0.18\%	& 0.978	& 0-0.004		& 8.8053	& $\pm$ 0.03\%	&$\pm$ 0.13\% 	&0.992	\\
2-4		& 0.051144	& $\pm$ 0.06\%	&$\pm$ 0.08\%	& 0.985	& 0.004-0.008	& 8.6969	& $\pm$ 0.03\%	&$\pm$ 0.13\% 	&0.993	\\
4-6		& 0.053232	& $\pm$ 0.05\%	&$\pm$ 0.08\%	& 0.994	& 0.008-0.012	& 8.5624	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&0.993	\\
6-8		& 0.047383	& $\pm$ 0.05\%	&$\pm$ 0.08\%	& 1.000	& 0.012-0.016	& 8.3378	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&0.994	\\
8-10		& 0.040568	& $\pm$ 0.04\%	&$\pm$ 0.09\%	& 1.010	& 0.016-0.02	& 8.0881	& $\pm$ 0.03\%	&$\pm$ 0.14\% 	&0.994	\\
10-12	& 0.034317	& $\pm$ 0.06\%	&$\pm$ 0.11\%	& 1.010	& 0.02-0.024	& 7.7920	& $\pm$ 0.03\%	&$\pm$ 0.14\% 	&0.995	\\
12-14	& 0.029157	& $\pm$ 0.07\%	&$\pm$ 0.12\%	& 1.010	& 0.024-0.029	& 7.4174	& $\pm$ 0.02\%	&$\pm$ 0.12\% 	&0.995	\\
14-16	& 0.024804	& $\pm$ 0.06\%	&$\pm$ 0.14\%	& 1.010	& 0.029-0.034	& 7.0360	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&0.996	\\
16-18	& 0.021268	& $\pm$ 0.05\%	&$\pm$ 0.15\%	& 1.010	& 0.034-0.039	& 6.5989	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&0.998	\\
18-20	& 0.018325	& $\pm$ 0.04\%	&$\pm$ 0.16\%	& 1.010	& 0.039-0.045	& 6.1608	& $\pm$ 0.02\%	&$\pm$ 0.12\% 	&0.998	\\
20-22.5	& 0.015605	& $\pm$ 0.03\%	&$\pm$ 0.14\%	& 1.010	& 0.045-0.051	& 5.7085	& $\pm$ 0.01\%	&$\pm$ 0.13\% 	&0.999	\\
22.5-25	& 0.01318	0	& $\pm$ 0.03\%	&$\pm$ 0.15\%	& 1.000	& 0.051-0.057	& 5.2791	& $\pm$ 0.02\%	&$\pm$ 0.14\% 	&1.000	\\
25-27.5	& 0.011207	& $\pm$ 0.04\%	&$\pm$ 0.17\%	& 1.000	& 0.057-0.064	& 4.8488	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&1.000	\\
27.5-30	& 0.0095568	& $\pm$ 0.05\%	&$\pm$ 0.19\%	& 0.999	& 0.064-0.072	& 4.4139	& $\pm$ 0.01\%	&$\pm$ 0.12\% 	&1.000	\\
30-33	& 0.0081029	& $\pm$ 0.06\%	&$\pm$ 0.17\%	& 0.998	& 0.072-0.081	& 3.9705	& $\pm$ 0.01\%	&$\pm$ 0.12\% 	&1.000	\\
33-36	& 0.0067881	& $\pm$ 0.08\%	&$\pm$ 0.19\%	& 0.996	& 0.081-0.091	& 3.5515	& $\pm$ 0.01\%	&$\pm$ 0.12\% 	&1.000	\\
36-39	& 0.0057563	& $\pm$ 0.09\%	&$\pm$ 0.21\%	& 0.994	& 0.091-0.102	& 3.1421	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&1.000	\\
39-42	& 0.0048769	& $\pm$ 0.12\%	&$\pm$ 0.23\%	& 0.993	& 0.102-0.114	& 2.7659	& $\pm$ 0.01\%	&$\pm$ 0.13\% 	&1.000	\\
42-45	& 0.0041688	& $\pm$ 0.12\%	&$\pm$ 0.25\%	& 0.992	& 0.114-0.128	& 2.4125	& $\pm$ 0.01\%	&$\pm$ 0.13\% 	&1.000	\\
45-48	& 0.0035213	& $\pm$ 0.14\%	&$\pm$ 0.28\%	& 0.993	& 0.128-0.145	& 2.0648	& $\pm$ 0.01\%	&$\pm$ 0.12\% 	&1.000	\\
48-51	& 0.0029751	& $\pm$ 0.17\%	&$\pm$ 0.31\%	& 0.991	& 0.145-0.165	& 1.7299	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&1.000	\\
51-54	& 0.0025433	& $\pm$ 0.18\%	&$\pm$ 0.35\%	& 0.992	& 0.165-0.189	& 1.4282	& $\pm$ 0.02\%	&$\pm$ 0.13\% 	&1.000	\\
54-57	& 0.0021832	& $\pm$ 0.20\%	&$\pm$ 0.38\%	& 0.994	& 0.189-0.219	& 1.1469	& $\pm$ 0.02\%	&$\pm$ 0.12\% 	&1.000	\\
57-61	& 0.0018779	& $\pm$ 0.15\%	&$\pm$ 0.31\%	& 0.994	& 0.219-0.258	& 0.8848	& $\pm$ 0.02\%	&$\pm$ 0.12\% 	&1.000	\\
61-65	& 0.0015932	& $\pm$ 0.17\%	&$\pm$ 0.35\%	& 0.994	& 0.258-0.312	& 0.6470	& $\pm$ 0.03\%	&$\pm$ 0.11\% 	&1.000	\\
65-70	& 0.0013519	& $\pm$ 0.16\%	&$\pm$ 0.32\%	& 0.995	& 0.312-0.391	& 0.4387	& $\pm$ 0.03\%	&$\pm$ 0.11\% 	&1.000	\\
70-75	& 0.0011323	& $\pm$ 0.17\%	&$\pm$ 0.37\%	& 0.995	& 0.391-0.524	& 0.2610	& $\pm$ 0.03\%	&$\pm$ 0.10\% 	&1.000	\\
75-80	& 0.0009574	& $\pm$ 0.20\%	&$\pm$ 0.43\%	& 0.995	& 0.524-0.695	& 0.1414	& $\pm$ 0.04\%	&$\pm$ 0.13\% 	&1.000	\\
80-85	& 0.0008150	& $\pm$ 0.22\%	&$\pm$ 0.49\%	& 0.995	& 0.695-0.918	& 0.07462	& $\pm$ 0.07\%	&$\pm$ 0.17\% 	&1.000	\\
85-95	& 0.0006537	& $\pm$ 0.14\%	&$\pm$ 0.29\%	& 0.996	& 0.918-1.153	& 0.04047	& $\pm$ 0.12\%	&$\pm$ 0.27\% 	&1.000	\\
95-105	& 0.0004849	& $\pm$ 0.18\%	&$\pm$ 0.37\%	& 0.995	& 1.153-1.496	& 0.02167	& $\pm$ 0.14\%	&$\pm$ 0.30\% 	&1.000	\\
105-125	& 0.0003291	& $\pm$ 0.12\%	&$\pm$ 0.25\%	& 0.996	& 1.496-1.947	& 0.01084	& $\pm$ 0.18\%	&$\pm$ 0.42\% 	&1.000	\\
125-150	& 0.0001861	& $\pm$ 0.16\%	&$\pm$ 0.32\%	& 0.994	& 1.947-2.522	& 0.005386	& $\pm$ 0.23\%	&$\pm$ 0.59\% 	&1.000	\\
150-175	& 0.0001050	& $\pm$ 0.24\%	&$\pm$ 0.51\%	& 0.993	& 2.522-3.277	& 0.002738	& $\pm$ 0.31\%	&$\pm$ 0.79\% 	&1.000	\\
175-200	& 6.1279$\cdot 10^{-5}$	& $\pm$ 0.30\%	&$\pm$ 0.78\%	& 0.992	& 3.277-5.000	& 0.0011730	& $\pm$ 0.29\%	&$\pm$ 0.72\% 	&1.000	\\
200-250	& 3.0584$\cdot 10^{-5}$	& $\pm$ 0.22\%	&$\pm$ 0.66\%	& 0.995	& 5.000-10.00	& 0.0003372	& $\pm$ 0.30\%	&$\pm$ 0.78\% 	&0.997	\\
250-300	& 1.2211$\cdot 10^{-5}$	& $\pm$ 0.34\%	&$\pm$ 1.4\%	& 0.997	& 	& \\
300-350	& 5.9026$\cdot 10^{-6}$	& $\pm$ 0.56\%	&$\pm$ 2.3\%	& 0.994	& 	& \\
350-400	& 2.7742$\cdot 10^{-6}$	& $\pm$ 0.90\%	&$\pm$ 3.8\%	& 0.991	& 	& \\
400-470	& 1.2513$\cdot 10^{-6}$	& $\pm$ 0.82\%	&$\pm$ 4.9\%	& 0.991	& 	& \\
470-550	& 5.5219$\cdot 10^{-7}$	& $\pm$ 1.2\%	&$\pm$ 7.9\%	& 0.994	& 	& \\
550-650	& 2.0165$\cdot 10^{-7}$	& $\pm$ 1.5\%	&$\pm$ 13\%	& 0.995	& 	& \\
650-900	& 5.1153$\cdot 10^{-8}$	& $\pm$ 1.8\%	&$\pm$ 16\%	& 0.990	& 	& \\
900-2500	& 1.5735$\cdot 10^{-9}$	& $\pm$ 6.3\%	&$\pm$ 60\%	& 0.964	& 	& \\
\hline
\end{tabular}
\end{table}

\begin{figure}[htbp]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_04a.pdf}
\includegraphics[width=0.495\textwidth]{fig_04b.pdf}
\caption{\label{fig:combined}The measured normalized cross section as a function of $\Zpt{}$ (left) and \phiStar{} (right) for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown. The $\Zpt{}$ distribution is split into linear and logarithmic scales at 30~\GeV.}
\end{center}
\end{figure}

\subsection{Comparison with predictions}
\label{sec:comparepred}

The integrated fiducial cross-section is compared with a fixed-order
theory prediction that is computed in the same way as in
Ref.~\cite{STDM-2015-03}. The speed-optimized \textsc{DYTurbo}~\cite{Camarda:2019zyx} version of the \textsc{DYNNLO}~1.5~\cite{Catani:2009sm}
program with the CT14 NNLO set of PDFs~\cite{Dulat:2015mca} is used to
obtain a prediction at next-to-next-to-leading order (NNLO) in
$\alphas$ in the $G_\mu$ EW scheme~\cite{Hollik:1988ii}. The
\textsc{FEWZ}~3.1~\cite{Li:2012wna} program is used to compute
next-to-leading-order (NLO) electroweak corrections and to cross-check the
\textsc{DYNNLO} calculation. The prediction is shown in Table
\ref{tab:crosssections} together with its uncertainties estimated as
follows. The dominant uncertainty is from limited knowledge of the proton PDFs
and is estimated using the eigenvectors of the respective CT14 PDF
set, rescaled from 90\% to 68\% confidence level. The uncertainties
due to the strong coupling constant are estimated by varying $\alphas$
by $\pm 0.001$. Missing higher-order QCD corrections are estimated by
variations of the renormalization ($\muR$) and factorization
($\muF$) scales by factors of two with an additional
constraint of $0.5\leq \muR/\muF\leq 2$ around the nominal value of
\Mll. The deviation from the \textsc{FEWZ} calculation is taken as
an intrinsic uncertainty in the NNLO QCD calculation.
A more detailed discussion of the agreement with theory predictions using different PDF sets is given in Ref. \cite{STDM-2016-02}.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_05a.pdf}
\includegraphics[width=0.495\textwidth]{fig_05b.pdf}
\caption{\label{fig:comparison} Comparison of the normalized \Zpt{}
(left) and \phiStar{} (right) distributions predicted by different
computations: \PYTHIA{}8 with the AZ tune, \POWHEG{}+\PYTHIA{}8 with
the AZNLO tune, \SHERPA v2.2.1 and \textsc{RadISH} (only for \Zpt{})
with the Born level combined measurement. The uncertainties of the
measurement are shown as vertical bars and uncertainties of the
\SHERPA and \textsc{RadISH} predictions are indicated by the
coloured bands.}
\end{center}
\end{figure}

The differential measurements are compared with a variety of predictions of the
\Zpt{} and $\phiStar$ spectra that are based on different theoretical approaches to take
into account both the soft and hard emissions from the initial state (ISR).
Unless stated otherwise, the predictions do not consider NLO EW effects.
The comparisons between
the combined result corrected to QED Born level and the various
predictions are shown in Figures~\ref{fig:comparison} and \ref{fig:comparison2}. Systematic
uncertainties in the theoretical predictions are evaluated for
this comparison where feasible.

The first prediction is obtained from \PYTHIA{}8 with
matrix elements at LO in $\alphas$ supplemented with a parton shower
with the AZ set of tuned parameters~\cite{STDM-2012-23}. The AZ tune
optimized the intrinsic $k_\mathrm{T}$ and parton shower ISR
parameters to optimally describe the ATLAS 7~\TeV{} \Zpt{} and \phiStar{}
data~\cite{STDM-2012-23, STDM-2012-06}. It was later used in the
measurement of the $W$-boson mass using 7~\TeV{}
data~\cite{STDM-2014-18}, which requires a high-precision description
of the $W$-boson transverse momentum spectrum at low \pT.

The second prediction is based on \POWHEG{}+\PYTHIA{}8 using NLO matrix elements
with the \PYTHIA{}8 parton shower parameters set according to
the AZNLO tune~\cite{STDM-2012-23} derived using the same data as the
\PYTHIA{}8 AZ tune. The predictions using the AZ and AZNLO tunes
describe the 13~\TeV{} data to within 2--4\% in the region of low $\Zpt <
40$~\GeV{} and $\phiStar < 0.5$, and in this region the prediction using the
\PYTHIA{}8 AZ tune is the one that agrees best with the data. This
shows that predictions based on tunes to 7~\TeV{} collision data can also
provide a good description at significantly higher centre-of-mass
energies for low \Zpt{}. At high \Zpt{} these predictions are well below the data due to missing
higher-order matrix elements, similar to the situation observed at lower $\sqrt{s}$.

The third prediction is simulated with the
\SHERPA~v2.2.1~\cite{Bothmann:2019yzt} generator. In this set-up,
NLO-accurate matrix elements for up to two partons, and LO-accurate matrix
elements for up to four partons are calculated with the
Comix~\cite{Gleisberg:2008fv} and
OpenLoops~\cite{Cascioli:2011va,Denner:2016kdg} libraries. The default
\SHERPA parton shower~\cite{Schumann:2007mg} based on Catani--Seymour
dipole factorisation and the cluster hadronization model~\cite{Winter:2003tt} is
used with the dedicated set of tuned parameters developed by the
authors for the NNPDF3.0nnlo PDF set~\cite{Ball:2014uwa}. The NLO
matrix elements of a given parton multiplicity are matched to the parton
shower using a colour-exact variant of the MC@NLO
algorithm~\cite{Hoeche:2011fd}. Different parton multiplicities are then
merged into an inclusive sample using an improved CKKW matching
procedure~\cite{Catani:2001cc,Hoeche:2009rj} which is extended to NLO
accuracy using the MEPS@NLO prescription~\cite{Hoeche:2012yf}. The
merging cut is set to $20$~\GeV. Uncertainties from missing higher
orders are evaluated~\cite{Bothmann:2016nao} using seven variations of
the QCD factorization and renormalization scales in the matrix
elements by factors of $0.5$ and $2$, avoiding variations in opposite
directions. For the computation of uncertainties in the normalized
spectra the effect of a certain variation is fully
correlated across the full spectrum and an envelope of all variations
is taken at the end, which results in uncertainties of 3--4\% at low
\Zpt{} and up to 25\% at high \Zpt{}. The effects of uncertainties in the PDF set are
evaluated using 100 replica variations and are found to be very small,
typically $<1\%$ up to $\Zpt < 100$~\GeV{} and a few percent
above. \SHERPA does describe the data in the high $\Zpt > 30$~\GeV{}
and $\phiStar > 0.1$ region to within about 4\% up to the point where
statistical uncertainties in the data become significant, which is
better than the uncertainty estimate obtained
from scale variations. On the other hand, the \SHERPA prediction
disagrees with the shape of the data at low $\Zpt < 25$~\GeV{} and
somewhat less with the \phiStar{} distribution. The data may be useful
in improving the parton shower settings in this regime.

Finally, for the \Zpt{} spectrum only, a prediction based on the
\textsc{RadISH} program \cite{Bizon:2019zgf, Bizon:2018foh} is
presented that combines a fixed-order NNLO prediction of
$Z$+jet production ($O(\alphas^3)$) from
\textsc{NNLOjet}~\cite{Gehrmann-DeRidder:2016jns} with resummation of
$\log(\Mll/\Zpt)$ terms at next-to-next-to-next-to-leading-logarithm (N$^3$LL)
accuracy~\cite{Bizon:2017rah}. The NNPDF3.1nnlo set of
PDFs~\cite{Ball:2017nwa} is used with QCD factorization and
renormalization scales set to
$\muR=\muF=\sqrt{(\Mll)^2+(\Zpt)^2}$ and the resummation scale set to $Q = \Mll/2$. Uncertainties in this prediction are derived from
variations of $\muR$ and $\muF$ in the same way as for the \SHERPA
prediction described above and, in addition, two variations of $Q$ by a
factor of two up and down, assuming that the effects of scale variations
are fully correlated across the full spectrum. Within the
uncertainties of typically 1--3\% the \textsc{RadISH} prediction
agrees well with the data over the full spectrum, highlighting
the benefits of this state-of-the-art prediction.

\begin{figure}[t]
\begin{center}
\includegraphics[width=0.495\textwidth]{fig_06.pdf}
\caption{\label{fig:comparison2} Comparison of the normalized \Zpt{}
distribution in the range $\Zpt > 10$~\GeV. The Born level combined
measurement is compared with predictions by \SHERPA v2.2.1,
fixed-order \textsc{NNLOjet} and \textsc{NNLOjet} supplied with NLO
electroweak corrections. The uncertainties in the measurement are
shown as vertical bars and the uncertainties in the predictions
are indicated by the coloured bands.}
\end{center}
\end{figure}

Figure \ref{fig:comparison2} compares the measurement with predictions
in the range of $\Zpt > 10$~\GeV. In addition to the \SHERPA
prediction described above, the data are compared with the fixed-order
\textsc{NNLOjet} prediction described above both with and without NLO
EW corrections~\cite{Denner:2011vu}. The \textsc{NNLOjet} prediction
is only expected to describe the data at sufficiently large $\Zpt{}
\gtrsim 15\,\GeV$, while deviations for smaller values are expected
due to large logarithms
$\ln(\Mll/\Zpt)$~\cite{Gehrmann-DeRidder:2016jns}.  At the highest
\Zpt{} values probed, the application of NLO EW leads to a suppression
of up to $20\%$ due to large Sudakov logarithms. The theoretical
uncertainties on these corrections are not shown, but have been
elsewhere estimated to be up to 5\% for $\Zpt{} \approx
1\,\TeV$~\cite{Lindert:2017olm}. In this region, \textsc{NNLOjet}
without NLO EW corrections is generally above the data, and when
including these corrections it tends to be lower than the
data. However, the difference is not significantly larger than the
uncertainties in the measurements.
\section{Conclusion}

Measurements of the \Zgee and \Zgmm cross-sections, differential in
the transverse momentum of the \Zboson{} boson, have been performed in a
fiducial volume defined by $\pt^\ell > 27$~\GeV, $|\eta_\ell| < 2.5$ and
$66 < \Mll < 116$~\GeV, using $36.1~\ifb$ of data from
proton--proton collisions recorded in 2015 and 2016 at a
centre-of-mass energy of 13~\TeV{} with the ATLAS experiment at the
LHC. This data-set allows coverage of a kinematic range up to the $\TeV$-range. The cross-section results from the individual channels were combined and
good agreement between the two was observed. The relative precision of
the combined result is better than 0.2\% for $\Zpt{}<30\,\GeV$, which
provides crucial information to validate and tune MC event generators
and will constrain models of vector-boson production in future
measurements of the $W$-boson mass.

The integrated cross-section measurements are compared with fixed-order perturbative QCD predictions. Differential spectra in \Zpt{} and
\phiStar{} are compared with a selection of calculations implementing
resummation and non-perturbative effects through parton showers or
analytic calculations. The predictions based on the \PYTHIA{}8 parton
shower with parameters tuned to 7 \TeV{} data are found to describe
the 13 \TeV{} data well at low \Zpt{} and \phiStar{}. The \SHERPA
prediction based on merging of higher-order, high-multiplicity matrix
elements gives an excellent description of the data at high $\Zpt$,
while the very accurate \textsc{RadISH} NNLO+N$^3$LL prediction agrees
with data for the full spectrum. The fixed-order \textsc{NNLOjet}
prediction with and without NLO EW effects describes the data well for high $\Zpt$.

\clearpage
\section*{Acknowledgements}

We thank CERN for the very successful operation of the LHC, as well as the
support staff from our institutions without whom ATLAS could not be
operated efficiently.

We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZ\v{S}, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Sk{\l}odowska-Curie Actions and COST, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; The Royal Society and Leverhulme Trust, United Kingdom.

The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref.~\cite{ATL-GEN-PUB-2016-002}.

\printbibliography

\clearpage \input{atlas_authlist}
\end{document}

redpajama_set_name: RedPajamaArXiv


Cargill is expanding its portfolio of label-friendly functional native starches with the addition of three potato starches designed for meat, meat alternatives and culinary applications.
Designed for mild processing conditions, these starches offer superior viscosity and improved sensory experience, without compromising on taste and appearance.
The products will be available worldwide under the SimPure brand.
Designed for use in low-processing conditions, Cargill's newest SimPure starches offer food developers a range of options for both instant and cook-up applications.
SimPure 99500 creates firm and meaty textures without the salty taste as a result of its water binding capabilities.
This cook-up starch is a cost effective texturizer designed for meat and meat alternatives.
SimPure 99530, a functional cook up starch, delivers superior dispersibility, ideal for use in dehydrated culinary and dairy applications for immediate consumption.
Easy to use in both industrial and home settings, it has a delayed but full viscosity development, which means no lumping when mixed with hot water and top quality texture in the final product.
SimPure 99570 and 99571 are two new instant viscosifying starches designed for use in cold prepared soups, sauces, desserts and fillings.
These functional starches differ in particle size to offer either a smoother texture (fine particle size) or a pulpier texture (coarser particle size).
The SimPure native starch portfolio was launched in October 2017 to address consumer demand for label-friendly products.
The first tailor made starch, SimPure 99560, was designed to effectively replace modified starches in frozen meals and slow cooked meats.
Earlier this year, Cargill and its Danish potato starch partner, AKV Langholt AmbA,- invested in a new potato starch production unit at their Langholt facility in Denmark which was officially inaugurated on the 24th of August 2018.
The new SimPure products have been produced at this state-of-the-art processing facility, with more products in development.
AKV Langholt (Andelskartoffelmelsfabrikken Vendsyssel) is a Danish manufacturer of Potato Starch. AKV Langholt AmbA operates a Joint Venture with Cargill for the production of modified potato starch for non food applications. AKV's potato starch and potato protein are also sold through Cargill's distribution network.
To the snack industry and for the preparation of french fries, Cargill supplies Frying Oils and a range of Ingredients, including texturizers, flavor systems and salt. Frying oil brands include Clear Valley and eLitra high oleic canola oils.


US - The US Environmental Protection Agency (EPA) recently released a blog on going meatless. Bob Stallman, President of the American Farm Bureau Federation, has expressed concern regarding the contents of the blog post.
Mr Stallman said that he is concerned with the recent blog post by an EPA intern on the agency's official blog, "Greenversations." In the post, the intern promotes going meatless because of environmental and animal welfare concerns.
"Interns like all Americans are entitled to their own opinion on subjects being discussed in public forums. But they, like all government employees, should have an understanding that they are not just representing themselves, they are representing the particular government agency – in this case the EPA," said Mr Stallman.
"While this is a position taken by an intern of the agency, EPA should control its blog space. What is written on its blog comes across as its official position toward farmers and ranchers that it regulates and shows a terrible disregard for them and the agriculture industry," Mr Stallman concluded.


text:
Investment Policy Statement
"Dirty Dozen" Tax Scams to Watch For
Every year the Internal Revenue Service (IRS) releases its list of tax scams, spotlighting the myriad ways that people try to separate you from your money.¹
Using your personal information, an identity thief can file a fraudulent tax return and claim a refund. If you've been a victim of stolen personal information, you can contact the IRS so the agency can protect your tax account.
Be wary of fake emails or websites looking to steal your personal information. If you receive a request for information that appears to be from the IRS, contact the IRS directly to verify the request.
Telephone Scams
Scammers will contact you pretending to be from the IRS. They may say that you are due a large refund or owe money (even threatening arrest or revocation of your driver's license). If you receive such a call, call the IRS and contact the Federal Trade Commission using their "FTC Complaint Assistant" at FTC.gov.
Inflated Refund Claims
Tax preparers promising inflated returns may ask clients to sign a blank return or charge fees based on a percentage of the refund. Beware of phony storefronts or preparers advertising through word-of-mouth to community groups where trust is high.
Return Preparer Fraud
Dishonest preparers may use tax preparation as an excuse to steal your personal information, so only use a preparer who signs the return and has an IRS Preparer Tax Identification Number.
Hiding Income Offshore
The IRS has strengthened its ability to identify offshore holdings, and the failure to report them will be costly.
Impersonation of Charitable Organizations
Fraudulent charities raise money or obtain private information from individuals looking to help. Donate only to recognized charities, and beware of charities whose names sound similar to the well-known ones.
False Income, Expenses or Exemptions
Falsifying your tax return is a high risk, low reward exercise, especially in this age of Big Data.
Frivolous Arguments
Ignore promoters of frivolous arguments that promise you tax relief. Not only are they expected to fail, but you may be subjected to penalties and possible jail time.
Falsely Padding Deductions or Returns
Dishonestly reporting deductions to reduce tax bills or inflate refunds may open you up to penalties and prosecution.
Abusive Tax Structures
If someone is proposing to eliminate or substantially reduce your taxes through complex tax structures, walk away—they may be offering nothing more than illegal tax evasion.
Excessive Claims for Business Tax Credits
This happens when taxpayers or their tax preparers improperly claim the research credit or the fuel tax credit, which is generally limited to off-highway uses, such as farming.
Tax Reform: Before and After
What did the 2017 Tax Cuts and Jobs Act change? Here's a quick snapshot.
Klauenberg Retirement Solutions
14502 Greenview Drive
Securities offered and sold by and through CoreCap Investments, Inc., a registered broker-dealer and member FINRA / SIPC. CoreCap Investments and Klauenberg Retirement Solutions are separate and unaffiliated companies. CoreCap Investments Core Capital Advisors 4000 Town Center, 11th Floor Southfield, MI 48075 Direct: 248-784-1341 Main: 888-296-3360 Fax: 248-784-1459.
The registered representatives associated with this site may only discuss and/or transact securities business with residents of the following states: Arizona, California, Colorado, Delaware, District of Columbia, Florida, Georgia, Maryland, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, Washington, West Virginia.

redpajama_set_name: RedPajamaCommonCrawl


text:
Simon Chin Net Worth & Insider Trades
Simon Chin - Chief Executive Officer, Iris Biotechnologies Inc
What is Simon Chin's Net Worth?
The current estimated net worth of Iris Biotechnologies Inc's Chief Executive Officer, Simon Chin, is estimated to be about $2.86M . Simon Chin owns about 12,680,563 units of Iris Biotechnologies Inc common stock. In the last 2 years at Iris Biotechnologies Inc, Simon Chin has sold an estimated value of $14.39K worth.
What is Simon Chin's Past Insider Trading?
Simon Chin's largest purchase order was 1,389,089 units , worth over $83.37K on December 30, 2015. Simon Chin's largest sale order was 144,281 units , worth over $14.39K on December 2, 2016. In total, Simon Chin has made about 18 transactions over 2 years of their time at Iris Biotechnologies Inc. Simon Chin usually trades in December, with the busiest year in 2015.
Simon Chin
Ticker: irsbInsider CIK: 0001436579Group By: filingSort By: last_filing_date
What is Simon Chin's' Mailing Address?
Mailing address is C/o Iris Biotechnologies Inc. 5201 Great America Parkway, Suite 320 Santa Clara CA 95054 CA
What are Iris Biotechnologies Inc's Past Insider Trades?
Iris Biotechnologies Inc's most recent insider trade came on December 2, 2016 by Simon Chin who sold 144,281 units worth $14.39K . In the last year, insiders at Iris Biotechnologies Inc have sold an estimated value of $14.39K and bought an estimated value of $726.26K worth of shares. Insider trading is most common in December, with the busiest year in 2015. The most active traders at the company are Simon Chin, Chief Executive Officer, and Douglas Hendren, Director .
IRIS BIOTECHNOLOGIES INC Insider Trades
Trade Types: p, sTicker: irsbGroup By: filingSort By: last_filing_date


text:
All Souls Religious Studies
Sitemap URLs
Ernie Ford Hymns
June 7, 2019 All Souls Religious Studies admin
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Jeremy Mayfield was one driver who returned to the garage area early in the initial Happy Hour session for major adjustments to his Mobil 1 Ford. He didn't even try. we include MRN Studios in.
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Please purchase a subscription to continue reading. A subscription is required to continue reading. Thank you for reading 10 free articles on HeraldCourier.com. You can come back at the end of your 30.
The Ford Show (also known as The Ford Show, Starring Tennessee Ernie Ford and The Tennessee Ernie Ford Show) is an American variety program, starring singer and folk humorist Tennessee Ernie Ford, which aired on NBC on Thursday evenings from October 4, 1956, to June 29, 1961. The show was sponsored by the Ford Motor Company, whose founders shared a last name with the host but had no.
Previous inductees include Elvis Presley, Mahalia Jackson, Keith Green, Amy Grant, the Blind Boys of Alabama, Andrae Crouch, Sandi Patty, Vestal Goodman, Tennessee Ernie Ford, 2nd Chapter of Acts, The.
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Kaiser has arranged and produced albums for many gifted artists, including Kathleen Battle, Ernie Ford, Larnelle Harris. He received a Dove Award for his album Psalms, Hymns, and Spiritual Songs.
Aug 21, 2008 · Jim Reeves singing May The Good Lord Bless and Keep You. This one is specially for you Kathryn.
Brother Raul and friend Ernie wanted to see the films too. (The final scene has the three stars reunited on Nob Hill singing hymns along with other survivors.) John Ford's The Hurricane (1937).
v David Krakauer, 8 p.m.: The renowned clarinetist and his band, Ancestral Groove, in concert ($34, $28 for senior citizens) at Ford Family Recital Hall. works and arrangements of traditional.
A versatile, deep-voiced performer, Tennessee Ernie Ford became the model for future generations of. pop-oriented material as well as record gospel LPs like the top-selling Hymns, all while.
An agency of The United Methodist Church, Discipleship Ministries helps local church, district, and conference leaders fulfill the shared dream of making world-changing disciples. The agency connects leaders with needed resourcing, training, consulting, and networking that support spiritual formation, new church development, revitalization of local churches, and materials for use in Central.
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Some of Clarence's favorite hymns are "What a Friend We Have in Jesus", "Jesus Loves Me", and "Amazing Grace" which he learned as a young boy. He often recited the Lord's Prayer and during WWII, he.
Also among the 25 recordings recognized this year and unveiled Wednesday are the timeless hits "You've Lost That Lovin' Feelin'" by The Righteous Brothers, "Sixteen Tons" by Tennessee Ernie Ford and.
You are the owner of this article. Edit Article Add New Article Welcome, Guest.
D I S C L A I M E R I am occasionally asked if it's legal to download songs found on various sites such as this one. Well, I'm neither a lawyer nor a technician, and I've begun to wonder if it was legal for us to tape music off the radio back when tape recorders first came into existence.
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Featuring early 20th century American music (1900-1922) from the Dover Publications' 'Favorite Songs of the Nineties' and 'Song Hits from the Turn of the Century.'
Tennessee Ernie Ford, radio announcer, singer, and television personality, was born Ernest Jennings Ford on February 13, 1919, in Fordtown, Sullivan County, and raised in nearby Bristol. Ford began a radio career at Bristol's WOPI, where he worked as an announcer before studying voice at the Cincinnati Conservatory of Music.
Say A Prayer For You Alma Redemptoris Mater. Gracious Mother of our Redeemer, for ever abiding. Heaven's gateway, and star of ocean, O succour the people, Who, though falling, strive to rise again. St Thomas More Catholic Church Coventry Image caption Child sex abuse victims protested as the Pope toured
Explore Norma Clemente's board "Great Hymns and Gospel Songs by Tennessee Ernie Ford", followed by 212 people on Pinterest. See more ideas about Tennessee ernie ford…
Sep 30, 2009 · The Barn Again Gang is excited that Christ died for their sins. Because He did, they know that they are assured of heaven. They want to share that news in.
Will Smith On Religion 31 Angle Street, Hamilton. • Marsden First United Methodist Church: Join our women's ministry and guest speaker Rev Lashonna Smith for codfish breakfast at 10am. Tickets, $15, available from committee. 53 South Road, Smith's. • Wesley Methodist Church: Join us at 6.30am for sunrise worship.
Ford Huskey is pastor. Cedar Grove Baptist Church will have. will be featured during the afternoon and evening concerts including The Gaither Vocal Band, Ernie Haase and Signature Sound, The Dove.
If anyone would like to rent a table for £5 to sell their own things, phone Di Ford on 01723 859211. The choir was established in 1984 by Ernie Stear and a small number of local residents,
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"Sixteen Tons" is a song written by Merle Travis about a coal miner, based on life in coal mines in Muhlenberg County, Kentucky. Travis first recorded the song at the Radio Recorders Studio B in Hollywood, California, on August 8, 1946. Cliffie Stone played bass on the recording. It was first released in July 1947 by Capitol on Travis's album Folk Songs of the Hills.
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Their first public appearance will be at the "Hymns of Hope" program April 30. 10:30 a.m. at the church, 1780 Rocky Ford Road. Nursery is provided during the cantata. Everyone is welcome. First.
If you opened this page from a search engine: Visit The-Record-Collector.com main page. The Record Collector in Bordentown, NJ! Voted as the favorite independent record store in the tri-state area;
Hymns is an album recorded by Tennessee Ernie Ford that was released in 1956. It was the second best selling record in the United States in 1957. The album is one of the best selling of all time and spent 277 weeks on the Billboard 200. The album appeared the same year that Ford launched his 5-year NBC television series, The Ford Show, Starring Tennessee Ernie Ford.
Making the evening even more interminable is the interweaving of these bits with smugly simplistic sermonettes delivered by Ernie Banks and Marilyn Coleman. which consists of fragmentary hymns that.
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Following Alibaba's decision about where to list, Jeffrey Goldfarb and Antony Currie discuss why a new stock issuer picks one exchange over another, when shares can be traded most anywhere.
Big news that Alibaba has chosen New York Stock Exchange for its listing everyone's anticipating. We start to wonder why it was a big news that they -- premier stock exchange. -- -- -- -- the NASDAQ answer me this rivalry is storage but what's the difference between western of one or someone that's at what becomes a listing -- listing and having lost the cup that soul. In it views right it's basically zero there is no different than mr. via their views didn't really he's looking at have a -- how two to mountain's main markets. So -- exchange was always specialist based people on the floor of the stock exchange as it is being front -- testimony year old right. The -- that NASDAQ has always been a truck -- had its own from running in market making progress in the nineties. But now -- -- exchanges -- I always -- total liquidity of -- or the other apps that -- that if I can both treat each other stocks you can easily switch from one to the other that he really wants to know we're just off exchange anyway thank you to the latest that was an attempt last week has some NASDAQ -- have changed just under 25 -- -- trading volume. Going through them. 3638%. Is going through intimidation engines -- investment banks. -- -- and even the biggest asset at least two questions really so what are the exchanges telling companies that they can offer them what do companies really see and pick flooding. Well one thing if you're the New York Stock Exchange going up to -- -- but there's one thing people we don't need to say which is to. FaceBook again. And it's really sick at a Bob's going to be -- say that there are. Arguably legitimate concerns about whether NASDAQ is able to ensure -- that's so stop listing of the site now whether. Whether those concerns -- -- is not a massive publicity or come to be figured that I wanna play. -- accidentally great talking point if you also change or hinting pointedly switch but there are some there are some differences that may be at the margins but there are some things that yet if you -- photos of -- festival running price. I think I'm a big deal -- Bob Marshall by Sunday -- -- -- this both smaller deals in night out from what we've had so units and its issues. May will be the NASDAQ is it cheaper. NASDAQ certainly has made a -- at play full. Investor relations web sites and and other corporate resources services related which is really nice for. Tech companies in particular civil company and remember that they pulled a company a found very it's also what is -- to help Altman not avenue -- change. Hasn't offering is what people talk about an -- for more I think probably because they -- smaller companies. But then he gets the nitty gritty what is that the executives greedy wanna have well if you get them right. Tickets and write something -- -- -- the tickers and it could be a solid and that this historian for the years that that you'll still be -- for example. Kept their lesson and hoping frequent that Microsoft. Would one base which merit. I'd say this this'll save our siblings getting in the -- access to markets across the street that they do very good job of getting a bell and just you know looking at anything. Right it can get like he's got that I don't want to flicks at it's it's still a much much as -- hunting union near demonstrating -- flow for us it's yet. They still got -- the -- and it hasn't saying we're all on the floor of its 200 plus year old -- -- A summons came when he does come down to the simple things aren't we will leave it there -- -- -- all listings coming out about corporate news tomorrow.


text:
31,841 total votes
The Birds is a 1963 suspense/horror film directed by Alfred Hitchcock, loosely based on the 1952 story "The Birds" by Daphne du Maurier. It depicts Bodega Bay, California, which is, suddenly and for unexplained reasons, the subject of a series of widespread and violent bird attacks over the course of a few days. The film features the screen debut of Tippi Hedren. It also starred Rod Taylor, Jessica Tandy, Suzanne Pleshette, and Veronica Cartwright. The film was written by Evan Hunter. Hitchcock told him to develop new characters and a more elaborate plot, keeping du Maurier's title and concept of unexplained bird attacks. ... more on Wikipedia
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text:
package aashrai.android.gettowork.presenter;

import aashrai.android.gettowork.R;
import aashrai.android.gettowork.di.MainActivityScope;
import aashrai.android.gettowork.utils.AccessibilityChecker;
import aashrai.android.gettowork.utils.Constants;
import aashrai.android.gettowork.utils.Utils;
import aashrai.android.gettowork.view.MainActivityView;
import aashrai.android.gettowork.view.activity.CreditActivity;
import android.content.Context;
import android.content.DialogInterface;
import android.content.Intent;
import android.content.SharedPreferences;
import android.net.Uri;
import android.provider.Settings;
import java.util.Set;
import java.util.concurrent.TimeUnit;
import javax.inject.Inject;

@MainActivityScope public class MainActivityPresenter implements DialogInterface.OnClickListener {

  private final Set<String> activatedPackages;
  private final SharedPreferences sharedPreferences;
  private final Context context;
  private final AccessibilityChecker accessibilityChecker;
  private MainActivityView mainActivityView;

  @Inject
  public MainActivityPresenter(Set<String> activatedPackages, SharedPreferences sharedPreferences,
      Context context, AccessibilityChecker accessibilityChecker) {
    this.activatedPackages = activatedPackages;
    this.context = context;
    this.sharedPreferences = sharedPreferences;
    this.accessibilityChecker = accessibilityChecker;
  }

  public void setView(MainActivityView mainActivityView) {
    this.mainActivityView = mainActivityView;
  }

  public void checkAccessibilityEnabled() {
    if (!accessibilityChecker.isAccessibilityEnabled(context)) {
      mainActivityView.showAccessibilityDialog();
    }
  }

  public void onAppLockActivateClick() {
    boolean appLockActivate = Utils.isAppLockActivated(sharedPreferences);

    if (!appLockActivate) {
      sharedPreferences.edit().putBoolean(Constants.APP_LOCK_ACTIVATED, true).apply();
      checkAndActivateAppLock();
    } else {
      showWarning();
    }
  }

  private void showWarning() {
    mainActivityView.hideActivateButton();
    mainActivityView.showWarningText();
    mainActivityView.hideActivateHeader();
    mainActivityView.setWarningText(Constants.PAUSE_WARNING_MESSAGE_FIRST);
  }

  public void onTimingClick(String timing) {
    sharedPreferences.edit().putBoolean(Constants.APP_LOCK_ACTIVATED, false).apply();
    storeTiming(timing);
    mainActivityView.showToast(Constants.OVERLAY_DEACTIVATED_MESSAGE);
    restoreToDefault();
    launchHomeScreen();
  }

  private void restoreToDefault() {
    mainActivityView.hideTimingGrid();
    mainActivityView.showActivateButton();
    mainActivityView.showActivateHeader();
    mainActivityView.setActivateDrawable(
        Utils.createVectorDrawable(mainActivityView.getActivityContext(),
            R.drawable.ic_play_circle));
  }

  void storeTiming(String timing) {
    long millis = getMillis(timing);
    sharedPreferences.edit()
        .putLong(Constants.OVERLAY_DEACTIVATED_TIMESTAMP, System.currentTimeMillis())
        .apply();
    sharedPreferences.edit().putLong(Constants.OVERLAY_DEACTIVATED_MILLIS, millis).apply();
  }

  long getMillis(String timing) {
    String[] timeAndUnit = timing.split(" ");
    String time = timeAndUnit[0];
    String unit = timeAndUnit[1];
    long millis = 0;
    if (unit.toLowerCase().equals("min") || unit.toLowerCase().equals("mins")) {
      millis = TimeUnit.MINUTES.toMillis(Long.valueOf(time));
    } else if (unit.toLowerCase().equals("hr") || unit.toLowerCase().equals("hrs")) {
      millis = TimeUnit.HOURS.toMillis(Long.valueOf(time));
    }
    return millis;
  }

  public void onWarningTextClick() {
    String text = mainActivityView.getWarningText();
    if (text.equals(Constants.PAUSE_WARNING_MESSAGE_FIRST)) {
      mainActivityView.setWarningText(Constants.PAUSE_WARNING_MESSAGE_SECOND);
    } else {
      mainActivityView.hideWarningText();
      mainActivityView.showTimingGrid();
    }
  }

  private void checkAndActivateAppLock() {
    mainActivityView.setActivateDrawable(
        Utils.createVectorDrawable(mainActivityView.getActivityContext(),
            R.drawable.ic_pause_circle));

    if (activatedPackages.size() == 0) {
      mainActivityView.showToast(Constants.ADD_APPS_MESSAGE);
      launchSettingsActivity();
    } else {
      mainActivityView.showToast(Constants.OVERLAY_ACTIVATED_MESSAGE);
      launchHomeScreen();
    }
  }

  private void launchHomeScreen() {
    mainActivityView.launchActivity(Utils.getHomeScreenIntent());
  }

  private void launchSettingsActivity() {
    mainActivityView.launchActivity(Utils.getSettingsActivityIntent(context));
  }

  public void onCreditTextClick() {
    Intent intent = new Intent(context, CreditActivity.class);
    mainActivityView.launchActivity(intent);
  }

  public void onGithubClick() {
    Intent intent = new Intent();
    intent.setAction(Intent.ACTION_VIEW);
    intent.setData(Uri.parse("https://github.com/aashrairavooru/GET-TO-WORK"));
    mainActivityView.launchActivity(intent);
  }

  @Override public void onClick(DialogInterface dialog, int which) {
    Intent intent = new Intent(Settings.ACTION_ACCESSIBILITY_SETTINGS);
    mainActivityView.launchActivity(intent);
    mainActivityView.showToast(String.format("Enable for %s",
        context.getResources().getString(R.string.accessibility_name)));
  }
}

redpajama_set_name: RedPajamaGithub


Submitted by Christopher Duggan Photography. Originally published on June 22nd, 2016.
A word from the photographer: Charlotte & Scott's Seranak wedding at Tanglewood in the Berkshires was absolutely gorgeous with a magnificent view. They had their ceremony outside during a stunning sunset, so we were able to photograph them with an incredible backdrop.
There were flowers everywhere. It was beautiful. Charlotte's bouquet and all the floral design was created by Carolyn Valenti Flowers. Charlotte & Scott had floral arrangements throughout the venue and on the cake. Every now and then you see a cake with flowers, but this one really stood out to my team, because it was bright and eye-catching and abundant.
The bride's nieces were three of four flower girls, and they were all adorable.
The band that played during the reception was called The Sweetbeats. They were great, and their music had everyone dancing all evening. It's always fun to see everyone on the dance floor having the time of their lives. During the reception, Charlotte & Scott's friends and family said such wonderful things about them in their toasts. A lot of them mentioned how the couple really enjoys the same activities and spending time together hiking, biking, rock climbing and running.
As always, I am very grateful to have been a part of Charlotte & Scott's wedding day. Congratulations!!


The industry must reduce the consumption energy, water, material and land in existing and new buildings and through embodied energy and running energy costs.
For example, 80% of energy consumed during the whole life cycle of a building occurs during its service life. Equally, household and services are the third largest source of C02 emissions in the EU-15.
Greater efficiency must be sought in the amount of raw material used and waste produced; in the energy content of construction materials; and in a sustainable approach to land use. The sector must respond to the imminent and growing threat of irreversible climate change.
Efficient and renewable energy concepts, technologies and tools for new and existing buildings.
Zero energy consuming and CO2 emitting buildings.
Environmentally friendly manufacturing technologies for construction materials and components.
Training, dissemination and legislation to support reduced energy consumption.