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Int J Adult Orthodon Orthognath Surg. 2001;16(2):108–20. 143. Shell TL, Woods MG. Facial aesthetics and the divine proportion: a comparison of surgical and non-surgical class II treatment. Aust Orthod J. 2004;20(2):51–63. 144. Bashour M. An objective system for measuring facial attractiveness. Plast Reconstr Surg. 2006;118(3):757–74; discussion 75—6. 145. Kim YH. Easy facial analysis using the facial golden mask. J Craniofac Surg. 2007;18(3):643–9. 2 Objective Aspects of Beauty 43 146. Marquardt SR.
Penton-Voak IS, Jones BC, Little AC, Baker S, Tiddeman B, Burt DM, et al. Symmetry, sexual dimorphism in facial proportions and male facial attractiveness. Proc Biol Sci. 2001;268(1476):1617–23. 63. Grammer K, Thornhill R. Human (Homo sapiens) facial attractiveness and sexual selection: the role of symmetry and averageness. J Comp Psychol. 1994;108:233–42. 64. Hume DK, Montgomerie R. Facial attractiveness signals different aspects of "quality" in women and men. Evol Hum Behav. 2001;22(2):93–112.
2004;15(5):864–71. 49. Borod JC, Bloom RL, Brickman AM, Nakhutina L, Curko EA. Emotional processing deficits in individuals with unilateral brain damage. Appl Neuropsychol. 2002;9(1):23–36. 50. Pflüger LS, Oberzaucher E, Katina S, Holzleitner IJ, Grammer K. Cues to fertility: perceived attractiveness and facial shape predict reproductive success. Evol Hum Behav. 2012;33(6):708–14. 51. Gangestad SW, Thornhill R, Yeo R. Facial attractiveness, developmental stability, and fluctuating asymmetry. Ethol Sociobiol.


text:
\section{Introduction}

Gravitational lensing of an object by mass along the line of sight produces
two measurable effects: shape distortion and magnification of the source.
It is usually impossible to measure the level of magnification
since one typically does not know the intrinsic flux of the source.
Because of this
fundamental problem, the field of weak lensing has focused on shape
measurements of sources since
one can usually assume that galaxies have no preferred orientation.
(For a review of gravitational lensing theory and techniques, see
\citealt{schneider04}.)
However, if the intrinsic luminosity of sources can be estimated,
then lensing magnification can be used to study large scale structure.

Recently, large scale sky surveys have become efficient and prolific
enough such that variability studies can be conducted on large samples
of rare objects.  For example, ensembles of type I (broad-line)
quasars have been
studied for the purpose of probing the physical causes of their common
and dramatic variability (e.g., \citealt{vandenberk04}, \citealt{devries05},
\citealt{wilhite08}, \citealt{bauer09a}, \citealt{kelly09}, \citealt{macleod10},
\citealt{meusinger11}).
In these works, the mean ensemble variability amplitude has been seen to correlate
with a number of properties of the objects: e.g., time scale of the variability,
wavelength of observation, redshift, mass, and luminosity of the systems.
In particular, a strong anti-correlation
has repeatedly been observed between variability amplitude and quasar luminosity,
particularly when comparing quasars of similar mass.

Given a measurement of a quasar's variability amplitude,
one can estimate its luminosity using this empirical relation.
If the quasar were gravitationally lensed by intervening mass,
the magnification effect would alter the observed luminosity.  However,
the fractional variability would remain unaffected, as the magnification,
which is multiplicative on the luminosity, cancels.  Therefore,
magnification due to gravitational lensing will shift the quasar's position
in variability-luminosity space.  Quantification of this shift
constitutes a measurement of the quasar's lensing magnification.
Because the variability-luminosity relation has a large intrinsic variance,
the estimate of a single quasar's magnification is not precise.  However,
because the relation is well-determined in the mean, an ensemble of quasars can
yield a significant measurement of the lensing effect.

In this paper we
take advantage of the variability-luminosity relation seen in type I
quasars to measure their magnification due to galaxy clusters
along the line of sight.
We use the Palomar-QUEST Variability Survey to measure the lensing
magnifications of 3573 quasars, and compare the signal to that
expected from gravitational lensing due to the 13,823 galaxy clusters in
the Sloan Digital Sky Survey MaxBCG catalog (\citealt{maxbcg}).
We measure an average cluster profile shape that is consistent with previous
studies of the MaxBCG cluster catalog, showing that this method of
measuring lensing magnification is
indeed effective.

\section{AGN Structure and Quasar Optical Variability}

An active galactic nucleus (AGN) is typically described as having
several prominent structural components.  The central black hole is
fed by an accretion disk, which radiates a continuum of flux
primarily in the optical and UV bands.  Offset from the
plane of the disk
exist clumps of gas which absorb the disk's flux and
reradiate it as broad emission lines (hence the name broad line
region (BLR)).  Emission from the disk and the BLR constitute
the majority of the optical flux observed in type I quasars,
which are the highly luminous AGN for which the observer has a
direct line of sight to these two structures.  (Type I
quasars will hereafter be referred to simply as quasars.)
The detailed
geometry of the disk and BLR are not well understood;  they
may be part of one continuous structure (e.g. \citealt{nicastro03},
\citealt{elvis00}, \citealt{lovegrove10}), but they traditionally
are described as being distinct.
A minority of quasars also show evidence
of radio emission from a jet.  The fraction of quasars
that have strong radio emission (i.e. are radio loud) is
uncertain;  estimates range
from roughly 5 to 25\% \citep{zamfir08}.
Jet emission can account for
a portion of a quasar's optical flux, but the fraction of optical
flux that is jet-based depends strongly on the properties and
orientation of the particular object.

Radio-loud quasars are on average more
optically variable than radio-quiet quasars;  in particular, their
short-timescale variability is enhanced (e.g., \citealt{kelly09}).
This distinction implies that
the jet contributes to the optical variability in these systems, most
likely due to the dissipation of shocks.

Not all quasar variability is due to jet physics;  quasars with
no evidence of a jet also exhibit significant flux variations.
\cite{sesar07}, using the Sloan Digital Sky Survey (SDSS), found that
at least 90\% of quasars are variable by at least 0.03 magnitudes
on timescales of up to several years.  The variability of
individual nearby radio-quiet quasars (and their lower-luminosity
counterparts, Seyfert I galaxies) have been studied in detail using the
technique of reverberation mapping (e.g. \citealt{blandford82},
\citealt{bentz09}):  spectra of the AGN are regularly obtained,
and the variability of the spectral features is measured as a function
of time.  Such monitoring has revealed that the disk continuum
fluctuations are followed by fluctuations in the broad emission lines
that echo the continuum variation.  The time lag between the continuum
and broad line variability constitutes a measurement of the BLR size,
which is typically on the order of light-days.  These measurements
show that the primary source of variability in these objects is
inside the BLR, presumably from the accretion disk and instabilities
therein.

Accretion disk instabilities have long been a prime suspect as the
main source of quasar variability.  \cite{kawaguchi98} calculated
the expected relationship between variability amplitude and timescale
based on a model of disk instabilities, as well as a
relation expected from starbursts
in the quasar host galaxy.  \cite{hawkins02} extended this work
to include a prediction based on microlensing of the quasars.  A
number of studies of ensemble quasar variability have measured the
average variability amplitude versus timescale relation, which best agrees
with the accretion disk instability calculation
(\citealt{collier01}, \citealt{vandenberk04}, \citealt{devries05},
\citealt{wilhite08}, \citealt{adam}, \citealt{bauer09a}, \citealt{meusinger11}).
However, this type of analysis would benefit from updated predictions
using more complex models.

Disk instability models invoke relatively small, short-lived flares from
numerous instabilities that combine to produce a
stochastic light curve.
These fluctuations could be prompted by changes
in, for example, the accretion rate (e.g., \citealt{li08}) or in a
magnetic field (\citealt{hirose09}).
Stochastic light curves have been succesfully used to describe
optical observations of large samples of quasars (\citealt{kelly09},
\citealt{kozlowski10}, \citealt{macleod10}).  These works model the
quasar light curves as a damped random walk, using only three parameters:
the typical amplitude of the short-timescale variability, the damping
timescale, and the average flux of the light curve.
Such light curves,
generated from a constant component plus similar flares with
random start time, follow the variability-luminosity relation
\begin{equation}
\frac{\Delta L}{\overline{L}} \propto \overline{L}^{-\delta},
\label{poissonian_equ}
\end{equation}
where $L$ is the object's luminosity and $\delta$ depends on the
details of the flares (\citealt{cidfernandes96}, \citealt{cidfernandes00}).
Qualitatively, the anti-correlation simply notes that, given similar
flare properties between two quasars, the fractional variability
$\Delta L / \overline{L}$ will be smaller in the quasar whose
mean luminosity $\overline{L}$ is larger.
This prediction indeed describes the variability-luminosity
anti-correlation seen in quasar ensembles (\citealt{vandenberk04},
\citealt{bauer09a}).

Alternatively, it is possible that much of the observed relationship
between variability
and luminosity is a secondary effect of a correlation between,
for example, variability and the quasar's Eddington ratio.
The Eddington ratio of a quasar describes its accretion rate, and
can be estimated as a ratio between the quasar's luminosity and its
mass.  \cite{ai10} find the correlation between Eddington ratio
and variability to be more significant than that
between luminosity and variability, using a sample of several hundred
SDSS Seyfert I galaxies.  The authors note the positive correlation
between the accretion rate and the disk radius which emits at a
given wavelength.  Therefore an anti-correlation between variability
amplitude and Eddington ratio implies that the outer regions of the
disk show less variability than the inner radii (regardless of the
physical mechanism behind the variability).  This position-dependence
qualitatively agrees with the observed anti-correlation between
variability and wavelength (\citealt{wilhite05}), if
the hotter, bluer, inner areas of the disk vary more strongly than
the redder, outer regions.  The variability amplitude may be
anti-correlated with the quasar's luminosity via such a positional
dependence, and therefore through the definition of the
Eddington ratio as related to the luminosity.
As described in section \ref{mu_meas_section},
we normalize the quasar variability measurements according
to the objects' physical properties, including black hole mass.
This procedure effectively allows us to measure how the variability
amplitude scales with luminosity, for quasars of similar mass.  Holding
the mass constant in this way allows the variability-luminosity
relation to contain the same information as the correlation between the
variability and the Eddington ratio.

\section{Measuring $\mu$} \label{mu_meas_section}

The average amplitude of quasar variability has been seen
to depend on several factors: time lag between measurements,
luminosity of the quasar
(\citealt{vandenberk04}, \citealt{devries05}, \citealt{wilhite08},
\citealt{bauer09a}, \citealt{macleod10}, \citealt{meusinger11}),
wavelength of observation (\citealt{vandenberk04}, \citealt{devries05},
\citealt{meusinger11}),
and  black hole mass (\citealt{wilhite08}, \citealt{bauer09a}).
The dependence of variability amplitude on redshift is less obvious;
\cite{vandenberk04} measured a slight increase in the variability
with redshift, while \cite{devries05} measured a slight decrease.
More recently, \cite{macleod10} and \cite{meusinger11} have measured no
significant dependence of variability amplitude on redshift.

In practice, the variability-luminosity trend measured is of the form:
\begin{equation}
\log(V) = C - \alpha \times \log(L_{\mathrm{meas}}).
\label{v_vs_l_equation}
\end{equation}
A linear relation has indeed been observed (\citealt{vandenberk04},
\citealt{bauer09a}), although there are conflicting results for the
value of the power-law slope $\alpha$, perhaps due to
selection effects.
If faint quasars are included in the analysis for which one cannot
observe the full extent of the variability, the measured slope will
become artificially shallow;  this effect most clearly manifests itself
as a flattening in the relation at the lowest observable luminosities,
as is illustrated in figure 5 of \cite{bauer09a}.  The low-luminosity
limit of the binning scheme in this work is chosen to exclude this
regime from the data set.  The value of the constant $C$
depends on the details of the normalization of the data, as described
below.

When studying how the variability
of a large quasar sample depends on one of the quasars'
properties, we must treat the parameters as independently as possible.
To this end, we use a method
introduced by \cite{vandenberk04} and adopted in \cite{bauer09a}.
Four basic quantities are known for all of the quasars in our sample:
time lag between measurements $\tau$, luminosity $L$, estimated black
hole mass $M$, and redshift $z$.  There are known correlations
between all of these parameters, due to physical relationships or
artificial effects such as detection biases in flux-limited surveys.
To avoid these complications and study only the dependence of variability
on luminosity, we would like to identify a set of quasars with identical
properties except for their luminosity, and then examine how the
variability differs between them.  To approximate this procedure, we
have split each parameter's range into bins: 8 bins in $\tau$, 6 bins
in $M$, 6 bins in $z$, and 4 bins in $L$.  The bin limits are given
in table \ref{bin_limits};  quasars with properties outside the given
ranges are not used in the analysis.

\begin{table}
\begin{center}
\begin{tabular}{|llll|}
\hline
$\tau$ & $z$ & $M$ & $L$ \\
\hline
1 & 0.40 & 1 & 30.85 \\
5 & 0.80 & 4 & 31.05 \\
10 & 1.10 & 8 & 31.20 \\
20 & 1.40 & 12 & 31.40 \\
60 & 1.65 & 20 & 31.50 \\
130 & 1.90 & 30 &  \\
160 & 2.20& 75 & \\
220 & & & \\
400 & & & \\
\hline
\end{tabular}
\end{center}
\caption{Bin limits used in determining the normalization constants.  Measurements with values outside the limits are not considered.  Units of time lag $\tau$:
days; black hole mass $M$: $10^{8} \times M_{\odot}$, luminosity $L$: $\frac{\mathrm{erg}}{\mathrm{s} \cdot \mathrm{Hz}}$.}
\label{bin_limits}
\end{table}

To measure quasar variability we use a quantity similar to that of the structure
function.  We define:
\begin{equation}
V = \sqrt{(\Delta m)^{2} - \sigma^{2}},
\label{v_equation}
\end{equation}
where $\Delta m$ is the magnitude difference between two independent
observations of an object, and $\sigma$ is the error on those measurements.
This is similar to the structure function as used in \cite{vandenberk04} and
\cite{bauer09a}; however, here instead of being an ensemble measurement,
one $V$ is measured for each pair of magnitude
measurements of a quasar.  Four measurements of a quasar will
yield six $\Delta m$ measurements, and therefore six different $V$ measurements
for the single quasar.  As $V$ is imaginary when $\Delta m$ is less than the
measurement error $\sigma$, we only use data which show significant ($>1 \sigma$)
variability.  This cut on the data is described further in section \ref{datacuts_section}.

For each multi-dimensional bin, a mean variability amplitude $\overline{V}$ is
determined by taking the mean of all $V$ values measured for that bin.  Then,
holding constant the indices for time lag, mass, redshift, and wavelength,
one can compare the $\overline{V}$ values across the 4 $L$ bins.  This procedure yields
$8 \times 6 \times 6 = 288$ possible 4-point plots of mean variability amplitude
$\overline{V}$ versus luminosity, or 1152 possible $\overline{V}$ values.  Most of the
multi-dimensional bins are not well populated by the quasar sample (for
example, high-redshift low-luminosity bins).  In fact, we obtain 403 bins
with at least 50 measurement pairs, which is the minimum we require in
order to adequately determine $\overline{V}$.  To examine the overall behavior
of $\overline{V}$
with respect to $L$ one can normalize the 4-point measured trends together
and average the resulting normalized data in each $L$ bin to find a
simple, meaningful result of how the variability scales with the quasar
luminosity.  The normalization consists of an additive constant in log($\overline{V}$),
i.e. each 4-point measured trend has its own constant $C$ as defined in
equation \ref{v_vs_l_equation}.
The $\tau$, $M$, and $z$ multi-dimensional bin that has the best
statistics is chosen to be the standard, and the log($\overline{V}$) versus log($L$)
trends from all other $\tau$, $M$, and $z$ bins are normalized to
that standard using one constant offset per $\tau$, $M$, $z$ combination.
The constant is determined by minimizing the chi square difference between
the $\overline{V}$ values from the two datasets in the same $L$ bin, for the $L$ bins
where there exist data from both sets.  For a visual representation of the
normalization procedure, see figure 4 in \cite{bauer09a}.  After averaging
the normalized data, we are left with one $V_{\mathrm{norm}}$ versus $L$ trend with
arbitrary $y$ axis normalization but meaningful slope.  This normalization
technique has been shown to give results for variability amplitude versus time
lag $\tau$ that are
consistent with independent measurements (see table 4 in \cite{bauer09a}.
In this way, we study how the
variability scales with luminosity, comparing only objects with
similar values of the other parameters.
After the normalization, deviations from the mean $V_{\mathrm{norm}}-L$ relation will
not be caused by known, but lensing-independent, correlations such
as that between variability and time lag.
Using the normalization constants calculated in this way, each measured
variability amplitude $V$ is normalized
according to its $\tau$, $M$, and $z$ bin;
the resulting $V_{\mathrm{norm}}$ then can be used to estimate the quasar's
lensing magnification, as detailed below.

We note that, for data taken in a single pass-band, the redshift of the
quasar is degenerate with the rest-frame wavelength observed:  the
effective wavelength of the filter in the quasar's rest frame scales as
$(1+z)$.  Because rest-frame wavelength of observation scales
monotonically with redshift, these two
variables are simultaneously accounted for in our normalization versus redshift.

When fitting for $\alpha$ and $C$ in equation \ref{v_vs_l_equation} , we
find the linear fit that best describes the whole quasar sample.
On average, the measured luminosity $L_{\mathrm{meas}}$ of quasars in the sample
is equal to $\overline{\mu} L_{\mathrm{intrinsic}}$, where $\overline{\mu}$ is
a magnification value representative of the entire sample.  We can therefore
rewrite equation \ref{v_vs_l_equation} as
\begin{equation}
\log(V_{\mathrm{norm}}) = C - \alpha \times \log(\overline{\mu} L_{\mathrm{intrinsic}})
\label{v_vs_l_equation2}
\end{equation}
where we have also explicitly noted the fact that the $V$ measurements
are normalized.

Once we have determined the unique constants $\alpha$ and $C$ for the whole
data set, we can use a measured and normalized $V_{\mathrm{norm}}$ of a quasar to
constrain $\overline{\mu} L_{\mathrm{intrinsic}}$ since equation
\ref{v_vs_l_equation2} is equivalent to
\begin{equation}
\overline{\mu} L_{\mathrm{intrinsic}} = \left(\frac{10^{C}}{V_{\mathrm{norm}}}\right)^{1/\alpha}.
\end{equation}
Qualitatively, we simply use the $V_{\mathrm{norm}}$ for a quasar
to read off
an expected value of $L_{\mathrm{meas}}$ from the linear
$\log(\overline{V}_{\mathrm{norm}})-\log(L_{\mathrm{meas}})$ plot.
This expected $L_{\mathrm{meas}}$,
as it is the prediction calculated using the luminosities of the entire
quasar sample, is an estimate of $\overline{\mu} L_{\mathrm{intrinsic}}$,
where $L_{\mathrm{intrinsic}}$ is the intrinsic luminosity of that quasar.
Comparing this value
to a direct measurement of the luminosity of the quasar $L_{\mathrm{meas}}$,
we can measure any shift in the luminosity, i.e. magnification or
demagnification:
\begin{equation}
\frac{\mu_{\mathrm{true}}}{\overline{\mu}} \equiv \frac{L_{\mathrm{meas}}}{\overline{\mu} L_{\mathrm{intrinsic}}} = L_{\mathrm{meas}} \left(\frac{V_{\mathrm{norm}}}{10^{C}}\right)^{1/\alpha}.
\label{mu_equation}
\end{equation}
We define $\mu' \equiv \mu_{\mathrm{true}}/\overline{\mu}$, the relative
magnification with respect to the typical value for the whole sample.
For a large set of quasars taken from a broad sky area, such as the
data used in this work, we expect $\overline{\mu}$ to be close to unity.
In this way, each variability measurement $V$, calculated from a pair of magnitude
measurements $\Delta m$, yields an estimate of the quasar's relative magnification
$\mu'$.

\section{Palomar-QUEST RG-610 Data Set}

We measure the lensing magnification of quasars using the
Palomar-QUEST Variability Survey.
The survey used the QUESTII large area camera
(\citealt{camera_paper}), which comprises 112 CCDs with a pixel scale of
0.878''/pixel, with an overall field of view of 9.6 square degrees.
The camera was mounted on the 48'' Samuel Oschin Schmidt telescope at
the Palomar Observatory, during which it aquired two main data sets.  First
completed was a 3.5 year multi-color survey covering 15,000 square
degrees roughly four times in each of seven optical filters
(Johnson UBRI and SDSS $riz$), with
a depth of roughly mag 21 in the SDSS $r$ filter.  These data have been
used to study the variability of AGNs (\citealt{bauer09a},
\citealt{bauer09b}), to search for highly variable objects in both the
archival data (\citealt{bauer09c}) and real-time data (\citealt{george}),
and to study brown dwarfs (\citealt{cathy}, \citealt{cathy2}),
among other phenomena.  The
multi-color survey was executed concurrently with a 5 year, 30,000
square degree survey which used a single broad, red, RG-610 filter, and
reached a depth of about mag 19.5 in SDSS $r$ in each 60 second exposure
(under dark sky conditions).
The bandpass for the RG-610 filter (filter transmission times the QUEST
CCD quantum efficiency) is shown in figure \ref{rg610}.
The single-filter survey has been used as the discovery data for the
Nearby Supernova Factory (\citealt{snfactory}), as well as for searches
for dwarf planets and other solar system bodies (\citealt{planet}).

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{fig1.eps}
\end{center}
\caption{RG-610 filter transmission, multiplied by CCD response.}
\label{rg610}
\end{figure}

This work uses the single-filter Palomar-QUEST data, and constitutes the
first results from these data which rely on precision photometry.  For
this reason, we describe the DeepSky image reduction pipeline and
the photometric calibration techniques
which are used to generate flux measurements.

\subsection{The DeepSky Image Reduction Pipeline}

The Palomar-QUEST RG-610 data were taken by a variety of groups with
different strategies, under different observing conditions, and with no
uniform calibration procedure; daily operations in each of the groups
emphasized simply discovering transients, rather than more precise
photometry.  Industry-standard reduction frames such as dark frames,
dome flats, twilight flats, and fringe frames,
were taken rarely or not at all.  The DeepSky project has re-reduced these
data using a standard pipeline designed to improve the precision of
differential photometry, which we briefly describe below.

The DeepSky reduction suite treats each of the 112 CCDs in the QUESTII
camera as a separate detector, attempting only to produce a uniform response
on each frame rather than trying to flatten the detector response across
the entire mosaic.  Data frames for each stage of the reduction
(dark, flat, etc.) were pooled for each month, and sometimes from several
months in succession depending on what was available, to achieve the high
statistics necessary to reject bad pixels and regions of the camera with
unstable performance.  The algorithms were cross-validated on subsamples of
the data for months with many reduction frames, suggesting that the loss
of performance is minimal when building reduction frames monthly rather than
more frequently (weekly or daily).  Each month about 100 calibration images
from each chip were used to create 'superframes' for the reduction.

Bias levels were first subtracted from each $600 \times 2400$ pixel
image using a
$40 \times 2400$ overscan region.  Dark frames of different exposure times
(10, 60, 100, and 240~sec) were combined, with the dark current $D_i$ at
each pixel $i$ being fit to a model linear in the exposure time $t$:
$D_i = a_i + b_i t$,
where the 'instantaneous' portion corresponds to dark current which
accumulates during readout.  Iterations of the fitting procedure rejected
outlier
values in determining the model parameters $a_i$ and $b_i$.  Pixels for which
no solution converged, or for which the ``1 $\sigma$'' interquantile width
(median minus 16th percentile) of the distribution of residuals from the
fit was more than three times wider than the
median residual value for the image,
were masked as bad pixels.  All images were then corrected according
to their exposure time using the superdark $D_i$.

In general neither twilight nor dome flats were regularly available,
and images taken under dark-sky conditions were often contaminated with
strong fringes.  We therefore chose to construct flat fields using science
images taken both during twilight and in moonlight.
The calculations of \cite{ks91}, appropriately
adapted to Mt. Palomar, suggests that the loss of flatness due to the
non-uniformity of moonlight should be less than 2\% over the extent of each
chip (0.5 degrees) for images taken more than 20 degrees from the moon,
and we required this of images used to build the flat field.  We also required
that the sky level from scattered moonlight in these images be at least five
times that typical of dark sky conditions.  Images passing both of these cuts
were normalized to a mean sky brightness of 1.0, and combined by taking the
median at each pixel location to produce a superflat for the month.
Pixels with unusually large fluctuations relative to other pixels were masked
as bad, as for superdarks above.  All images were then flattened using these
superflats.

Superfringes were constructed in an analogous manner to the superflats
using images taken under dark-sky conditions.  The baseline fringe pattern
was assumed to be a time-invariant characteristic of each chip over the month.
The fringe amplitude in each science image was determined through
cross-correlation with the superfringe, and the fringes in the image removed
by subtracting the superfringe scaled by this amplitude.
Changing sky conditions can of course produce time variations in the fringes,
particularly in such a wide-band filter.  We accept this as a systematic
error in the photometry, with a contribution roughly on the same order as
the sky noise under dark sky conditions.

Object detection and aperture photometry were performed on the detrended
(bias-subtracted, dark-subtracted, flattened and de-fringed) images using
\texttt{SExtractor} (\citealt{sextractor}).
Astrometry was performed on each frame using
the \texttt{astrometry.net} suite (\citealt{astronet}).
Magnitudes were measured for
each object using ten aperture diameters ranging from 1 pixel to 16 pixels;
 this work uses the 3-pixel diameter aperture measurements
as the primary flux measurement.
Aperture corrections were calculated as the clipped median of the 12-pixel
diameter aperture flux divided by the 3-pixel aperture flux.  This correction
was calculated using all good-quality measurements on a frame, and calculated
separately for each frame.

\subsection{Photometric Calibration}

A number of cuts are made on the quality of the data.  First,
\texttt{SExtractor}
flags are checked for objects that are blended or saturated, close
to image boundaries, or for which the measurement failed;  these
flags eliminate about 3\% of the data.
A further saturation check is made by comparing object fluxes to
saturation levels determined for each chip, which are more accurate than
the single baseline saturation level checked by \texttt{SExtractor}.
Very few objects are eliminated by this cut.
Variations in the background sky level on the small spatial scale of
5$\times$5 pixels
are examined, and a frame is discarded if they are typically large
enough to contribute a 3\% error to the photometry of an object with
magnitude 18;  this removes about 3\% of the images.  Frames are also
rejected for which the median full width at half maximum (FWHM)
exceeds 5 pixels, eliminating about 6\% of the data.
To ensure that closely spaced detections do not contaminate the aperture
photometry, an object is rejected if it has a neighbor within four arc
seconds ($\sim$3.5 pixels);  most object affected by this cut are
already discarded for being blended in \texttt{SExtractor}.

Spatial fluctuations of the moon/twilight flat fields
and variability in night sky lines during observations made it
difficult to completely remove the fringes from the data.  This
results in a $\sim 2\%$ flux systematic for bright objects, which is
added in quadrature to the statistical errors.

A standard method of calibration is simply to determine a multiplicative
constant zero point which corrects differences in average response
(both intrinsic
and weather-related) between two images.
We perform such a frame-based calibration as a first step.  All overlapping
images are determined, and the one in which objects have the brightest
measurements is assigned as the standard.  A single zero point
is determined for each frame in order to bring it to the
level of the standard.  This constant is determined using objects
of all magnitudes.  The correction is also calculated separately
for bins of different magnitudes:  five bins between SDSS r magnitudes
of roughly 15 and 20.  On average, the difference in the mean correction
between the brightest bin
and a fainter one ranges from about 3\% to 6\%.  If a larger variation
than this is seen, the frame is assumed to be non-linear and is not
used.  This eliminates about 15\% of the data;  the non-linearity is
partially due to intrinsic properties of the CCDs, but primarily due to
the insufficient quality of the fringe and flat field calibration data.
Poor frames are also identified by comparing their measurements to the
mean measurements of the objects they hold.  If 15\% of the flux
measurements on a frame disagree with their objects' means by
more than 3 times the measurement errors,
then the frame is discarded.  This removes about 8\% of the data,
for which the systematic effects are catastrophic.

The systematic residuals in the data make a frame-based calibration
insufficient for accurate photometry;
while a systematic term describes the typical uncertainties, the error
distribution is not Gaussian and therefore this procedure generates
many photometric outliers.  Because the spatial scale of the systematic
variations is much larger than the size of each object, we can refine
the frame-based calibration using an object-based one.  This technique
involves generating a zero point for one object at a
time by considering only its close neighbors.  A large scale systematic
variation far away from an object will affect a frame-based calibration;
an object-based one will be less sensitive to such features.
We therefore implement, as a second step, an
object-based calibration. We use the average of at least 10 neighbors
within 150 arc seconds to calculate the correction between an object's
measurements on two scans.  About 8\% of the detections do not have enough
neighbors to satisfy this requirement, and are therefore disregarded.

The frame calibration is calculated using only
objects detected by the Sloan Digital Sky Survey (SDSS; \citealt{sdss7})
and classified by them
as pointlike.  This precaution eliminates the possibility of our
inadvertently using data artifacts, or galaxies which may exceed
the aperture size, as calibration objects.
For the purposes of this work, the object calibration is only
performed on quasars which have been spectroscopically identified
by SDSS.
The reference objects used in the object calibration are limited
to pointlike SDSS detections.

The strict cuts implemented in the photometric calibration throw
away roughly 35\% of the measurements.  The systematic error is
typically 2\%, with about 3\% of measurements disagreeing with the
mean flux of the object by over 3 times their measurement errors.
From previous variability
studies we expect $< \sim$1\% of measurements to be truly variable
(\citealt{huber06}; \citealt{sesar07});  therefore, we infer that our
photometry contains
residual calibration errors in $\sim$2\% of the measurements.
Because in this work we deal only with the ensemble average over hundreds
of measurements, and we expect the systematic error to be uncorrelated
across different observations,
we do not expect the residual errors to affect our results.
Nonetheless, as a precaution against these errors we make additional
cuts against variability behavior uncharacteristic of quasars;  this
is described further in section \ref{datacuts_section}.

\section{Results}

We study a sample of 3573 spectroscopically identified quasars,
spanning roughly 5,000 square degrees, from the SDSS data release 7.
For each quasar we calculate its luminosity at 2500 \AA\ by multiplying
a redshifted composite quasar spectrum taken from \cite{compositespectrum}
with SDSS filter curves.
We use the SDSS $z$ band magnitude, corrected for galactic extinction
using the maps from \cite{schlegel}, to normalize the composite spectrum's
amplitude.  We choose the $z$ band as it is least extincted by
dust along the line of sight.
We then take the normalized flux at 2500 \AA\, and convert
it to luminosity using the object's spectroscopic redshift and
the cosmological parameters, which we assume to be $\Omega = 1,
\Omega_{\Lambda} = 0.7, \Omega_{M} = 0.3, H_{0} = 0.71 \frac{km}{s \cdot Mpc}.$
We also estimate the black hole mass for each quasar from the $M-\sigma$
relation, using the widths of the $\mathrm{H_{\beta}}$ and $\mathrm{Mg_{II}}$
emission lines
as in \cite{salviander07}, and calculating the radius of the BLR as
described in \cite{bentz09}.

In many cases during the Palomar-QUEST Survey, the same sky area was
observed twice in the same night.  Because quasars are not variable
on such short timescales to the precision of our measurements, we
average all intranight measurements to yield one flux value per night.

\subsection{Data Cuts \label{datacuts_section}}

As described above, we have made restrictions on the data quality in order
to produce reliable photometry.  Here we describe the criteria we use
to select our quasar sample so that we have a well-measured, homogeneous
dataset.

While the majority of quasar variability is thought to be due to
accretion disk instabilities, some AGN show blazar characteristics,
with considerable flux
and variability from a jet component.  Jet-based variability
is much more dramatic than that seen in typical quasars, and because
it is powered by shock dissipation and enhanced by relativistic beaming,
it is not
expected to follow the variability-luminosity relation seen in most quasars.
It is therefore important to restrict the quasar list to those
displaying variability that is characteristic of quasars.

We select quasars from SDSS that have the following properties:

\begin{enumerate}

\item spectroscopic identification in the SDSS, with a match to the
spectral cross-correlation template number 29 (QSO) with confidence
of at least 0.95

\item no detection in the FIRST \citep{becker95} or RASS
\citep{anderson07} catalogs

\item redshift between 0.4 and 2.2

\item estimated black hole mass between $10^{8} \mathrm{M_{\odot}}$ and $7.5 \times 10^{9} \mathrm{M_{\odot}}$

\item luminosity between $10^{30.85} \frac{\mathrm{erg}}{\mathrm{s} \cdot \mathrm{Hz}}$ and $10^{31.5} \frac{\mathrm{erg}}{\mathrm{s} \cdot \mathrm{Hz}}$.

\end{enumerate}

Criteria 1 and 2 are meant to select radio-quiet AGN whose optical fluxes do
not show significant contributions from a jet.  Numbers 3-5 serve in part to
crop the tails of the parameter distributions, and are the edges of
the binning scheme that we use when normalizing the data as described
below.  The bin limits are stated again in table \ref{bin_limits}.  The
low redshift cut is chosen to eliminate objects that may appear
spatially extended in the data, and therefore be poorly measured by
the Palomar-QUEST photometry.  The high redshift cut ensures that
the quasar sample is selected homogeneously using UV-excess techniques.
The low luminosity cut is chosen because for objects fainter than this
level we see a flattening in the variability-luminosity relation,
implying that these less luminous quasars vary to fluxes below our
detection limit, causing us to not observe their full range of variability.

These criteria yield a sample of 4845 quasars that have Palomar-QUEST repeated
measurements.

Furthermore, the quasars' Palomar-QUEST measurements must satisfy:

\begin{enumerate}

\item $V \leq 0.1$ where $V$ is defined as in
equation \ref{v_equation} and is calculated using all measurement pairs
with rest-frame time lag less than 10 days

\item $V \leq 0.5$ where $V$ is defined as in
equation \ref{v_equation} and is calculated using all measurement pairs
with rest-frame time lag greater than 100 days.

\end{enumerate}

These cut values
are motivated by the variability amplitudes of quasars and blazars
measured in \cite{bauer09b}.  Because the
variability amplitude is unchanged for lensed and unlensed objects,
this is an unbiased cut with respect to lensing analyses.
This step eliminates 967 objects, leaving 3878 quasars.  We note
that this cut eliminates a much larger fraction of the AGN than is
expected from the relative numbers of known blazars and quasars.
However, the cut is not only sensitive to jet-based variability but also
calibration errors, and is a strict but important criterion to ensure
that poor quality measurements do not overwhelm the lensing signal.
These 3878 quasars have, in total, 230,674 Palomar-QUEST measurement
pairs.

We only use measurement pairs for which:

\begin{enumerate}

\item the rest frame time lag between measurements is shorter than 400 days

\item the measurements show significant ($> 1 \sigma$) variability

\item the variability amplitude is less than 1 magnitude

\item no more than 40 measurement pairs from each quasar are included.

\end{enumerate}

Cut 1 eliminates a long but low-level tail out to larger time lags,
and throws away 19,523 pairs.  Cut 2 is necessary to avoid the
calculation of imaginary $V$ values for individual
measurement pairs and is very restrictive because the Palomar-QUEST
systematic error is of the order of typical quasar variability amplitudes over
rest frame timescales of weeks.  Still, as will be seen in figure
\ref{v_vs_l2500}, the data after this cut continue to follow a
variability-luminosity relation described by equation
\ref{v_vs_l_equation}, and therefore can be used to measure
magnification using equation \ref{mu_equation}.  We also note that
removing the lowest signal-to-noise $V$ values reduces error due to
imprecise determination of the survey's systematic uncertainties,
given the dependence of $V$ on the measurement uncertainty as stated
in equation \ref{v_equation}.  Because the systematic uncertainties
stem in part from imperfect flat field and fringe calibration images,
they can be time and position dependent and therefore difficult to
determine precisely for each measurement.
This cut eliminates 134,745 pairs.

Cut 3 is a final check against outliers
that are uncharacteristic of quasar behavior, and only throws away 17
measurement pairs.  Cut 4 serves to keep a minority of
exceptionally well-measured
quasars from dominating the dataset, since the number of measurement
pairs rises as the number of measurements squared.  This cut
removes 14,671 pairs.

Normalization of the quasars, described in section \ref{mu_meas_section},
is only done if there are sufficient quasars with similar
properties so that we can determine accurate zero points.
We require 50 measurement pairs per $\tau$ $M$ $z$ $L$
bin, which removes 4359 measurement pairs.

We are left with 57,359 useable measurement pairs, from 3573 quasars.
We note that many of our cuts are due to properties of the
data such as statistics and measurement errors, rather than properties of
the quasars.  Cuts on future quasar data sets may not need to be
so restrictive.

\subsection{Variability-Luminosity Relation}

After normalizing the data as described in section \ref{mu_meas_section},
we plot the normalized variability vs. luminosity relation,
which is shown in figure \ref{v_vs_l2500}.
The $y$ axis error bars on the points are errors on the mean calculated
from the spread of the measurements in each bin.
The bins in the figure are smaller than those used in the normalization,
in order to show the relation in more detail.
The trend is indeed well-described by a
power law, with index $\alpha = 0.565 \pm 0.007$.
The best fit line is shown on the plot.  This slope is steeper than
that found in other works (e.g., \citealt{vandenberk04}, \citealt{bauer09a});
however, it is
not directly comparable since we only consider measurements for which
we observe significant variability.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{v_vs_l2500.eps}
\end{center}
\caption{Log Variability vs. Log Luminosity at 2500\AA, after normalizations, determined using 57,359 measurement pairs.  The best-fit line is shown, with slope $-0.565 \pm 0.007$.}
\label{v_vs_l2500}
\end{figure}

The precision of our magnification measurements relies on a narrow
spread in the variability-luminosity relation.  The residuals
in log($V$) around the best-fit line are distributed normally, with
a standard deviation $\sigma = 0.22$ that is constant across our range
of luminosity.  These residuals are larger than the
typical measurement errors, and therefore reflect an inherent
spread in the quasar variability properties.
There are several possible
sources of the enhanced variance.

The measurement error on the luminosity is small, and is based simply on
the uncertainties in the SDSS broadband magnitudes.  This error estimate
ignores the fact that the quasars vary by more than these uncertainties;
the true mean luminosity could be tens of percent different from
what was measured when the SDSS made its photometric observation.  Since
the spectroscopic quasar sample is much brighter than the SDSS photometric
detection limit, there should be no Malmquist bias in the quasar sample
and the error on the measured luminosity should be unbiased.  Therefore,
when discussing the variance of the relation, we assume that the luminosity
is correct and simply absorb its error into the $y$ axis spread in values.
In future studies this uncertainty can be reduced in cases where
there are more photometric measurements per quasar, which can be averaged
to produce a more accurate typical luminosity.  (This is not possible
with the Palomar-QUEST data, as it is relatively calibrated rather than
absolutely.)  Or, if many measurements
are available, a lightcurve may be fit to each quasar's data to
determine the object's baseline luminosity most accurately
\citep[see, e.g.,][]{macleod10}.

If the spread in the variability-luminosity relation is due to an
effect with zero bias, then all quasars will
intrinsically lie on the mean relation and increased statistics for a given
object will move it closer to the mean trend.  If quasars in the
sample have heterogeneous variability properties, an object may lie
intrinsically off the variability-luminosity relation and more statistics
will only reveal this property more clearly.  Some insight into this
issue is given in \cite{macleod08}, which explores the implications
of using many, as opposed to simply two,
measurements of each quasar when studying the objects' variability
properties.  They note that using only two measurements yields
ensemble variability results similar to those determined using many
epochs from the SDSS stripe 82 region.  However, they also point out
that there is an intrinsic spread in the quasars' variability behavior
that is not currently understood.  This intrinsic difference between
individual objects surely contributes to the scatter observed in the
variability-luminosity relation, and is our motivation for including
no more than 40 measurement pairs from each object in this analysis.
More insight into the physical processes driving the different
variability properties of individual quasars would help in
understanding and reducing the systematic errors in the
lensing magnification measurement.

Assuming statistically identical quasar lightcurves with power law
spectra, \cite{bauer09a} used Monte Carlo simulations to show
that using few measurements per quasar to calculate $V$ versus $\tau$
gives a slope that is unbiased, although windowing effects can make
the trend non-linear at very short and long time lags (which are excluded
from the data set in this work).  Therefore we expect no significant
bias to be introduced in this analysis due to the behavior of $V$ in
the presence of sparse data sampling.

\subsection{Magnification Measurements}

Now that we have calibrated the quasar measurements and confirmed the
variability-luminosity relation, we calculate the magnification
$\mu' \equiv \mu_{\mathrm{true}}/\overline{\mu}$ as defined in equation
\ref{mu_equation}.  The distribution of $\log(\mu')$, with one entry per
quasar measurement pair, is shown in figure \ref{measured_mu}.
The distribution is wide and close to Gaussian.
This is due to the distribution of the residuals of the $V-L$ relation,
which has a similar shape.  Because the scatter in the $V-L$ relation
is large, the width of the $\log(\mu')$ distribution is much wider than
the range of magnifications expected in the data.
Just as the mean of many variability measurements produces a precise
variability-luminosity relation, the mean of many magnification
measurements produces a meaningful result, with an error on the mean
much smaller than the width of the distribution.

In order to obtain mean magnifications, we must bin the measurements
in a physical quantity
which we believe to be related to the magnification.  Here we choose
to bin the measurements in terms of their distance perpendicular to
the line of sight
(scaled as distance/$R_{200}$) from the nearest member of the MaxBCG
galaxy cluster catalog.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{log_urel.eps}
\end{center}
\caption{Logarithm of the measured quasar magnifications, determined using 57,359 measurement pairs.}
\label{measured_mu}
\end{figure}

\subsection{The Expected Signal} \label{expected_signal_section}

The strongest magnification of quasars is expected to be due to clusters
of galaxies along the line of sight.  To estimate the level of
magnification that we expect quasars to experience, we can calculate
the magnification due to the members of known cluster catalogs.
For this purpose we use the MaxBCG catalog, which contains
13,823 clusters and covers about 7,500 square degrees.
The MaxBCG catalog
is estimated to be 90\% pure and 85\% complete for clusters between
redshifts 0.1 and 0.3 and with masses greater than $10^{14}M_{\odot}$
\citep{maxbcg}.
By adopting a model for each cluster, we
can calculate the expected magnification of each quasar due to the galaxy
clusters in the catalog.

We assign a Navarro-Frenk-White (NFW) profile (\citealt{nfw}) to each cluster:
\begin{equation}
\rho(r) = \frac{\delta_{c} \rho_{\mathrm{crit}}}{(r/r_{s})[1+(r/r_{s})]^{2}}
\label{nfw_equ}
\end{equation}
where $c$ is the concentration parameter of the profile,
$\delta_{c} = \frac{200}{3} \frac{c^{3}}{\mathrm{ln}(1+c) - c/(1+c)}$,
$r_{s} = R_{200}/c$,
and $R_{200}$ is the radius inside which the density is 200
$\times \rho_{\mathrm{crit}}$, the critical density of the universe at the
redshift of the cluster.   The total mass inside $R_{200}$ is $M_{200}$.
We assume the cluster mass-richness relation used by \cite{rozo09}:

\begin{equation}
M_{200}(N_{200}) = \frac{1}{1.022} e^{0.48} \times \left(\frac{N_{200}}{20} \right)^{1.13}
\label{rozo_equ}
\end{equation}
to estimate $M_{200}$ given the richness $N_{200}$ stated in the cluster
catalog.
$M_{200}$ and $c$ have been seen to correlate with each other;  we
use the mass-concentration relation from \cite{duffy08}:

\begin{equation}
c(M_{200},z) = 5.71 \times \left(\frac{M_{200}}{2.e12 \times h^{-1}}\right)^{-0.084} \times ( 1 + z )^{-0.47}
\end{equation}
 to determine $c$ given $M_{200}$ and the cluster redshift $z$.  When
calculating the magnifications, we truncate each profile at
$3 \times R_{200}$.  If a quasar is close to more than one cluster,
we multiply the magnifications due to each nearby cluster to produce a
magnification estimate that includes contributions from all known
clusters.  Multiple magnifications are a minor effect;  only 6.5\%
of the quasars have expected magnifications of $>1\%$ from more than
one cluster.

A histogram of the logarithm of the predicted magnifications for the
quasar sample is shown in figure \ref{expected_mu}.
We predict one magnification per
quasar, but we measure one $\mu'$ per pair of magnitude measurements.
The thick-lined histogram in figure \ref{expected_mu} shows the distribution of
magnification predictions, with one entry per quasar and therefore 3,573
entries.  The thin-lined
histogram includes one entry for each magnitude measurement pair:  if
a quasar has $N$ measurements, then that quasar's predicted magnification
will have $N(N-1)/2$ entries of identical value.  The thin histogram
therefore has 57,359 entries and is comparable to figure \ref{measured_mu}.
The vast majority of the quasars are not
significantly magnified by the clusters;  however, there is a small
tail of highly magnified objects up to $\mu \sim 2$.
The striking difference in the shapes of the expected magnification distribution
(figure \ref{expected_mu}) and the measured distribution (figure \ref{measured_mu})
is due to the large scatter in the individual measured values, reflecting the scatter in the
$V-L$ relation.  These figures visually demonstrate the need to average many
magnification measurements in order to obtain a result precise enough to compare
to theory/models.

The transverse distance between each
quasar and the MaxBCG cluster closest on the plane of the sky is shown
in figure \ref{qso_cluster_dist};  panel (a) shows the distance in the
logarithm of kiloparsecs at the redshift of the closest cluster, while panel
(b) shows the distance as a fraction of $R_{200}$ of the closest cluster.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{log_ue.eps}
\end{center}
\caption{Logarithm of the lensing magnification of quasars assuming NFW profiles for MaxBCG galaxy clusters.  The thick-lined histogram includes one entry for each of the 3573 quasars which overlap the MaxBCG area.  The thin-lined histogram shows the same calculated magnifications, but includes one entry for each of the 57,359 quasar measurement pairs and is therefore comparable to figure \ref{measured_mu}.}
\label{expected_mu}
\end{figure}

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{dist_logkpc.eps}
\includegraphics[width=85mm]{dist_r200.eps}
\end{center}
\caption{Distance, transverse to the line of sight at the redshift of the cluster, between the quasars and the clusters which are closest on the plane of the sky.  (a) in units of log kiloparsecs at the redshift of the cluster.  (b) in units of $R_{200}$ of the closest cluster.}
\label{qso_cluster_dist}
\end{figure}

\subsection{Magnification profile of the MaxBCG Clusters}

For each quasar, we calculate the magnification we expect due to
clusters in the MaxBCG catalog, as described in section
\ref{expected_signal_section}.  We then group the quasars in
logarithmically spaced bins given by the quasar's distance from
the closest cluster, in units of $R_{200}$ of that cluster.
The errors on the measurement are determined through bootstrap
resampling.  In particular, for each bin 1000 mean magnifications
are calculated using random sampling with replacement such that the
randomized set includes the same number of measurements as the
original bin.  The errors are taken to be the $1 \sigma$
(15.9 and 84.1 percentile) values of the 1000 mean magnifications.
Because the errors on the magnifications are in part caused by
systematic uncertainties that are not currently understood, we prefer
to derive the errors empirically in this way using the properties of
the distribution itself.

Figure \ref{results_rozo}
shows the mean measured $\mu'$ (points with error bars) and the expected
magnification (points without errors, connected by lines),
assuming NFW haloes for the MaxBCG catalog's clusters as described above.
While the magnification is significant and does fall with radius,
we measure a systematically different magnification profile from that
expected.  The measured values compare to the expected magnifications
with reduced $\chi^{2} = 1.84$.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{result_rozo.eps}
\end{center}
\caption{Measured $\mu'$ (points with error bars) and expected $\mu$ (points connected by lines), versus distance from the nearest cluster in units of $R_{200}$, assuming NFW profiles truncated at $3 \times R_{200}$.}
\label{results_rozo}
\end{figure}

The MaxBCG catalog's cluster positions are given as the location of the
brightest cluster galaxy (BCG).  This position does not always coincide
with the center of the dark matter halo.  Errors in the cluster
central coordinates cause the average radial profile of the MaxBCG
clusters (as measured from the reported centers) to differ from
NFW.  \cite{johnston08} studied this effect using weak lensing shear
measurements and N-body simulations, and found a Gaussian distribution
of positional offsets between the true and measured centers, with a
richness-dependent probability that the cluster is correctly centered.
To determine the effects of such offsets on our expected magnifications
we implement the prescription of \cite{johnston08}, offsetting
the center of a subset of the clusters in a random manner.  The results
are shown in figure \ref{results_rozo_offset}, and provide an
improved fit to the data, with reduced $\chi^{2} = 1.17$.
The agreement between the data and predictions may be further improved by
modelling the effects of large scale structure outside the redshift
range of the MaxBCG catalog;  however, these effects are subdominant
and beyond the scope of this paper.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{result_rozooff.eps}
\end{center}
\caption{Measured $\mu'$ (points with error bars) and expected $\mu$ (points connected by lines), versus distance from the nearest cluster in units of $R_{200}$, assuming NFW profiles truncated at $3 \times R_{200}$ and with centers offset as prescribed in \cite{johnston08}.}
\label{results_rozo_offset}
\end{figure}

Using the errors on the binned measurements determined through bootstrap
resampling,
we can estimate the error on each magnification measurement.
Figure \ref{errors_figure} shows the logarithm of the error on each bin
versus the number of measurements in that bin, taken from the
data in figure \ref{results_rozo_offset}, with a best-fit line
superimposed.  The trend falls as $\sqrt{N}$, and at the level of our
most populated bin (with 21,482 measurement pairs) shows no sign of
a systematic floor.  The best-fit line extrapolates to imply that
each $\mathrm{log}(\mu')$ measurement, made using one magnitude measurement pair,
has an error of roughly 0.4.  For a magnification of 1.1, this corresponds
roughly to a signal to noise of 1 for each magnification measurement.

\begin{figure}
\begin{center}
\includegraphics[width=85mm]{error_vs_n.eps}
\end{center}
\caption{Logarithm of the error on each bin versus the number of measurements in that bin, taken from the data in figure \ref{results_rozo_offset}, with a best-fit line superimposed.  The trend falls as $\sqrt{N}$, implying an error on $\mathrm{log}(\mu')$ of 0.04 for a magnification measurement using one pair of magnitude measurements. }
\label{errors_figure}
\end{figure}

\section{Discussion and Conclusions}

We introduce a new technique to measure the effects of gravitational
lensing by large scale structure.  Type I quasars on average follow a
well-constrained
variability-luminosity relation;  lensing magnification causes a
significant shift in this relation.  We describe the magnification
measurement procedure, and use it to determine the magnification of
background quasars due to
lensing by the galaxy clusters in the MaxBCG catalog.

The measured magnification is significant, and consistent with the
signal expected
from the catalog.  In particular, the agreement between the
data and expectations is improved when the
miscentering of the clusters is taken into account, as described in
\cite{johnston08}, compared to when the clusters are assumed to be NFW
profiles centered exactly on their catalog positions.
The lensing effects of the MaxBCG catalog have been measured very
precisely using weak lensing shear, for example in \cite{sheldon09}.
Although the magnification measurements presented here are not
competitive with such results, they serve to show that quasar
variability can be used to study lensing magnification, and are
the first measurements using this new technique.

Using quasar variability to measure gravitational magnification is a very
different approach from the common method of using galaxy shapes to
measure gravitational shear.  Precision shape measurements of distant
galaxies require very good quality data with faint flux limits and small
point spread functions.  Furthermore, the complex intrinsic shapes of
galaxies make the shear measurement nontrivial.  Measuring the variability
amplitudes of quasars is much more simple, as these objects are often bright
and they typically vary by amounts larger than the photometric errors of
surveys such as Palomar-QUEST or SDSS.  We see the error
on the measured magnification to be roughly
given by
\begin{equation}
\log(\sigma_{\log \mu}) = -0.5 \log(N_{\Delta m}) - 0.4
\end{equation}
where $N_{\Delta m}$ is the number of $\Delta m$ measurements.
This can be compared to the error
on weak lensing measurements of the reduced shear $g$, which scale as
$\sigma_{g} = \sigma_{\epsilon} \times (1 - |g|^{2}) / \sqrt{N_{\mathrm{gal}}} \approx
\sigma_{\epsilon} / \sqrt{N_{\mathrm{gal}}}$ for moderate shears.
The intrinsic scatter in the galaxy shapes is
$\sigma_{\epsilon} \sim 0.35$ (\citealt{schneider04}), and $N_{\mathrm{gal}}$ is
the number of measured galaxies.
For magnification by a galaxy cluster with an NFW profile parameterized
by $M_{200} = 10^{14}$ and concentration $c = 6$, a region with moderate
magnification $\mu = 1.05$ would have reduced shear $g = 0.035$.  In this
regime, 100 galaxies and 100 $\Delta m$ measurements would yield signal to noise
$S/N_{\mu}$ = 10 and $S/N_{g}$ = 1.
A region of higher magnification $\mu$ = 1.25 and $g$ = 0.083 would
yield again $S/N_{\mu}$ = 10, with $S/N_{g}$ = 2.4.
This lensing strength is at the upper end of the range for which
weak lensing techniques have been rigorously tested, for example by the
STEP2 project (\citealt{step2}).  Stronger distortions are not
straightforward to measure, particularly using the common KSB
method (\citealt{ksb}).  The variability-based quasar magnification
analysis, on the other hand, is equally applicable to all lensing
regimes.

The fact that the magnification
measurements each have higher signal to noise than the shear measurements
is offset by the relative sparsity of quasars compared to inactive galaxies.
In the current SDSS spectroscopic sample, which reaches depths of $r$ $\sim$
19.5, there are about 15 quasars per square degree.  In data down
to $r$ $\sim$ 20.5 we expect roughly 40 quasars per square degree
(\citealt{richards06}).  This depth will soon be available over large areas
of sky.  For example, BOSS is expected to double the number of
spectroscopic quasars in the SDSS Survey over the next several years,
over a footprint of 10,000 square degrees\footnote{http://www.sdss3.org/}.
Shear analyses are usually performed over much smaller areas of high
quality data.  For example, results from the CFHT Wide Survey use
50,000 galaxies per square degree, down to a magnitude of $i$ $\sim$ 24.5,
with a total area of 34 square degrees (\citealt{fu08}).
In order to reach the same signal to noise for moderate lensing strengths
of $\mu = 1.05$ and $g = 0.035$, assuming 40 quasars and 50,000 galaxies
per square degree, we would need fewer than 4 magnitude
measurements on average for each
quasar (assuming that the measurements give uncorrelated information
about the magnification;  more work is required to investigate this
systematic complication).  Assuming shallower data with 15 quasars
per square degree, we would require 6 magnitude measurements of each
quasar, on average, over just 34 square degrees.  Alternatively,
the same signal to noise could be achieved with just 2 measurements per
quasar over an area of 1100 square degrees.
These levels of data are easily obtainable with surveys
such as BOSS and Pan-STARRS, which will provide $>$10,000 square degrees
with sufficient data quality for magnification measurements.  This is in
sharp contrast with the $\sim 70$
square degrees which will be available from Pan-STARRS Medium Deep fields
with the quality assumed here for shear measurements.

This work introduces the technique of using variability to measure
the lensing magnification of quasars, and measures the magnification of
3573 quasars, with magnitudes down to SDSS r $\sim$ 19.5, due to lensing
by known galaxy clusters along the line of sight.  Future large-area
sky surveys will be able to apply this new technique to measure lensing
magnification over larger volumes, and improve
our understanding of large scale structure.

\section*{Acknowledgments}

We thank Peter Nugent and Janet Jacobsen for their work in developing
and running the DeepSky pipeline.
We acknowledge the support of the European DUEL Research Training Network,
Transregional Collaborative Research Centre TRR 33, the Cluster of
Excellence for Fundamental Physics,
Spanish Science Ministry
AYA2009-13936, Consolider-Ingenio CSD2007-00060 and project
2009SGR1398 from Generalitat de Catalunya.
The National Energy Research Scientific Computing Center,
which is supported by the Office of Science of the U.S.
Department of Energy under Contract No. DE-AC02-05CH11231,
has provided resources for this project by supporting staff,
providing computational resources and data storage.
We thank the Office of Science of the Department of Energy
(grant DE-FG02-92ER40704) and the National Science Foundation
(grants AST-0407297, AST-0407448, AST-0407297) for support.

redpajama_set_name: RedPajamaArXiv


The second instalment in the Cardinham Summer Series 2019 is the 10k (easy), nice wide pathways for the most part, nothing too evil!The course will be 1 x 10k loop through the forest with a water station along the route.
All competitors will of course receive a Cardinham Summer Series medal with the 10k 'easy' coloured ribbon!
Race registration opens on Friday 22nd February at noon, so why not take advantage of our limited early bird offer entry price of £18!


Virtual Backpack is a place for Ramsey School District families to receive flyers and other information from the district and community organizations without the hassle of sorting through printed papers sent home with your child.
How to submit a flyer?
Send flyer in PDF form to Donna Ryan, Executive Secretary to Superintendent, at dryan@ramsey.k12.nj.us requesting approval and posting. Please indicate which school(s) you are targeting. A response will be given within a few days.
Note: In order for a flyer to be approved, the event must be for non-profit.


Both fascinated about our beautiful Earth and very practically oriented camper, travelling the world with her own camperbus was her forever dream.
In 2016 she and her family got a housing issue; their beloved home was going to be fully renovated and they had to find another house for a period of time. It would be impossible to find a place so wonderfully rural... Her mother started needing some care and without a car it was too far to go there. So on Maaike's wishlist was a car ánd a 'get away-thing'. 1 + 1 = 2, and with that big dream she finally decided to go find 'her perfect campervan'.
Husband, little boy ánd 2 dogs should have to be able to join her on trips, and she wanted a realiable van as she's not technical nor strong.
This bus had it all; technically in a very good condition, and inside made of wood. Perfect for me, 'cause that I can adapt if I want to.


text:
Q: Let $H$ be a nonempty set and let $f:H\to\mathbb R$ be such that $f(H)=\{f(h): h \in H\}$ is a bounded subset of $\mathbb R$. Prove that ... Let $H$ be a nonempty bounded subset of $\mathbb R$ such that $\inf H>0$.
Show that
$\frac1{\inf H} = \sup\{\frac1h : h\in H\}$
Proof:
$\sup\{\frac1h : h\in H\} = \frac1{\inf H}$
By definition, if $H$ is bounded there exists $a,b\in H$ such that $a\leq a_1\leq...x\leq....\leq b$.
\begin{align}\sup\{\frac1h : a\leq x\leq b\} = \frac1{\inf H}\\
\frac1a = \sup\{\frac1h:a\leq x\leq b\}\\
\frac1a = \sup\{\frac1h:a\leq x\leq b\} = \frac1{\inf H}\end{align}
$\inf H = a$ since by definition any $k$ such that $a>k$ implies $k\notin H$.
$\frac1a=\sup\{\frac1h:a\leq x\leq b\} = \frac1{\inf H}=\frac1a$

A: Let $$A=\{\frac 1h , h\in H\}$$
$$a\in A \implies$$
$$\exists h\in H \;: a=\frac 1h .$$
$$ h\in H \implies h\ge \inf H>0 \implies$$
$$ a\le\frac{1}{\inf H} $$
thus $\frac{1}{\inf H}$ is an upper bound for $A$.
$\inf H$ is limit of a sequence $(h_n)$ of elements in $H$ with $h_n\ge \inf H>0$.
but
$$\frac{1}{\inf H}=\lim_{n\to+\infty}
\frac{1}{h_n}$$
thus $\frac{1}{\inf H}$ is an upper bound and limit of a sequence of elements in $A$ .
therefore it is the $\sup$.

redpajama_set_name: RedPajamaStackExchange


text:
All articles Health Tech Business
Can Digital Healthcare Boost Employee Retention?
A decade ago few would have believed we'd be ordering pizzas, taxis—and now even doctors appointments, straight from our iPhones.
But according to 2014 research from McKinsey, more than 75% of patients said they expect to use digital services in the future. And as AI gets smarter, product design gets better, and healthcare professionals become more involved with digital platforms, these tools will become not only more commonplace—but more loved by users.
And that's important, because as millennials enter the office in increasing force—flexible, personalised, and user-friendly tools will become crucial to retaining top talent.
The state of employee turnover
In their global survey, consulting firm PwC found that due to lackluster economic conditions, many millennials have made compromises when accepting a job.
72% felt they made some sort of tradeoff to get into work
38% said they were actively looking for a different role
43% said they were open to offers
Only 18% expected to stay with their current employer for the long term
As the economy picks up, voluntary turnover among this giant talent pool is almost certain to increase—leaving employers with turnover costs of up to 2x the exiting employee's yearly salary.
The role of digital healthcare in employee retention
According to a US-based survey from Willis Towers Watson, over half of employees view healthcare and retirement benefits as compelling reasons to stay with an employer.
If you think about it in personal terms, it just makes sense. We're never more scared than when we're trying to access healthcare.
In fact, in 2015 Nuance Communications conducted a global survey of 3,000 people to determine how the NHS would need to evolve to meet the needs of both its boomer and millennial patients. They found that more than 54% of millennials said they search online for health information before seeing a clinician.
Digital healthcare takes traditional benefits the extra mile by alleviating the key fears for millennials seeking healthcare (namely cost, time and cyberchondria) and meeting them where they're comfortable—their mobile phones.
The Keys to Retention: Career path, flexibility and culture
While traditional benefits like healthcare and pensions are still fundamental to retaining your best people—they're certainly not the only factors.
According to an Arch Apprentices survey of 750 senior leaders, the most effective retention strategies are enabling employees to learn new skills (73%), providing access to promotions (67%), and pay rises (65%).
Flexibility is another must. Given the rising demand for better work-life balance, and the ensuing popularity of flexible working programmes, the ability to control their own time is of paramount importance to today's employees. Digital healthcare can help by adding an additional level of flexibility to the overall benefits package and eliminating the headache and time-burden of long hospital queues.
Strong retention also comes from strong culture. Providing digital healthcare, along with other modern benefits, lets your employees know you're actively updating and examining your benefits. Demonstrating a flexibility and willingness to adapt can go a long way in making employees feel confident about the future.
Enrich your employee benefits programme and give employees access to video GP appointments, health assessments, symptom checking, prescription delivery and more from wherever they are, 24/7.
Contact the Babylon team today on business@babylonhealth.com or 0207 100 0762 and learn how you can keep your benefits on the cutting edge.
babylonhealth.com/business
Written by Zoe Puckering
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Other articles you might find interesting
10 things we did in 2019
As 2020 begins, we take a look back at some of Babylon's highlights from last year.
Radical transparency - rewriting the rules
Babylon's founder and CEO Ali Parsa explains why, when it comes to business, transparency is vital
How to Prove Your ROI on Employee Benefits Spending
How to win internal buy-in, invest in better benefits and become a true advocate for your employees.

redpajama_set_name: RedPajamaCommonCrawl


High performance exhaust systems are very essential for your vehicle because they offer better gas mileage, better torque and more horsepower. They also enhance the high performance sound with fewer restrictions. The Audi R8 exhaust system make it easier for the engines to release exhaust gases easily.
The Single Side Exit exhaust system uses just one pipe to vent out the gases from the motor away from the vehicle. These systems cost less than the systems with two exhaust pipes. The advantage of this system is that it produces more power in the engine when compared to other dual systems.
The Dual Side Exit is similar to the above system but the only difference is that there are two pipes to allow the gases to exhaust from the vehicle. Here the engine has less restriction but has a quicker throttle response.
The Dual rare exit in the vehicle exhaust systems uses two pipes to exhaust the gases. There is a unique design when compared to the latter. Here the pipes are placed under the bumper or the roll pan. The sound that comes out of these systems depends on how aggressive the driving is.
The Opposite Side Dual exhaust uses two pipes from the motor but the difference is the rear exit. There is each pipe exiting behind each of the tires at the rear. These vehicle exhaust systems have all the benefits that are mentioned in the previous types of exhaust systems.
Choosing the right exhaust system might be very painstaking sometimes. But not when you know how to choose the right vehicle exhaust systems.
Most of the vehicles come with a great exhaust system. However, you do not have to compromise on the system that is a direct factory fit. Depending upon the nature of use, you can modify the exhaust system by replacing them with an aftermarket system.
Always consider your vehicle's exhaust manifolds. You can replace those manifolds so as to remove the exhaust gases more efficiently. This sometimes generates additional power in the engine.
If you vehicle has only a single exhaust pipe, you can always convert to exhaust systems with dual pipes. This radically improves the exhaust flow and allows the engine to breath better.
The catalytic converters and the mufflers play an important role in the performance of the exhaust systems. There are different types of converters and mufflers that can be considered depending upon your vehicle type.
The car and vehicle manufacturers these days leave lot of room for modifications and improvement especially when it comes to increasing the horse power, torque and reducing the carbon gases. Whenever you are looking for alternative vehicle exhaust systems ensure that you rely on branded systems. The ultimate goal is to improve the efficiency of the exhaust system, help the engine breathe and thus spend wise on the fuel. Here more info about exhaust systems.
Champion Motorsport is the best place for Audi R8 Exhaust, Ferrari 488 TUBI Exhaust System and other high-end exhaust, intake and ECU upgrades for exotic and high-end sports cars. They have a number of affordable exhaust system designs for different vehicle models in their stocks which are available at the best market prices.


text:
College to Consider Pausing Development of Trinity East
Trinity East is a five-and-a-half acre development situated in the Grand Canal Dock.
Cormac WatsonEditor
College Board will decide the fate of Trinity East – College's large-scale capital investment project – in March of this year, with a temporary halting of the project on the table.
According to minutes of an October meeting of the College Board, the chief governing body in Trinity will decide the "future direction of the project", which was previously called TTEC, in two months' time, and the option to pause the project will be considered.
In the meeting, Provost Patrick Prendergast said that the "implications of a pause should be investigated", and it should be considered as an option.
Some Board members, the minutes noted, were unsettled by multinational companies releasing office accommodation and a possible realignment of how they companies do business, as well as uncertainty in society more generally due to the pandemic.
As a result, they cautioned against College making large financial decisions, saying that there would be more clarity on these issues in two to three years.
Prendergast also proposed appointing an independent monitor for the project. Trinity did not, however, confirm whether this monitor was appointed by time of publication.
In an email statement to The University Times, Diarmuid O'Brien, project sponsor for Trinity East and College's chief innovation and enterprise officer, said: "Like all capital projects we are continuously reviewing progress against cost, risk and strategic issues."
"Trinity remains committed to the Trinity East project and the Board has approved the early activation of the site. There is a planning application submitted to Dublin City Council in this regard. The Board has also approved the Strategy for E3RI and we are progressing on our plans for this development."
"In March a preliminary business case will be brought to Board and on the back of this the Board will reflect on the next phase of the project. As agreed at Board there are five options that will be considered at this point", he concluded.
Trinity did not say on what these options are, after this newspaper requested more information on them.
Trinity East – a five-and-a-half acre site – will be situated in the Grand Canal Dock, and bound by Pearse St, Macken St, the DART line and Grand Canal Quay.
In November, College announced that it had submitted the planning application for its proposed Innovation Hub.
It is billed as a space for entrepreneurs, academics, early stage start-ups and innovation teams to convene.
The Innovation Hub will include a new public square with a cafe and seating as well as a landmark tower that dates back to 1862 when the area housed a sugar refinery.
The development of the Innovation Hub will allow College to move forward with its plans for the Grand Canal Innovation District – approved by the government in January – including the establishment of Trinity East.
The project is central to the government's plans to make the area a globally competitive centre for innovation and entrepreneurship.
The state is likely to invest €150 million in the ambitious project over the next decade.


What's bigger than an elephant, but doesn't weigh anything?
What does a piece of toast wear to bed?
Got a good joke? Send it to Randi and she might put it up on the website.
there are fish that fly? In Hawaiian, these fish are called manono and the leap around our islands all the time. They swim really fast underwater, then leap out and spread their fins. They can glide hundreds of feet this way!
Hawaii has bats, too? Our bats are small and cute and they like to climb trees. They are a soft grey color and they're only about the size of a ruler when they spread their wings out. In Hawaiian, bats are called 'ope'ape'a.


text:
JENNIFER CAPRIATI Biography - Famous Sports men and women
Biography » famous sports men and women » jennifer capriati
Name: Jennifer Capriati
Born: 29 March 1976 New York, New York
Jennifer Marie Capriati (born March 29, 1976, in New York City) is a former
World No. 1 women's tennis player from the United States. She won three Grand
Slam singles titles (2001 and 2002 Australian Open, 2001 French Open), and the
women's singles gold medal at the 1992 Olympic Games.
While she was still a toddler, Jennifer was introduced to tennis by her father,
Stefano Capriati, an Italian-American boxer turned tennis coach, who has
continued to coach her in her later professional career.
In 1986, when Jennifer's aptitude for tennis was beginning to show, her family
moved to Florida. At the age of ten, Jennifer enrolled in an intense training
program run by Jimmy Evert (whose daughter Chris Evert also became a world class
tennis player).
In 1989, Capriati became the youngest player to win the French Open junior
singles title at the age of 13 years and 2 months. (The record stood until 1993,
when Martina Hingis won the title as a 12-year-old.) She then went on to win the
junior singles title at the 1989 U.S. Open and the junior doubles titles at both
the U.S. Open and Wimbledon, partnering with Meredith McGrath.
On March 5, 1990, three weeks before her 14th birthday, she became a
professional tennis player. In her debut tournament at Boca Raton, Florida, she
defeated four seeded players while becoming the youngest-ever player to reach a
tour final, where she lost 6-4, 7-5 to Gabriela Sabatini. Despite the loss, her
debut was on the cover of Sports Illustrated the following week. Three months
later, she became the youngest-ever semifinalist at the French Open (aged 14
years and 2 months), where she lost to the eventual champion, Monica Seles. She
then reached the fourth round at both Wimbledon and the U.S. Open that year and
won her first professional singles title in October at San Juan, Puerto Rico.
She finished the year ranked eighth in the world.
In 1991, she reached the semifinals at both Wimbledon and the U.S. Open. She was
Wimbledon's youngest-ever semifinalist after defeating the then-defending
champion Martina Navratilova in the quarterfinals, which was Navratilova's
earliest Wimbledon exit in 14 years. Capriati won two singles titles that year
and her only tour doubles title (in Rome, partnering with Seles).
The biggest moment of Capriati's early career came in 1992, when she won the
women's singles gold medal at the Olympic Games in Barcelona. In the final, she
defeated Steffi Graf (who was the gold medalist four years earlier in Seoul) 3-6,
6-3, 6-4.
» Kim Hunter
» Ingrid Seynhaeve
» Tex Avery
» Otis Redding
» Derek Luke
» Stella McCartney
» Annette Funicello
» Robert Palmer
» Baha'u'llah
» W. C. Fields
» Arlo Guthrie
» Roger Maris
» Willard Gibbs
» Hugo Weaving


text:
Teo Ormond-Skeaping
Sign up for artist updates and more
Teo Ormond-Skeaping was born in Devon and lives and works in Krakow. He graduated from Slade in 2013 with an MA in Fine Art after completing his BA in Photography from Falmouth University College in 2010. Teo was awarded the FOTONOW South West Photographer of the Year award in 2012, and was shortlisted for Sony World Photography People's Choice Moving Image Award and the Red Mansion prize. He is a prolific exhibitor, taking part in exhibitions at the Getty Images Gallery in London, Mall Galleries, Vyner Street Gallery and the Embankment Gallery at Somerset House amongst others. Teo's work centers upon the exploration and attainment of knowledge and the difficulties, responsibilities and ethical conflicts derived from such a pursuit. Using his work to connect with the viewer through a collection of experiences, conscious or unconscious, Teo's collection is an acknowledgement of an interaction or process that is considered by the artist to be of a certain value at a certain time. Teo's Jealous Prize print edition is now held in the Victoria & Albert Museum Permanent Print Collection.
ORBIT ßTERNA
Jealous East
The Shit Show
Mark Denton Esq.
02 Feb 2023 — 26 Feb 2023
Small Tom, Middle Tom, Floor Tom, Crash
02 Mar 2023 — 19 Mar 2023
Jealous North
Sacred Cartography
08 Feb 2023 — 05 Mar 2023


text:
Q: How can I change the last slide opacity to 0 (transparent) if the active slide is the first one? I want to make the last slide opacity to 0 when the centered slide is the first one.

Here is a pen of what I have done so far:
https://codepen.io/Guerreiro-Borghi/pen/NWBRqLa
Any help is welcome!
Thanks!
I tried
slickCarousel.on("afterChange", function(event, slick, currentSlide) {
  const lastSlide = slick.slideCount - 1;

  if (currentSlide === lastSlide ) {
    jumpBack();
  }

  if (currentSlide === 0) {
    $('.last-slide').css('opacity') = '0';
    // set last slide opacity to 0
  } else {
    // set opacity to 1
    $('.last-slide').css('opacity') = '1';
  }
});

A: Add the following to your CSS;
.slick-slide:has(+ .slick-current) {
  opacity: 0.5;
}

and change to whatever opacity you want. Note that this solution does not work on older browser as :has() is a newer CSS selector (Firefox still doesn't support it yet).


text:
Thomas Wentworth Higginson, Massachusetts in the Army and Navy during the war of 1861-1865, vol. 1, Mass. officers and men who died.
You are currently sorting in descending order. Sort in ascending order.
Andersonville, Ga. (Georgia, United States) 2,061 2,061 Browse Search
Wilderness, Va. (Virginia, United States) 922 922 Browse Search
Petersburg, Va. (Virginia, United States) 834 834 Browse Search
Antietam (Maryland, United States) 732 732 Browse Search
Gettysburg (Pennsylvania, United States) 719 719 Browse Search
Spottsylvania (Virginia, United States) 703 687 Browse Search
September 17th, 1862 AD 688 688 Browse Search
Salisbury, N. C. (North Carolina, United States) 643 643 Browse Search
May 6th, 1864 AD 566 566 Browse Search
Fredericksburg, Va. (Virginia, United States) 461 449 Browse Search
Browsing named entities in a specific section of Thomas Wentworth Higginson, Massachusetts in the Army and Navy during the war of 1861-1865, vol. 1, Mass. officers and men who died.. Search the whole document.
Found 162 total hits in 83 results.
... 4 5 6 7 8 9
June 17th, 1864 AD (search for this): chapter 42
Pa.,July 2, 1863. Guilford, Joseph A.,19th Mass. Inf.,Fredericksburg, Va.,Dec. 11, 1862. Guilford, Samuel W., Sergt.,40th Mass. Inf.,Cold Harbor, Va.,June 1, 1864. Guinon, Philip R., Sergt.,19th Mass. Inf.,White Oak Swamp, Va.,June 30, 1862. Gulwin, Thomas R.,20th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gunnison, Benjamin F.,22d Mass. Inf.,Gaines' Mill, Va.,June 27, 1862. Gurney, Charles,37th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gurney, Richard,29th Mass. Inf.,Petersburg, Va.,June 17, 1864. Gustine, Edward F.,5th Batt. Mass. L. A.,Gaines' Mill, Va.,June 27, 1862. Gustus, Charles A.,33d Mass. Inf.,Resaca, Ga.,May 15, 1864. Gutemuth, Frederick,20th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hackett, Horatio B.,35th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hackett, Thomas,9th Mass. Inf.,Wilderness, Va.,May 5, 1864. Hackett, William,35th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hadley, Eugene B.,39th Mass. Inf.,Near Hatcher's Run, Va.,Feb. 6, 1865. Haffner, Thomas,30th M
July 2nd, 1863 AD (search for this): chapter 42
Grush, Joseph Name and rank. Private understood when not otherwise stated.Command.Engagement.Date. Grush, Joseph,40th Mass. Inf.,Drewry's Bluff, Va.,May 16, 1864. Guilford, Charles H.,11th Mass. Inf.,Gettysburg, Pa.,July 2, 1863. Guilford, Joseph A.,19th Mass. Inf.,Fredericksburg, Va.,Dec. 11, 1862. Guilford, Samuel W., Sergt.,40th Mass. Inf.,Cold Harbor, Va.,June 1, 1864. Guinon, Philip R., Sergt.,19th Mass. Inf.,White Oak Swamp, Va.,June 30, 1862. Gulwin, Thomas R.,20th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gunnison, Benjamin F.,22d Mass. Inf.,Gaines' Mill, Va.,June 27, 1862. Gurney, Charles,37th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gurney, Richard,29th Mass. Inf.,Petersburg, Va.,June 17, 1864. Gustine, Edward F.,5th Batt. Mass. L. A.,Gaines' Mill, Va.,June 27, 1862. Gustus, Charles A.,33d Mass. Inf.,Resaca, Ga.,May 15, 1864. Gutemuth, Frederick,20th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hackett, Horatio B.,35th Mass. Inf.,Antietam, Md.,Sept. 17, 1
July 3rd, 1863 AD (search for this): chapter 42
old Harbor, Va.,June 1, 1864. Guinon, Philip R., Sergt.,19th Mass. Inf.,White Oak Swamp, Va.,June 30, 1862. Gulwin, Thomas R.,20th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gunnison, Benjamin F.,22d Mass. Inf.,Gaines' Mill, Va.,June 27, 1862. Gurney, Charles,37th Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Gurney, Richard,29th MaJuly 3, 1863. Gurney, Richard,29th Mass. Inf.,Petersburg, Va.,June 17, 1864. Gustine, Edward F.,5th Batt. Mass. L. A.,Gaines' Mill, Va.,June 27, 1862. Gustus, Charles A.,33d Mass. Inf.,Resaca, Ga.,May 15, 1864. Gutemuth, Frederick,20th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hackett, Horatio B.,35th Mass. Inf.,Antietam, Md.,Sept. 17, 1862. Hackett, Thomas,9th M, 1864. Hall, Francis R.,4th Mass. Inf.,Port Hudson, La.,June 14, 1863. Hall, Isaac C.,1st Mass. Cav.,Aldie, Va.,June 17, 1863. Hall, Jeremiah T., Corp.,2d Mass. Inf.,Gettysburg, Pa.,July 3, 1863. Hall, Levi,4th Mass. Cav.,John's Island, S. C.,July 17, 1864. Hall, Samuel M., Corp.,34th Mass. Inf.,Piedmont, Va.,June 5, 1864.


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text:
NewSimulationShoes
scarpe nike air max
Portugal's banking tiff puts Mário Centeno under pressure
NewSimulationShoes, May 15, 2020
Mário Centeno is chairing efforts within the Eurogroup to agree on a recovery plan to mitigate COVID-19's economic impact | Nuno Fox/AFP via Getty Images
Dispute with PM António Costa revives speculation on future of Eurogroup president.
Paul Ames
5/14/20, 1:05 PM CET
Updated 5/14/20, 2:34 PM CET
LISBON — The Portuguese government has averted a crisis that sparked speculation Eurogroup President Mário Centeno would resign as finance minister because of a tiff over a €850 million state loan to a loss-making bank.
After a three-hour huddle with Centeno late Wednesday, Prime Minister António Costa issued a statement reaffirming his "personal and political confidence" in the finance minister. The meeting followed days of tension between the two men and opposition calls for Centeno to resign.
"Centeno can't stay," Rui Rio, leader of the center-right Social Democratic Party, tweeted earlier Wednesday. "A PM who keeps a disloyal minister is in trouble."
Although Costa and Centeno patched up their differences, the dispute intensified speculation that Centeno won't seek a second term as president of the Eurogroup and will step down as minister this summer, possibly in a move to the governor's office at Portugal's central bank.
The spat began last week when Costa was embarrassed in parliament, seemingly unaware his government had transferred €850 million to a fund propping up Novo Banco. Costa had previously told parliament there would be no new state aid before the results of an audit into Novo Banco's finances.
With Costa under pressure, Centeno admitted Tuesday to a "failure in communication" with the PM's office over the transfer. However, Centeno said the loan was accounted for in the government's 2020 budget and the state was legally bound to transfer the money under a 2014 plan to support Novo Banco.
"Centeno is weakened by this incident, even if he was in the right as far as the legal contract to support Novo Banco is concerned," said Helena Garrido, professor of economic journalism at Lisbon's Universidade Lusófona.
Novo was created from the wreckage of Banco Espírito Santo, Portugal's second-largest bank, which fell victim to the financial crisis in 2014. The new lender has struggled to avoid losses and continues to receive capital injections through a resolution fund financed by loans from the state and the financial sector.
Despite the dispute, Costa was loath to lose his finance minister as he coordinates efforts to keep the economy afloat through the pandemic. The European Commission's forecast of a 6.5 percent contraction in Portugal's economy this year is among the more optimistic.
Centeno is also chairing efforts within the Eurogroup — finance chiefs from the 19 euro countries — to agree on a recovery plan to mitigate COVID-19's economic impact.
Nicknamed the "Cristiano Ronaldo of finance," Centeno has been a star in the Socialist administration that came to power in 2015. He's overseen five years of unbroken growth and was credited last year with securing the first balanced budget since democracy was restored to Portugal in the mid-1970s.
However, Centeno has faced criticism for his handling of sensitive Eurogroup meetings, and there is growing speculation that he will step down at the end of his term in July. In Lisbon, he is touted as a possible replacement for Bank of Portugal Governor Carlos Costa who is also due to leave this summer.
This article is part of POLITICO's premium policy service: Pro Financial Services. From the eurozone, banking union, CMU, and more, our specialized journalists keep you on top of the topics driving the Financial Services policy agenda. Email pro@politico.eu for a complimentary trial.
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How Prisons are Handling Covid-19
Deja Heard December 1, 2020 0 0
When Covid-19 first began to change the world drastically, many news stations and articles always covered how people should stay safe during the pandemic. They gave tips like always wearing your mask or staying six feet away if you're standing in a grocery store checkout line, but as the death rate increased no one really mentioned the people who are serving time in prison during this pandemic. Prisons in the United States have an estimated total of over 2.3 million people incarcerated as of today.
With the number of people currently in prison in the country, it's a huge question on how workers in the prison are handling the pandemic to make sure not only that they're safe but the safety of the inmates also. Inmates were given masks to attempt to slow down any spreads of Covid-19. Also, President Trump granted early release for many inmates that served half of their prison sentence, just to decrease the population in prisons and help slow the spread of Covid-19.
However, even with masks, some social distancing, and early compassionate release for some inmates the rates of Covid-19 in prisons are only increasing by the day. According to the New York Times, "More than 252,000 incarcerated people and staff have been infected with coronavirus and at least 1,450 have died."
The number of cases, that's been reported could be higher since they are conducting limited testing on incarcerated people. Many prisons won't test incarcerated people who die after showing symptoms of Covid-19.
According to the Equal Justice Initiative, the largest outbreak of Covid-19 is linked to correctional facilities. Those correctional facilities being Marion Correctional Institution, with 2,443 cases, Miami-Dade County Jail, with 2,099 cases, and Ohio's Pickaway Correctional Institution, with 1,791 cases. The largest coronavirus case cluster in a prison is at California's San Quentin State Prison, there were more than 2,600 incarcerated people and staff members have been infected and 25 incarcerated people have died.
CBS Sunday Morning News created a video hosted by Ted Koppel where he explains prison time during the time of coronavirus. He speaks with current and former inmates to explain the living conditions in many prisons, which could also be one of the reasons that Covid-19 spreads in prisons. One inmate describes that many prisoners are "on top of each other" inside a room. This confirms that it's hard for inmates to actually social distance in the prisons that they are serving their time in.
As the pandemic continues and many medical companies are rushing to create a vaccine, it's important to wonder if the people that are in prison will be able to live in conditions that won't make the virus spread so quickly. Cases continue to rise in the United States and hopefully, when it comes to combatting Covid-19, this includes ways for it to be handled in the many prisons in America also. Many inmates are in there serving time for a crime they committed (some are even in there and actually are innocent), they are still human beings that deserve the common safety that many that aren't in prison have to combat the spread of Covid-19.
Tags coronavirus COVID-19 Health prison prison systems
Deja Heard
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This Club is a Sit & Sew for the Birthstone Series Block of the Month. Participants must be signed up for the Birthstone Series BOM to join. Each month we will demonstrate that month's block and give tips and tricks for finishing each block. Club begins July 24, 2018 and ends June 25, 2019 from 10:30am - 1:30pm. Evening session may be scheduled if there are at least 3 participants interested. There is no charge for this event and refreshments will be served.