text:
\section{Introduction}

Dusty, intensely star-forming galaxies (SFR$ >10$\,$M_\odot$\,yr$^{-1}$) are the dominant contributors to the $z \geq 0.5$
cosmic SFR density \citep[e.g.,][]{Flores99,Elbaz02,Pozzi04,LeFloch05}.
The thermal dust emission from these galaxies accounts for
much of the cosmic IR background, which contains half of the
radiation energy of the extragalactic background light
\citep{Hauser01,Lagache05,Dole06}.
Therefore understanding the IR SEDs of the dusty star-forming galaxies is essential for
mapping the evolution of star formation as a function of
cosmic time, and is a key observational ingredient of our understanding
of galaxy evolution.

Detailed studies of the IR SEDs over the full range of 3-1000\,$\micron$
have only been carried out for nearby galaxies \citep[e.g.,][]{Dale05}, finding that the IR SED shape is
correlated with dust temperature \citep{Soifer91,Dale02,Chapman03,Lagache03}.
Interestingly, despite this diversity
of SED shapes, local galaxies' mid-IR (10-30\,$\micron$)
luminosities are tightly correlated with their total IR luminosities
with a scatter of $\sim 0.3$\,dex \citep{Chary01,Papovich02,Takeuchi05b,Dale05}.

This {\it local} observation has often been used as
a key assumption in studies exploiting
deep mid-IR imaging from the {\it Infrared Space
Observatory} at 15\,$\micron$ and the {\it Spitzer Space
Telescope} at 24\,$\micron$ \citep[e.g.,][]{Flores99,Flores04,Zheng04,Hammer05,Bell05,Melbourne05}.  Such studies
have found significantly enhanced star formation at $0.5 \la z \la 1$,
compared to the present day.  Yet, this conclusion rests critically
on the extent to which mid-IR luminosities reflect the total IR luminosity.
Unfortunately, only a small fraction of mid-IR detected
sources can be  individually detected in the far-IR bands
(e.g., {\it Spitzer} 70 \& 160\,$\micron$), owing to limited signal-to-noise
ratio (S/N) and source confusion \citep{Dole04b,Frayer06}.
Thus, testing of this key assumption remains limited to the brightest
sources \citep[e.g.,][]{Sajina06,Borys06} or is indirect \citep[e.g.,][]{Appleton04,Yan05,Pope06,Marcillac06}.

The goal of this paper is to explore the average IR SEDs of a
stellar mass-limited sample
$z \sim 0.7$ galaxies in the extended Chandra Deep Field-South (E-CDFS).
In previous works, we have shown that stacking noise-limited
images of a set of galaxies allows one to securely detect the mean flux of the
galaxy set substantially below the individual detection limit \citep{Zheng06,Dole06}.
Here, we stack at longer wavelengths, i.e., 70\,$\micron$ and
160\,$\micron$, to empirically determine the population-averaged
IR SED.  We combine these results with morphologies, and average
fluxes at shorter wavelengths, to place constraints on the
dust extinction and SEDs of these galaxies.
In \S 2 we describe the multi-wavelength data used to construct galaxy SEDs
and the samples of $z\sim 0.7$ galaxies. \S 3
presents our stacking methods. In particular we test the results of stacking
noise and confusion limited 70 and 160\,$\micron$ images.
In \S 4 we present the properties of the average SEDs.
Discussion and conclusion are given in \S 5. Throughout this paper,
we assume $\Omega_M$\,=\,0.3, $\Omega_{\Lambda}$\,=\,0.7 and
$H_0$\,=\,70\,km\,s$^{-1}$\,Mpc$^{-1}$ for a $\Lambda$-CDM cosmology.
All magnitudes are given in the Vega system except where otherwise specified.

\section{The data and samples}

\subsection{The data}

We use {\it Spitzer} 24, 70 and 160\,$\micron$ data to study the
thermal dust emission of $z \sim 0.7$ galaxies.
In addition, we include deep ultraviolet data from the
{\it Galaxy Evolution Explorer} (GALEX: \citealt{Martin05a}),
optical data from the Classifying Objects by Medium-Band Observations
(COMBO-17; \citealt{Wolf03}) survey,
and four band (3.6, 4.5, 5.8 and 8.0\,$\micron$) Infrared
Array Camera (IRAC; \citealt{Fazio04}) data to construct the
stellar SED of a galaxy.
These data cover wavelength range from 0.15\,$\micron$ to
160\,$\micron$ in the observed frame, equal to the rest-frame range
$\sim$0.09 to $\sim$100\,$\micron$ for $z=0.7$.

The COMBO-17 survey has imaged the $30\farcm5\times 30\arcmin$ E-CDFS in
five broad ($U, B, V, R$ and $I$) and 12 medium optical bands, providing
high-quality astrometry  (uncertainties $\sim 0\farcs 1$) based on a very deep
$R$-band image (26\,mag at the 5$\sigma$ limit), photometric
redshifts ($\delta z/(1+z) \sim 0.02$ at $m_{\rm R}<23$; \citealt{Wolf04}) and
stellar masses \citep{Borch06}
for $\sim 11,000$ galaxies with $m_{\rm R}<24$. We use the photometric
redshift and stellar mass catalogs to select galaxy samples.

GALEX ultraviolet
observations provided deep far-ultraviolet (FUV; 1350--1750\AA) and
near-ultraviolet (NUV; 1750--2800\AA) images centered on the E-CDFS.
The FUV and NUV images have a field of view of one square degree, a typical
point-spread function (PSF) of full width at half-maximum (FWHM) $\sim5\arcsec$, a resolution of 1$\farcs$5\,pixel$^{-1}$ and
a depth of 3.63\,$\mu$Jy at the 5$\sigma$ level.
The data reduction and source detection is described in
\citet{Morrissey05}.

The deep IRAC 3.6, 4.5, 5.8 and 8.0\,$\micron$ imaging data and MIPS 24, 70 and
160\,$\micron$ imaging data were obtained as part of the first run of MIPS GTO
observations \citep{Rieke04}. A rectangular field of $\sim
90\arcmin\times30\arcmin$ was observed in all bands (with small shifts between
different bands). The effective exposure time is 500\,s for the four IRAC
band images, 1378\,s\,pix$^{-1}$ for MIPS 24\,$\micron$,
600\,s\,pix$^{-1}$ for 70\,$\micron$ and
120\,s\,pix$^{-1}$ for 160\,$\micron$.
IRAC 3.6 and 4.5\,$\micron$ images have a PSF of FWHM $\sim 1\farcs$8
and 5.8 and 8.0\,$\micron$ images have a PSF of FWHM $\sim 2\farcs$0
\citep{Huang04}.
The 24\,$\micron$ image has a PSF of FWHM $\simeq$6$\arcsec$.
Sources are detected at 24\,$\micron$ down to 83\,$\mu$Jy (at 80\% completeness; see \citealt{Papovich04} for details of data reduction, source detection
and photometry). The 70\,$\micron$ image is characterized by a PSF of
FWHM\,$\simeq$\,18$\arcsec$ and a resolution of 9$\farcs$9\,pixel$^{-1}$.
The 160\,$\micron$ image has a PSF of FWHM\,$\simeq$\,40$\arcsec$ and a
resolution
of 16$\arcsec$\,pixel$^{-1}$. Sources with fluxes of $f_{70}>$15\,mJy
can be individually resolved at 70\,$\micron$ and of $f_{160} >
50$\,mJy at 160\,$\micron$ \citep[see][for details]{Dole04a}.

We take COMBO-17 astrometry as the reference coordinate and
cross-correlate all other band catalogs with the COMBO-17 catalog.
In each cross-correlation, we use bright stars and compact sources to estimate
the systematic offsets and uncertainties between two coordinates.
A position tolerance of 4$\sigma$ uncertainty is adopted so that objects in
the two catalogs having coincident coordinates within the tolerance
(corrected for the systematic offsets) are identified as the same objects.
The nearest COMBO-17 object is chosen if multiple ones exist within the
tolerance.
The adopted tolerances are 2$\farcs$5 (FUV), 3$\farcs$0 (NUV),
1$\farcs$0 (3.6\,$\micron$), 1$\farcs$2 (4.5\,$\micron$), 1$\farcs$5
(5.8 and 8.0\,$\micron$) and 2$\farcs$2 (24\,$\micron$). For the 70 and
160\,$\micron$ catalogs,  we firstly cross-correlate them
with the 24\,$\micron$ catalog as individually-detected 70 and 160\,$\micron$
sources are bright at 24\,$\micron$; we then associate 70 and
160\,$\micron$ objects with COMBO-17 objects using the coordinates of the
corresponding 24\,$\micron$ sources.  The tolerance between 70/160
and 24\,$\micron$ is 5$\arcsec$/16$\arcsec$ respectively.

\begin{figure}[] \centering
  \includegraphics[width=0.48\textwidth,clip]{f1.ps}
\caption{The rest-frame color $U-V$ versus stellar mass for a
sample of 1092 galaxies of known (GEMS) morphology in the redshift slice $z=0.7\pm0.05$.
Open symbols show the objects detected individually at 24\,$\micron$
($f_{24}>83\mu$Jy) and skeletal symbols represent individually-undetected
ones. Most red ($U-V >$0.7) elliptical/lenticular
galaxies and blue ($U-V <$0.7) small ($M_\ast <$10\,$M_\odot$)
spiral/irregular/peculiar galaxies are not individually detected at
24\,$\micron$. The number of objects in each category is given in the brackets.
The {\it dashed} line shows the mass cut $M_\ast = 10^{10}$\,$M_\odot$ that we
apply in the subsequent analysis.}\label{mass_UV}
\end{figure}

\begin{figure*}[] \centering
  \includegraphics[width=0.3\textwidth,clip]{f2a.ps}
  \includegraphics[width=0.3\textwidth,clip]{f2b.ps}
  \includegraphics[width=0.3\textwidth,clip]{f2c.ps}
  \parbox[t]{0.3\textwidth}{\hskip 1cm }
  \includegraphics[width=0.3\textwidth,clip]{f2d.ps}
  \includegraphics[width=0.3\textwidth,clip]{f2e.ps}
\caption{Observed MIPS 24, 70 and 160\,$\micron$ image sections (15$\arcmin\times15\arcmin$) in the extended CDFS
  and simulated 70 and 160\,$\micron$  images.
  The simulated 70 and 160\,$\micron$ images are created by
  degrading the 24\,$\micron$ image to the 70 and 160\,$\micron$
  resolution.  No additional noise is added to the simulated
  images.}\label{images}
\end{figure*}

\subsection{The samples}\label{samples}

We combine spectroscopic redshifts from the VLT VIMOS Deep Survey \citep{LeFevre05} and the GOODS survey \citep{Vanzella05,Vanzella06} with photometric redshifts
from the COMBO-17 survey \citep{Wolf04} to select sample
galaxies.
The E-CDFS has HST imaging from the Galaxy Evolution from Morphology and SEDs
\citep[GEMS;]{Rix04} Survey. Visually classified morphologies
are available for 1458 galaxies with $m_{\rm R}<24$ in a thin redshift slice
$z=0.7\pm0.05$ \citep{Bell05}.
Of the 1458 galaxies, 1114 galaxies have all observations,
including GALEX,  IRAC and MIPS observations.
To avoid contamination from active galactic nuclei (AGNs), we remove 22 X-ray detected sources
in the Chandra 250\,ks observation \citep{Lehmer05},
leaving a sample of 1092 galaxies. Spectroscopic redshifts are available for 64\% of the sample galaxies.
The contribution from the X-ray-undetected AGNs to the total 24\,$\micron$ luminosity of $z<1$ galaxies is suggested to be $<\sim 10$\% \citep{Zheng06,Brand06}. This will not have significant effects on our results.

The sample demographics are shown in Figure\ \ref{mass_UV}.  The
sample is limited by $R$-band apparent magnitude ($m_{\rm R}<24$),
corresponding to approximately the rest-frame $B$-band at $z \sim 0.7$.
Accordingly, the completeness of the sample, in terms
of stellar mass, is a strong function of color: the
mass limit for red (old or dusty) galaxies is
$M_\ast \sim 10^{10}$\,$M_\odot$ whereas blue
galaxies can be included down to almost
$M_\ast \sim 10^{9}$\,$M_\odot$.  We choose to impose
a stellar mass cut of $M_\ast \geq  10^{10}$\,$M_\odot$ in
what follows; not only are almost all 24\,$\micron$ emitters
above this mass cut, but also the completeness of this
sample is not a strong function of color (i.e., age or dust
obscuration). We also select another sample to explore the relationship between the IR SED shape and 24\,$\micron$ luminosity; we extend the redshift slice to  0.6$<z<$0.8 to increase the number of sample galaxies. The final sample then comprises
some 579 galaxies with $M_\ast \ge 10^{10}$\,$M_\odot$ in the redshift
range 0.6$<z<$0.8. Of them, 218 are individually-detected at
24\,$\micron$ with fluxes in excess of $>83\mu$Jy; none is individually
detected at 70 or 160\,$\micron$.
X-ray detected sources have been excluded.

\section{Image stacking}

As outlined earlier, current missions are unable to yield
individual detections for the vast majority of intermediate-redshift
objects at far-IR wavelengths, owing both to
contributions from instrumental noise and confusion noise.
In order to place constraints on the shape of the IR
SEDs of `typical' star-forming intermediate redshift
galaxies, stacking on the positions of known star-forming
galaxies can lower the effective noise \citep{Zheng06}, allowing
detection of the `average' galaxy.  In \citet{Zheng06}
we presented a description of stacking of 24\,$\micron$
data (in that case to resolve the 24\,$\micron$ luminosity
of dwarf galaxies); we  briefly summarize the most important aspects of 24\,$\micron$
stacking in \S \ref{stack:24}.  The focus of this paper is stacking
at longer wavelengths, at 70\,$\micron$ (PSF FWHM 18$\arcsec$) and
160\,$\micron$ (FWHM 40$\arcsec$), discussed in \S \ref{stack:70160}.

\subsection{FUV, NUV and MIPS 24\,$\micron$ image stacking}\label{stack:24}

While much of this paper describes stacking results for subsamples
that are individually detected at 24\,$\micron$, some subsamples
are not individually detected at 24\,$\micron$.  Furthermore,
the 24\,$\micron$, FUV and NUV images share many of the same characteristics:
the PSFs have similar FWHM, and at each wavelength, $\ga 1/2$
of the extragalactic background at that wavelength is
resolved by these images (i.e., the images are only
mildly confusion-limited; e.g., \citealt{Papovich04,Xu05,Dole06}).
Accordingly, stacking of
FUV, NUV and 24\,$\micron$ images is carried out in the same
way \citep[see][for more details]{Zheng06}.

Three basic steps are adopted to derive the mean fluxes for galaxy subsets.
First, we subtract all individually-detected sources from the
images. This is done using the software tool STARFINDER\footnote{STARFINDER
  gives identical results within the errors to the tool ALLSTAR in IRAF
  package.} \citep{Diolaiti00}
with an empirical PSF constructed from 18/42/56 bright point sources
at 24\,$\micron$/FUV/NUV respectively.
Then we perform mean stack of the residual image postage stamps centered on
the optical coordinates of the objects that are individually undetected
in the subset of interest. An aperture of 5$\arcsec$ is used to
integrate the
central flux of the mean-stacked image and estimate the background from the
outer regions. Aperture corrections of a factor of 1.88/1.19/1.14, derived
from the empirical PSF at 24\,$\micron$/FUV/NUV respectively, are adopted
to calibrate the stacked fluxes to the total fluxes.
Last we sum the fluxes of individually-detected sources and the
stack flux of individually-undetected sources in each galaxy subset,
giving the mean 24\,$\micron$/FUV/NUV fluxes.
Uncertainties are derived from bootstrapping.

\begin{figure*}[] \centering
  \includegraphics[width=0.49\textwidth,clip]{f3a.ps}
  \includegraphics[width=0.49\textwidth,clip]{f3b.ps}
\caption{Recovery of the mean fluxes of
  {\it randomly} distributed 24\,$\micron$
  sources through stacking after convolution to the
  70\,$\micron$ ({\it left}) and 160\,$\micron$ ({\it right}) resolution.
  The fluxes were taken from the 8255 individually-resolved sources in the
  $\sim1\fdg 5\times0\fdg 5$ 24\,$\micron$ image and were split into 18
  flux bins in the range from 83\,$\mu$Jy to 10\,mJy. The number of
  objects in each bin decreases with
  increasing flux. Three measures of the stack
  flux are presented for each bin: the integrated
central flux of the {\it mean} stack using the {\it mean} background for
sky subtraction; the integrated central flux of the {\it mean} stack using the
{\it median} background; and the integrated central flux of the {\it median}
stack using the {\it median} of the background (see text for details).
  Errorbars are derived from bootstrapping. While the flux can be recovered
  with little bias at 70\,$\micron$ resolution, flux recovery at
  160\,$\micron$  leads to a systematic overestimate.
}\label{random}
\end{figure*}

\begin{deluxetable*}{ccccccccccccccccccc}
  \centering \tabletypesize{\scriptsize} \tablewidth{0pt}
  \tablecaption{The numbers of sources and average fluxes for
  18 flux bins composed of 8255 individually-resolved 24\,$\micron$ sources.
  \label{stackbin}}
\tablehead{No. & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18}
\startdata
$<f_{24}>$($\mu$Jy) & 87 & 99 & 115 & 132 & 153 & 176 & 204 & 235 & 271 & 311 & 359 & 416 & 480 & 559 & 637 & 747 & 863 & 1946 \\
Number & 1070 &  1259 &  1067 &  894 &  803 &  683 & 508 &  418 &  328 & 267 &  222 &  174 &  134 &  98 &  72 &  46 &  39 &  169
\enddata
\end{deluxetable*}

\subsection{MIPS 70 and 160\,$\micron$ image stacking} \label{stack:70160}

The 70\,$\micron$ and 160\,$\micron$ PSFs are considerably larger than
those at shorter wavelengths, yielding confused
images, resolving only the brightest, relatively nearby sources --- some
$<$30\% of the extragalactic background at this wavelength \citep{Dole04a}.
Galaxies at $0.6<z<0.8$ are heavily confused in all but the
brightest cases \citep{Dole04a,Lagache03,Lagache04}, requiring stacking
to gain insight into their long-wavelength IR SEDs \citep[see, e.g.,][]{Dole06}.

The large angular extent of the long-wavelength PSFs
poses a significant challenge for those wishing to estimate
their average properties.  At $z$\,=\,0.7, the PSF size of
the 70\,$\micron$ image
(FWHM\,$\simeq 18\arcsec$)
corresponds to a physical scale of $\sim$130\,kpc. For the 160\,$\micron$ image
(PSF FWHM\,$\simeq 40\arcsec$),  the corresponding scale is $\sim$290\,kpc.
Thus, the stacking results are a reflection of the IR-luminosity
weighted two-point correlation function on $\ga 100$\,kpc scales,
overestimating the true average fluxes of the galaxies of interest.

In order to understand this source of systematic error
in better detail, we carried out some simulations where
synthetic 24\,$\micron$ data (using the observed positions
and fluxes of individually-detected 24\,$\micron$ sources)
are degraded in resolution to the resolution
of the 70\,$\micron$ and 160\,$\micron$ data.  For the purposes
of this test, we assume a constant ratio between 24\,$\micron$ flux
and the wavelength of interest.  We explore two cases.   First,
the positions are randomly scrambled (i.e., the relative
brightnesses of galaxies are preserved but their positions
are random).  This test gives an indication of the
systematic effects of stacking randomly-positioned sources.
The second case is when both the fluxes and positions of
sources are preserved.  This second case is our `best'
estimate of the likely systematic uncertainties of stacking
in a realistically-clustered case.
Figure~\ref{images} shows the 24\,$\micron$ images degraded to the 70 and
160\,$\micron$ resolution respectively, compared with the
observed MIPS 24, 70 and 160\,$\micron$ images.
Because of confusion, only a handful of bright 24\,$\micron$ sources can
be individually resolved in the degraded images.
Through stacking the mean flux can be estimated for 24\,$\micron$ source
subsets; the accuracy of the recovery of the mean flux
shows how well stacking works at that corresponding image
resolution.

\begin{figure*}[] \centering
  \includegraphics[width=0.49\textwidth,clip]{f4a.ps}
  \includegraphics[width=0.49\textwidth,clip]{f4b.ps}
\caption{The recovery of the mean properties of
  24\,$\micron$-detected sources at their
  observed positions.  {\it Left}: Stacking results at
  70\,$\micron$ resolution. {\it Right}: Stacking results at 160\,$\micron$
  resolution.   The errorbars reflect the variance from bootstrapping.
}\label{cdfs24}
\end{figure*}

\subsubsection{Stacking randomly distributed sources}\label{stack:random}

The whole 24\,$\micron$ mosaic image of the E-CDFS covers a
rectangular sky area of $\sim1\fdg5\times 0\fdg5$ and contains 8255
individually-detected sources at 5$\sigma$ detection limit (83\,$\mu$Jy).
The vast majority of the 8255 sources ($f_{24}>83\mu$Jy) are point sources.
Replacing all sources with the 24\,$\micron$ PSF (empirically constructed from
bright stars), we generated an artificial 24\,$\micron$ image having the 8255
sources {\it randomly} distributed into a $\sim1\fdg5\times 0\fdg5$ blank
field.  The artificial 24\,$\micron$ image was then
degraded to the 70 and 160\,$\micron$ image resolution respectively.
The degraded images are then stacked at the 24\,$\micron$ source positions, giving indications as to
the expected quality of stacking results at 70\,$\micron$ and 160\,$\micron$.
We note that the replacement of the background
blank field with the PSF-subtracted
24\,$\micron$ image (having some contribution from individually-undetected
sources) does not modify our results significantly in the
mean, adding only some modest additional scatter.

The 24\,$\micron$ sources were sorted into 18 bins in flux, ranging from
83\,$\mu$Jy to 10\,mJy \citep[see also][]{Dole06},
with $\sim 1000$ objects per bin at faint flux levels,
and $\la 100$ per bin at brighter fluxes. The numbers of sources
and average 24\,$\micron$ fluxes per bin are listed in Table~\ref{stackbin}.
In real stacks,
we choose to PSF-subtract out the individually-detected sources,
stacking the remaining image (in order to reduce bias from bright sources
in background estimates).  In the real 70 and 160\,$\micron$
images, we detect $\sim 130$ sources in each image.  Thus, we must
subtract approximately this many bright sources from the simulated
images before stacking to avoid unrealistically biasing the results.
Ideally, one would subtract all detected sources in the simulated
70 and 160\,$\micron$ images; unfortunately, as the simulated
and real 70 and 160\,$\micron$ images have (unavoidably) somewhat
different noise and brightness distribution properties, the
number of detected sources is different, and substantially higher
in the case of (the rather deeper) simulated 70\,$\micron$ data.
Thus, we choose to PSF-subtract the 169 brightest sources (the brightest of
the 18 original bins) before stacking for the remaining 17 bins.

For each flux bin, postage stamps were cut
from the simulated image centered on the
positions of the sources and were stacked.
The size of postage stamps is 2$\farcm$5$\times$2$\farcm$5 for the
70\,$\micron$ image and 4$\arcmin \times 4\arcmin$ for the 160\,$\micron$
image, allowing for a proper background estimate.
Two stacked images were created for each bin through averaging or medianing.
For the stacked 70\,$\micron$ postage stamp images, an aperture of radius
0$\farcm$49 is used to integrate the central stack flux and an annulus with
inner radius 0$\farcm$82 and outer radius 1$\farcm$23 to estimate the
background. For the stacked 160\,$\micron$ postage stamp images, the
corresponding aperture is of radius 1$\farcm$07 and the corresponding
annulus is of inner radius 1$\farcm$07 and outer radius 1$\farcm$87.
The mean and median of pixels in the annulus
region were taken as the background
of the mean-stacked image for sky subtraction.  Only the median (nearly
identical to the mean) was adopted as the background of the median-stacked
image. Therefore, we derive three measures of the stack flux for each
subsample: the integrated
central flux of the {\it mean} stack using the {\it mean} background for
sky subtraction; the integrated central flux of the {\it mean} stack using the
{\it median} background; and the integrated central flux of the {\it median}
stack using the {\it median} of the background.
Finally, aperture corrections derived from model PSFs
(http://ssc.spitzer.caltech.edu/mips/psffits/) were applied to correct the
estimates of stack flux to the total. The aperture correction is a factor of
1.30 for 70\,$\micron$,  1.25 for 160\,$\micron$ using the
median background, and 1.34 for 160\,$\micron$ using the mean
background.

Figure~\ref{random} shows the results of stacking {\it randomly} distributed
sources at 70 and 160\,$\micron$ resolution. Uncertainties are derived from
bootstrapping.
At 70\,$\micron$ resolution, the mean flux of 24\,$\micron$ sources of
comparable flux can be properly recovered over a flux range of one order
of magnitude.  At 160\,$\micron$ resolution, recovered fluxes
are of reasonable quality at bright limits, and become progressively
more biased (by $\sim 20$-50\%) towards fainter limits.
After some investigation, it became clear that the
the stack recovery at 160\,$\micron$ is
correlated with the number of objects in the stack bin.
The increase of objects in number leads to an increase of the overlap
between these objects within a fixed area. This increasing overlap
leads to an increase of the stack flux relative to the input flux.
By stacking a number of identical sources without background and
foreground sources, we obtained similar results as shown in Figure~\ref{random}.
Indeed a point source contains 80\% of its flux in an area of 3.6 arcmin$^2$
in the 160\,$\micron$ imaging and an area of 0.9 arcmin$^2$ in the
70\,$\micron$ imaging.
To fill in a $1\fdg5\times 0\fdg5$ field, it requires around 750
160\,$\micron$ sources or 3000 70\,$\micron$ sources, compared to $\sim$\,50 -
1260 objects in the stack bins.
Therefore the stack results at 70\,$\micron$  are little affected by the
overlap between the stack objects but the stack results at
160\,$\micron$ are significantly influenced for stack bins of $\sim 1000$
objects.

The three measures of the stack flux are nearly identical within the errorbars
($\sim$\,10\%) for simulated 70\,$\micron$ stacking
and slightly different for simulated 160\,$\micron$ stacking, in
particular for the low flux bins, although the scatter is significant
($\sim$\,10 - 25\%).  Note that we divided sources into stack bins in terms of
their fluxes. For the stack bins having sources whose fluxes span a wide
range, the median stack
substantially underestimates the mean flux.

\subsubsection{Stacking sources at their observed positions}

In order to build a more realistic picture of the expected uncertainties
from stacking at 70\,$\micron$ and 160\,$\micron$, we repeat the last
analysis except we keep both the fluxes and positions of the sources
fixed to those observed at 24\,$\micron$.  This gives insight into the
influence of clustering on the results.  In this test, the observed
24\,$\micron$ map was degraded to the 70 and 160\,$\micron$
resolution respectively, as shown in Figure~\ref{images}.
As previously, sources were split into 18 bins in 24\,$\micron$
flux, and the stacking results for each flux bin were calculated.
Figure~\ref{cdfs24} shows the results.  Comparing the results
with Figure~\ref{random}, one can clearly see that
the mean and median recovered fluxes are significantly affected
by the clustering of sources on the sky.
In general, our test suggests that stacking subsets of 70\,$\micron$
and 160\,$\micron$ sources may overestimate the mean flux somewhat; typical
biases are $\sim 20$\%, with uncertainties of order $\sim 20$\%.
Yet, the magnitude
and direction of these effects on different subsamples
are difficult to estimate {\it a priori}.
Figure~\ref{cdfs24} demonstrates that
from bin to bin, the source clustering may lead to either an increase or a
decrease of the mean flux of the source subsample, depending on the actual
distribution and density of sources of different brightnesses on the sky.

\subsubsection{Stacking $z\sim$\,0.7 galaxies}\label{stackgal}

The above tests demonstrate that it will be difficult to
estimate the mean flux at 70\,$\micron$ and 160\,$\micron$
to better than $\sim 20$\%, and that the extent of the bias towards higher
or lower flux is difficult
to predict with accuracy.  In this paper, we adopt a
pragmatic approach: for each subsample that is stacked
at 70\,$\micron$ and 160\,$\micron$, we attempt to derive an individual
`bias correction' based on the 24\,$\micron$ image.
Sources bright at 24\,$\micron$ are generally bright at 70 and
160\,$\micron$, although the 24\,$\micron$ to 70 or 160\,$\micron$
flux ratio varies from object to object at a scatter at the 0.5\,dex level \citep{Dale05}.   Thus, we adopt the simplistic
assumption that the 24\,$\micron$ image can be taken as a good proxy
for the 70 or 160\,$\micron$ image at
$\simeq$\,6$\arcsec$ resolution and high S/N; 70\,$\micron$ and 160\,$\micron$
resolution images derived under this assumption (Fig.~\ref{images}) appear
a reasonable description of the real 70\,$\micron$ and 160\,$\micron$ images.
Thus, for a given subset of $z\sim 0.7$ galaxies, we estimated precisely the mean
24\,$\micron$ flux from their 24\,$\micron$ images. Then we degraded the
24\,$\micron$ images to 70 and 160\,$\micron$ image resolution. Two stack
fluxes were estimated by stacking each of the two sets of degraded images.  By
comparing the stack fluxes to the actual mean 24\,$\micron$ flux of the galaxy
subset, we obtained empirical corrections, which were applied to
the corresponding 70 and 160\,$\micron$ stack fluxes of the subset of
$z\sim 0.7$ galaxies, respectively.  Uncertainties in this correction
are applied also, in quadrature, to the derived stacking results at 70\,$\micron$
and 160\,$\micron$.

\begin{figure}[] \centering
  \includegraphics[width=0.48\textwidth,clip]{f5.ps}
\caption{The average SED, spanning a factor of 1000 in wavelength, as a function of morphological type for
  24\,$\micron$ detected ($f_{\rm 24}>83\mu$Jy) galaxies with $M_\ast \ge
  10^{10}$\,$M_\odot$ in a thin redshift slice 0.65$\leq z \leq$0.75. Vertical
  errorbars are the 1$\sigma$ uncertainties derived from bootstrapping and
  horizonal errorbars show the band widths. The number of objects in each
  morphology bin is given in the brackets.}\label{sedsir}
\end{figure}

\begin{figure}[] \centering
  \includegraphics[width=0.48\textwidth,clip]{f6.ps}
\caption{Average SED as a function the 24\,$\micron$ luminosity for
  galaxies with $M^\ast\geq 10^{10}$\,$M_\odot$ in the redshift range
  0.6$<z<$0.8. The vertical errorbars show the 1$\sigma$ uncertainties
  and horizonal errorbars show the band widths. The 579 galaxies are split
  into three bins in terms of their 24\,$\micron$
  luminosities: $L_{24}$-high, $L_{24}$-medium and $L_{24}$-low.
  The numbers in brackets denote the number of objects included in each bin.
}\label{sedsmass}
\end{figure}

\begin{deluxetable*}{lcccccccc}
  \centering \tabletypesize{\scriptsize} \tablewidth{0pt}
  \tablecaption{Average luminosities of massive-galaxy subsets in the redshift
  slice $0.6<z<0.8$.
  \label{table}}

\tablehead{ N$_{\rm obj}$ & $\log <M^\ast>$ & $\log \nu L_{\nu,24}$ & $\log
  \nu  L_{\nu,70}^\mathrm{a}$  & corr.(70) & $\log \nu L_{\nu,160}^\mathrm{a}$  & corr.(160) & $\log <L_{\rm
  12--100}>^\mathrm{b}$ & $\log <L_{\rm IR}>^\mathrm{c}$ \\
  & ($M_\odot$) & ($L_\odot$) & ($L_\odot$) & & ($L_\odot$) & & ($L_\odot)$ & ($L_\odot$)}
\startdata
58  & 10.7 & 10.52$^{+0.04}_{-0.05}$ & 10.79$^{+0.10}_{-0.13}$ & 1.51$\pm$0.24 & 11.17$^{+0.16}_{-0.26}$ & 1.04$\pm$0.39 & 11.30$^{+0.10}_{-0.13}$  & 11.42$^{+0.07}_{-0.08}$ \\
160 & 10.5 & 10.09$^{+0.01}_{-0.01}$ & 10.51$^{+0.16}_{-0.24}$ & 1.03$\pm$0.32 & 10.98$^{+0.23}_{-0.55}$ & 0.92$\pm$0.61 & 11.04$^{+0.16}_{-0.27}$  & 11.12$^{+0.05}_{-0.05}$ \\
361 & 10.7 &  9.31$^{+0.03}_{-0.03}$ &  9.90$^{+0.28}_{-0.99}$ & 1.19$\pm$0.96 & $ <$10.27               & ...           & 10.18$^{+0.26}_{-0.75}$  & 10.28$^{+0.14}_{-0.21}$ \\
\enddata
\tablenotetext{a}{The 70\,$\micron$ and 160\,$\micron$ luminosities
  estimated from stacking have included the corrections derived from degraded 24\,$\micron$ images. The corrections are listed in the table.}
\tablenotetext{b}{$L_{\rm 12--100}$ is the luminosity between 12 to
  100\,$\micron$, calculated by linearly interpolating observed 24, 70 and
  160\,$\micron$ luminosities (in logarithm space).}
\tablenotetext{c}{$L_{\rm IR}$ refers to the total IR luminosity between 8 to
  1000\,$\micron$. Local IR SED templates from \citet{Lagache04},\citet{Chary01} and \citet{Dale02} are used to fit the observed data points. The
  total IR luminosity is derived from the dust temperature-match
  templates. See text for details.}
\end{deluxetable*}

\section{Results}\label{results}

To explore the average IR SEDs of $z \sim 0.7$ star-forming galaxies, we first look into the dependence of the IR SED shape on galaxy morphology. Then, we investigate the relationship between the IR SED shape and the 24\,$\micron$ luminosity and the $z \sim 0.7$ IR SEDs to those of present-day star-forming galaxies. Finally, based on the IR SEDs, the extrapolation of the total IR luminosity from the 24\,$\micron$ luminosity is discussed.

\subsection{The relationship between IR SED and morphology}\label{consed}

We used a sample of 1092 galaxies of known morphology to investigate the
dependence of the IR SED shape on morphology.
The 1092 galaxies are classified into three
morphological types: elliptical/lenticular (E/S0), spiral, and
irregular/peculiar. Figure~\ref{mass_UV} shows the relationship between the
rest-frame color $U-V$ and stellar mass. Objects detected at 24\,$\micron$
($f_{24}>83\mu$Jy) are marked with open symbols and
undetected ones with skeletal symbols. The detection limit of 83\,$\mu$Jy
corresponds to the monochromatic observed-frame 24\,$\micron$ luminosity
$\nu L_{\nu,24} = 6\times 10^{10}$\,$L_\odot$ at $z=0.7$.
The 24\,$\micron$-undetected galaxies are intrinsically faint in the IR bands
(see \S\ref{ir:24}), and we excluded them in constructing these
composite SEDs.  To avoid selection bias in color (see \S\ref{samples}), we also excluded about one third of the 24\,$\micron$
detected galaxies with $M_\ast < 10^{10}$\,$M_\odot$, leaving  152
galaxies in the final sample.
We calculated the average luminosities in 14 bands for each
subsample of 24\,$\micron$-detected galaxies: FUV \& NUV
from GALEX, $U,B,V,R$ and $I$ from the COMBO-17 survey,
IRAC 3.6, 4.5, 5.8 and 8.0\,$\micron$, MIPS 24, 70 and
160\,$\micron$ bands.  The average luminosity of each
band includes contributions from
both individually-detected and individually-undetected sources (at
wavelengths other than 24\,$\micron$).
Errors in the average luminosities were derived from bootstrapping, including
contributions to the uncertainty from measurement errors in both the
individually-detected fluxes and the individually-undetected fluxes.
The average SED spans a range in the rest-frame of 0.1 to 100\,$\micron$.

Figure~\ref{sedsir} shows the average SEDs of the three
subsamples.  Each of the three average SEDs is dominated by a
dust-extincted stellar spectrum at $\lambda<$\,5\,$\mu$m
and emission by dust at $\lambda>$\,5\,$\mu$m.
Irregular/peculiar galaxies show a higher ratio of dust to stellar
emission compared to the spirals and E/S0
galaxies. The slope of the IR SED (rest-frame
10-100\,$\micron$) is steeper for irregular/peculiar galaxies,
somewhat intermediate for E/S0 galaxies, and lowest for spirals although the
uncertainties of 70 and 160\,$\micron$ luminosities are large.
The irregular/peculiar galaxies usually form stars in relatively
concentrated regions,  leading to a high star formation intensity (i.e. SFR per
unit area). In contrast, the spirals are often characterized by a
relatively low star formation intensity as the star-forming regions are
widely distributed over disks. The star formation
density for the E/S0 galaxies is somewhat between those of the
irregular/peculiar galaxies and spirals. The shapes of the IR SEDs are thus a
function of star formation intensity in the sense that the
dust temperature is primarily colder in systems of relatively lower star
formation intensity (we will return this topic in \S \ref{discuss}).

\subsection{The relationship between IR SED and 24\,$\micron$ luminosity} \label{ir:24}

We used a mass-limited sample to study the relationship between IR SED shape
and 24\,$\micron$ luminosity. This sample consists of 579 galaxies with
$M^\ast\geq 10^{10}$\,$M_\odot$ and  0.6\,$<z<$\,0.8.
The sample galaxies were divided into three 24\,$\micron$
luminosity bins: $L_{24}$-high, $L_{24}$-medium and $L_{24}$-low.
The first two bins contain 218 individually-detected 24\,$\micron$ sources
($f_{24}>$83\,$\mu$Jy) and all individually-undetected 24\,$\micron$ sources (361 of
the 579) are in the third bin. The $L_{24}$-high bin is chosen to
contain 58 brightest 24\,$\micron$ sources
so that its total 24\,$\micron$ luminosity equals that of the
$L_{24}$-medium bin. Consequently the stacked 70 and 160\,$\micron$ fluxes are expected to have comparable signal-to-noise ratios for the two bins. Average luminosities in all 14 bands were
calculated for the three subsets of galaxies.
The average luminosities in the 24, 70 and 160\,$\micron$ bands are listed in
Table~\ref{table}, along with the number of objects and mean stellar mass for
each of the three subsets. The empirical corrections adopted for the 70 and
160\,$\micron$ stack fluxes (see \S\ref{stackgal}) are also presented.
Errors include the uncertainties in measurements and bootstrapping errors.
Figure~\ref{sedsmass} shows the average SED from the rest-frame wavelengths 0.1
to 100\,$\micron$ as a a function of the 24\,$\micron$ luminosity.
It is clear that the observed 24\,$\micron$ (rest-frame 14\,$\micron$)
luminosity is correlated with the 70 and 160\,$\micron$ IR luminosities for
massive galaxies at $z\sim 0.7$ in the sense that the 70 and 160\,$\micron$
IR luminosities increase as the 24\,$\micron$ luminosity increases; i.e., 24\,$\micron$
luminosity typically reflects high IR luminosity, rather than an enhanced
rest-frame mid-IR excess (see also Bavouzet et al. 2007, in preperation).
The $L_{24}$-high bin has an average galaxy stellar mass 0.2\,dex larger than
the $L_{24}$-medium bin; this can also be seen from the redder
optical colors and higher rest-frame $\sim 3$\,$\micron$ luminosity
of the $L_{24}$-high bin.
The $L_{24}$-low bin contains 361 massive galaxies that are individually
undetected at 24\,$\micron$, including early-type galaxies with little star
formation and late-type galaxies in the quiescent star formation phase (see
Figure~\ref{mass_UV}).  The short-wavelength part of their average SED is dominated by a relatively
old stellar population. The large errorbars of the 70 and 160\,$\micron$
luminosities compared to the small errorbar of the 24\,$\micron$ luminosity
are partially due to the intrinsic scatter among the sample galaxies in this
bin.

\begin{figure}[t] \centering
  \includegraphics[width=0.48\textwidth,clip]{f7.ps}
\caption{Relative dust temperature estimate from the IR flux ratio $f_{60}/f_{100}$ versus $f_{15}/f_{60}$.
  {\it Asterisks} represent local star-forming galaxies
  from \citet{Dale00} with IRAS observations at the 60 and
  100\,$\micron$ and ISO observation at 15\,$\micron$.  The ratio
  $f_{60}/f_{100}$  is an indicator of the dust temperature, which tends
  to be correlated with IR luminosity in the local Universe.
  The {\it dotted lines} show typical values of $f_{60}/f_{100}$
  corresponding the given $\log (L_{\rm IR}/L_\odot)$ derived from
  IRAS data \citep{Soifer91}. These
  lines  are arbitrarily positioned on the Y-axis.
The {\it open  circle} shows the well-known local ultraluminous IR galaxy
  Arp\,220.   The {\it squares} show the three subsets of massive
  ($M^\ast\ge 10^{10}$\,$M_\odot$) galaxies in redshift slice 0.6$<z<$0.8,
  labeled with the corresponding IR luminosities
  $\log (L_{\rm IR}/L_\odot)$. Note that errorbars of the two flux ratios
  are not independent. The arrow indicates the effect of an increase in
  $f_{60}$ by a factor of 1.5.
}\label{colorcolor}
\end{figure}

\begin{figure*}[] \centering
  \includegraphics[width=\textwidth,clip]{f8a.ps}
  \includegraphics[width=\textwidth,clip]{f8b.ps}
\caption{Fitting three sets of local templates from \citet{Lagache04}, \citet{Chary01} and \citet{Dale02} to the average IR SEDs of three subsets of massive
  ($M^\ast\ge 10^{10}$\,$M_\odot$) galaxies in the redshift slice 0.6$<z<$0.8.
  Only the three MIPS data points (24, 70 and 160\,$\micron$) of each observed IR
  SED are used in the fitting.
  {\it Top}: The panels show $\chi^2$ as a function of the
  template's characteristic $\log (f_{60}/f_{100}$).
  For each observed IR SED two best-fit templates are chosen: one is matched
  in luminosity (i.e., with the normalization constant equal to
  unity) and the other is dust temperature match (or SED
  shape match; i.e., with the minimum $\chi^2$).
  {\it Bottom}: The {\it upper panels} shows the best-fit luminosity match templates
and the {\it lower panels} show the best-fit dust temperature match templates,
  compared to the observed SEDs from the rest-frame
  2 to 100\,$\micron$ delineated by IRAC and MIPS data points
  for the $L_{24}$-high bin ({\it left panels}),
  the $L_{24}$-medium bin ({\it middle panels}) and the $L_{24}$-low bin ({\it
  right panels}). The SEDs are normalized to unity at 2.1\,$\micron$.
  Note that the stellar components of the locoal templates are somewhat arbitrarily set. The disagreements between the averaged SEDs and the best-fit templates at $\lambda_{\rm rest} < 5\micron$ should be ignored.
}\label{sedsmodel}
\end{figure*}

\begin{figure*}[] \centering
  \includegraphics[width=0.49\textwidth,clip]{f9a.ps}
  \includegraphics[width=0.49\textwidth,clip]{f9b.ps}
\caption{The ratio between the rest-frame 15\,$\micron$ to the 12--100\,$\micron$
  luminosity  ({\it left} plot) and to the total IR
  luminosity (8--1000\,$\micron$; {\it right} plot) as a function of the IR flux
  ratio $f_{60}/f_{100}$. {\it Asterisks} are local galaxies  collected
  from literature with IRAS, ISO 15\,$\micron$ observations and at least one
  observation at wavelengths longer than 100\,$\micron$. {\it Squares} show
  our results for galaxies with $M^\ast\ge 10^{10}$\,$M_\odot$ and 0.6$<z<$0.8.
  The relations derived from SED templates are presented for
  comparison. One can see that the extrapolation of the rest-frame 15\,$\micron$ luminosity based on the three sets of templates give comparable estimates of total IR luminosity within a considerable scatter.
Our data points distribute within the scatter of the
local star-forming galaxies but towards the colder templates.
Roughly speaking, a factor of 10 is a quite good average bolometric correction to the estimate of the total IR luminosity from the rest-frame 15\,$\micron$ luminosity.
Generally speaking, the use of local templates of  $L_{\rm IR} \leq 10^{10.5}$\,$L_\odot$ for extrapolation of total IR luminosity from intermediate redshift 24\,$\micron$ luminosities gives a more accurate result than the use of  $L_{\rm IR} \sim 10^{11}$\,$L_\odot$ templates.
}\label{colorlum}
\end{figure*}

\subsection{Comparison with local SEDs}

A key goal of this paper is to compare the observed average IR SED shapes at
$z \sim 0.7$ to local `template' SEDs.  We adopted a sample of
local star-forming galaxies from \citet{Dale00} with $IRAS$ and ISO 15\,$\micron$ observations and total IR
luminosity (8 - 1000\,$\micron$) spanning from $\sim 10^9$ to
$10^{12}$\,$L_\odot$.
Figure~\ref{colorcolor} shows these nearby galaxies in the IR flux ratio
$f_{60}/f_{100}$ versus $f_{15}/f_{60}$ plot.
The local galaxies distribute along a sequence with considerable scatter.
The sequence is correlated with both dust temperature and IR
luminosity.  The dust temperature and IR luminosity increase for increasing
$f_{60}/f_{100}$ \citep[e.g.,][]{Soifer91}.
The average IR SEDs for $z\sim 0.7$ galaxies are determined by the three MIPS
bands, corresponding to the rest-frame $\sim$14, $\sim$41 and
$\sim$94\,$\micron$ bands. The 24\,$\micron$ and 160\,$\micron$
luminosities can be taken as rest-frame 15\,$\micron$ and 100\,$\micron$
luminosities, for which `$K$-corrections' are negligible.
We estimated the rest-frame 60\,$\micron$ luminosity by linear interpolation
between the MIPS measurements in $\log$-$\log$ space (as shown in Figure~\ref{sedsmass}). We used the local sample to test the linear interpolation. First, we derive the 41\,$\micron$ fluxes by linear
interpolation between $IRAS$ 25 and 60\,$\micron$ measurements in $\log$-$\log$ space.
Second, we derive 60\,$\micron$ fluxes by the same method between 41 and
100\,$\micron$ for the 59 local galaxies adopted.
The estimated 60\,$\micron$ fluxes are 25$\pm$11\% lower than
the observed $IRAS $ 60\,$\micron$ fluxes. This hints that our
rest-frame 60\,$\micron$ luminosities of the  $z\sim 0.7$ galaxies might be
underestimated.
We compared the $z\sim 0.7$ galaxies to the local galaxies.
As shown in  Figure~\ref{colorcolor}, the two populations are roughly
located in the same region of the $f_{60}/f_{100}$ versus $f_{15}/f_{60}$ plane.
Specifically, the $z\sim 0.7$ star-forming
galaxies of IR luminosity $11< \log (L_{\rm IR}/L_\odot) < 11.4$ (those
in the $L_{24}$-high bin and $L_{24}$-medium bin;  the estimates of the total
IR luminosities will be discussed later) populate the relatively
low-temperature
end of the template sequence.  In the local Universe, these low-temperature
galaxies tend to be of relatively low luminosity
$\log (L_{\rm IR}/L_\odot) < 10.5$.
This, with significant uncertainties, suggests
that the typical dust temperature of $z \sim 0.7$
luminous IR galaxies (LIRGs; i.e., galaxies with $\log (L_{\rm IR}/L_\odot) > 11$)
is lower than that of local galaxies of comparable IR luminosity.
The IR flux ratios are poorly determined for the $L_{24}$-low bin because of
the large uncertainties in the average 70 and 160\,$\micron$ IR luminosities,
which are partially due to the intrinsic scatter among the sample galaxies in
the $L_{24}$-low bin (including early-type galaxies and late-type galaxies in
the quiescent star formation phase).

We compared our average SEDs with the IR SED model templates
from \citet{Lagache04}, \citet{Chary01} and \citet{Dale02}.
The three sets of templates were empirically calibrated to represent
local star-forming galaxies spanning a wide range in the IR flux
ratio $f_{60}/f_{100}$.
Dale \& Helou's IR SED templates are characterized by the flux ratio
$f_{60}/f_{100}$. We use the equation from \citet{Chapman03} to parameterize the IR
luminosity (8 - 1000\,$\micron$) as a function of $f_{60}/f_{100}$.\footnote{The total IR luminosity defined in \citet{Chapman03} is between 3 and 1100\,$\micron$ , in good
  agreement with the adopted one (\citealt{Dale01}; see also
  \citealt{Takeuchi05b}).}
The templates from \citet{Lagache04} and \citet{Chary01} were characterized by the IR luminosity.
We extended each set of SED templates by linear interpolation in logarithmic
space to a grid of SEDs with the
characteristic IR luminosity ranging from 10$^9$ to 10$^{13}$\,$L_\odot$ with a
resolution of 0.1\,dex.
The observed IR SED of each subset was compared to the three sets of templates:
\begin{equation}
\chi^2 = \sum_{i=1}^{N_{\rm filters}} \left[{L_{{\rm obs},i}
- f_{scale} \times L_{{\rm temp},i}(z=0.7)\over \sigma_i} \right]^2 \:,
\end{equation}
where $L_{{\rm obs},i}$, $L_{{\rm temp},i}$ and $\sigma_i$ are the observed
and template luminosities and their uncertainty in filter i, respectively, and
$f_{\rm scale}$ is a normalization constant. Here only 24, 70 and 160\,$\micron$ bands
were used to fit templates, i.e., $N_{\rm filters}=3$.
$L_{\rm temp}$ is calculated by convolving the redshifted SED template to
$z=0.7$ with the 24, 70 or 160\,$\micron$ filter transmission function.
$f_{\rm scale}$ is chosen to minimize the $\chi^2$ for each
template. The top plot in Figure~\ref{sedsmodel} shows the fitting results.
The templates with a normalization constant $f_{\rm scale}$ of unity are chosen as the
best-fit luminosity match templates and those with a minimum $\chi^2$ as the
best-fit dust temperature match (or SED shape match) templates.
The best-fit templates are compared to the observed SEDs in
the bottom plot of Figure~\ref{sedsmodel}.

As shown in Figure~\ref{sedsmodel} the local SED templates track the observed
SEDs reasonably well, in particular for the $L_{24}$-medium
bin, which contains the majority of intense star-forming galaxies.
As the total IR luminosity is dominated by emission in the
rest-frame wavelength range between 10 and 100\,$\micron$, the estimates of
the total IR luminosity with templates from different models give similar
results.
In contrast, $\chi^2$ is a measure of the shape agreement between a
template and an observed SED.
It is worth noting that the estimate of total IR luminosity based on the three
measurements at 24, 70 and 160\,$\micron$ is not sensitive
to the shapes of the IR SED templates. Both the best-fit luminosity match and the
best-fit dust temperature match the SED templates and suggest a total IR (8 -
1000\,$\micron$) luminosity of $\log L_{\rm  IR}/L_\odot\sim$\,11.4, 11.1,
10.3 for the three average SEDs, respectively.
Generally speaking, the shape (or dust
temperature) of the average IR SEDs of star-forming galaxies at $z\sim 0.7$
(i.e., the $L_{24}$-high bin and $L_{24}$-medium bin) are better fitted by
the local SED templates of characteristic IR luminosity $L_{\rm IR} \leq
10^{11}$\,$L_{\odot}$ than those of $L_{\rm IR} > 10^{11}$\,$L_{\odot}$. This
holds for all three sets of templates.
It confirms that the typical star-forming galaxies at $z\sim 0.7$ are likely
to have relatively colder dust emission than local galaxies with comparable IR
luminosity.

\subsection{The extrapolation from the rest-frame 15\,$\micron$ to the total IR luminosity}\label{24toir}

Local IR SED templates are often used to estimate the total IR
luminosities from single mid-IR band luminosities for distant star-forming
galaxies. With the averaged IR SEDs determined in the three MIPS bands, we
are able to better constrain the estimates of the IR luminosities.
We compared the estimates with those transformed from single 15\,$\micron$
luminosities using local SED templates.

By linearly integrating between the average 24, 70 and 160\,$\micron$
luminosities (in logarithm space) for the star-forming galaxies at $z\sim$\,0.7, we
estimated the rest-frame 12--100\,$\micron$ luminosities.
Uncertainties are calculated from the combination of the uncertainties in the three
bands. The total IR (the rest-frame 8--1000\,$\micron$) luminosity is derived
from the three dust temperature-match templates to each observed IR SED (the 24, 70
and 160\,$\micron$). We combine the scatter between the templates and the
uncertainty in determining the dust temperature-match templates, in which the
uncertainties of the 24, 70 and 160\,$\micron$ luminosities are counted,
as the uncertainty for the total IR luminosity. The results are listed in Table~\ref{table}.
For comparison, we use the NASA/IPAC Extragalactic database (NED) to collect a
sample of 29 local star-forming galaxies ($z<0.1$) with observations
at 12, 25, 60 and 100\,$\micron$ by $IRAS$, at 15\,$\micron$ by ISO and at least
in one band longer than 100\,$\micron$.  The 12--100\,$\micron$ luminosity and
the total IR luminosity are calculated as above.
When observations do not reach 1000\,$\micron$, a modest ($\la 20$\% of the IR luminosity)
extrapolation is employed using the local SED templates.

Figure~\ref{colorlum} shows the extrapolations (bolometric corrections) from the rest-frame 15\,$\micron$
to the 12--100\,$\micron$ luminosity (left plot) and to the total IR luminosity (right
plot) as functions of the IR flux ratio $f_{60}/f_{100}$.
The local star-forming galaxies exhibit a significant scatter
(\citealt{Chary01}; see also \citealt{Dale05}); such scatter is typically adopted
as the systematic uncertainty of template-based estimates
of total IR luminosity from rest-frame mid-IR luminosity.
The $z\sim 0.7$ star-forming galaxies distribute within the scatter of the
local star-forming galaxies.
Again, there is a tendency for the $z \sim 0.7$ galaxies to cluster
towards the colder templates, suggesting that use of spiral
galaxy templates for extrapolation of total IR luminosity from
intermediate redshift 24\,$\micron$ fluxes gives a more accurate result
than the use of starburst/LIRG templates.

\begin{figure}[] \centering
  \includegraphics[width=0.48\textwidth,clip]{f10.ps}
\caption{The ratio IR/UV flux (i.e., the fractional dust obscuration) as a function of SFR.
  {\it Solid circles} show the three subsets of galaxies with $M_\ast \ge
  10^{10}$\,$M_\odot$ and $0.6<z<0.8$, sorted by the 24\,$\micron$ luminosity.
  Other symbols show three morphology subsets of star-forming
  (f$_{24}>83\mu$Jy) galaxies in redshift slice $0.65\leq z\leq 0.75$.
  The {\it dashed line} shows the local relation
  \citep{Martin05b}.
}\label{sfrextinction}
\end{figure}

\subsection{Dust extinction}\label{sec:extinc}

A universal relation between SFR and dust extinction is suggested by several
studies at $z < 1$
(\citealt{Hopkins01,Adelberger00,Bell03,Takeuchi05a,Zheng06,Buat06}; although
see \citealt{Reddy06} for a dissenting view at $z \sim 2$).
With a measured estimate of the total IR luminosity from the 24, 70 and
160\,$\micron$ luminosities, we explore the
relationship between SFR and dust extinction at $z\sim0.7$, comparing it to
that at $z\sim 0$. The dust extinction is described by the IR to UV ratio
$L_{\rm IR}/L_{\rm UV}$.  The UV luminosity, i.e., the integrated luminosity
between rest-frame 1500-2800\,\AA, is estimated from linear
interpolation of the $FUV$, $NUV$, $U$, and $B$ band luminosities for
$z\sim 0.7$ galaxy subsets shown in Figures~\ref{sedsir} and~\ref{sedsmass}.
Following \citet{Bell05}, we derived the SFR from the IR and UV
luminosities with the formula
\begin{equation}
{\rm SFR}/(M_\odot\,{\rm yr}^{-1}) = 9.8\times10^{-11}(L_{\rm IR}+2.2L_{\rm UV}),
\end{equation}
assuming a stellar population with a constant SFR for 100\,Myr and a Kroupa initial mass function.
Figure~\ref{sfrextinction} shows the relationship between dust
obscuration and SFR for $z\sim 0.7$ galaxies, compared to the  local
relation derived from IRAS and GALEX data with a scatter at $\sim$0.5-1\,dex
level \citep{Martin05b,Xu06,Buat06}.
As shown in Figure~\ref{sfrextinction}, the $z\sim 0.7$ galaxies
distribute perfectly along the local relation over one order of magnitude.

\section{Discussion and conclusion} \label{discuss}

We selected a sample of 152 galaxies in the redshift slice $0.65\leq z \leq
0.75$ of known morphology from HST imaging and another sample of 579
mass-limited ($M_\ast\ge 10^{10}$\,$M_\odot$) galaxies in the redshift
slice $0.6<z<0.8$ to study the IR SEDs at that redshift.
We divided our sample galaxies into different mass-limited morphology and
24\,$\micron$
luminosity bins. For each bin, we determined the average luminosities in 14
bands from the FUV to the FIR by summing the individual detections
and adding in the stacked flux from non-detections.   Careful efforts
were taken in stacking the noise and confusion limited 70 and 160\,$\micron$
images. Empirical corrections, determined from the 24\,$\micron$ image,
were introduced to account for the clustering effects on the stack results.

\begin{figure}[] \centering
  \includegraphics[width=0.48\textwidth,clip]{f11.ps}
\caption{Same as Fig.~\ref{colorcolor}.
The {\it filled  circles} show the two subsets of galaxies in the redshift
slice $0.6<z<0.8$ split by 24\,$\micron$ luminosity surface density. The inner
panel shows the average SEDs of the two subsets. The $\Sigma_{\rm L24}$-high
bin and the $\Sigma_{\rm L24}$-low bin data points are located separated along
the dust-temperature sequence of local star-forming galaxies, indicating that
the $\Sigma_{\rm L24}$-high bin is characterized by a hotter dust emission
than the $\Sigma_{\rm L24}$-low bin. This is clearly seen from the difference
between their IR SEDs.
}\label{sfrd}
\end{figure}

The average luminosities in three MIPS bands determine the IR SED from the
rest-frame 10 to 100\,$\micron$.
Our principal result is that the average IR SED shape of
$z \sim 0.7$ intensely star-forming
galaxies (with IR luminosities $\sim 10^{11} L_{\sun}$)
is similar to reasonably `cool' local templates (i.e., templates
of `normal' spiral galaxies).  The dust SED seems
to depend on morphological type for star-forming
galaxies at $z \sim 0.7$.
This has the immediate and important
implication that the use of local templates to extrapolate
total IR luminosity from observed-frame 24\,$\micron$
data is a well-posed problem, at least, on average.
Interestingly, galaxies with
`cool' dust temperatures in the local Universe all tend to have
IR luminosities $\la 10^{10.5} L_{\sun}$, i.e., distant intensely
star-forming galaxies tend to be characterized by colder dust
emission than their local counterparts of comparable IR luminosity.

Previous studies have found evidence for a somewhat cooler
dust SED at $0.2<z<2.5$ than for local galaxies of a comparable luminosity
\citep[e.g.,][]{Pope06,Sajina06} at $0.2<z<2.5$.
These studies were selected in rest-frame $>100$\,$\micron$
emission, and the authors suspected that their overall tendency
towards `colder' IR SEDs was in part due to that long-wavelength
selection.  Our sample is selected on rest-frame $\sim 15$\,$\micron$
emission, i.e., by warm dust; yet, we find a `colder' average
SED at a given luminosity than is found locally.  This tends
to support the interpretation that the offset which we and others
have found towards colder temperatures at a given luminosity are
at least in part a real difference.  We suggest that this tendency toward
colder dust temperature reflects a difference in dust and star
formation geometry: whereas local LIRGs tend to be interacting
systems with relatively compact very intense star formation and of comparable
masses \citep[e.g.,][]{Wang06},
$0.5\la z \la 1$ LIRGs tend to be disk-dominated, relatively
undisturbed galaxies \citep{Zheng04,Bell05,Melbourne05}.
We suggest that these disk galaxies host widespread intense star
formation, like star formation in local spirals but scaled up,
leading to relatively cold dust temperatures. This is consistent with what
\citet{Chanial06} found --- the scatter in the $L_{\rm
  IR} - T_{\rm dust}$ relation for star-forming galaxies is largely induced by
the size dispersion of the star-forming regions; and the IR luminosity surface
density $\Sigma_{\rm IR} - T_{\rm dust}$ relation is more fundamental than the
the $L_{\rm IR} - T_{\rm dust}$ relation.  We examined the IR flux ratio
$f_{60}/f_{100}$ (indicator of dust temperature) as a function of the IR
luminosity surface density for $z\sim 0.7$ star-forming galaxies. By taking
galaxy size (i.e. half-light radius) derived from HST imaging \citep{Haussler07} as a proxy of the size of star-forming regions
in a galaxy and 24\,$\micron$ luminosity as a proxy of total IR
luminosity, we split a sample of 311 24\,$\micron$-detected galaxies in the
redshift slice $0.6<z<0.8$ into two bins in the 24-$\micron$ luminosity
surface density $\Sigma_{\rm L24}$. The average SEDs for the two subsets of
galaxies were constructed in the same way as described in \S\ref{consed} and
shown in the inner panel of Figure~\ref{sfrd}. The total IR luminosity is
estimated as $\log (L_{\rm IR}/L_\odot) = 11.3$ and 11.0 for the
$\Sigma_{\rm L24}$-high bin and the $\Sigma_{\rm L24}$-low bin respectively.
As shown in Figure~\ref{sfrd}, the two subpopulations are
distributed along the dust-temperature sequence of local
star-forming galaxies. Galaxies in the $\Sigma_{\rm L24}$-high bin
on average show hotter dust emission than those in the $\Sigma_{\rm L24}$-low bin,
suggesting that the IR luminosity surface density plays an essential role in
shaping IR SED \citep{Chanial06}.

Finally, with UV luminosities derived
from GALEX data and IR luminosities derived from
three band MIPS data, we determined a quite precise relationship
between the dust obscuration and SFR for $z\sim$0.7.
Our results show an excellent agreement between the SFR-dust obscuration
relation at $z\sim$0.7 with that at the present day, indicating that no significant
evolution occurs since that redshift. Our measurements give the
mean values of SFR and dust obscuration. The actual scatter in dust
obscuration  can spread by 1 - 2\,dex for individual galaxies of given
SFR\citep{Martin05b,Xu06,Buat06}.
\citet{Reddy06} claimed that the dust obscuration for
star-forming galaxies is systematically smaller at $z \sim 2$ than the present
day. Their sample is dominated by rest-frame UV-selected galaxies.
This may lead to a potential selection bias against objects with
high IR/UV ratio (see \citealt{Buat06} for the comparison
between UV and FIR selected samples of local galaxies and a similar
discussion).
Therefore studies based on unbiased samples of high-redshift galaxies will
help to answer the question whether the SFR-dust obscuration relation still
holds at $z>1$.
This will add important constraints to our understanding of galaxy evolution
involving star formation and metallicity enrichment \citep{Zheng06}.

\begin{acknowledgements}

  We are grateful to Daniel A. Dale and Kirsten K. Knudsen for helpful
  discussions. E.\ F.\ B.\ was supported by
  the Emmy Noether Programme of the Deutsche Forschungsgemeinschaft.
  Support for E.\ L.\ F.'s work was provided by NASA through the Spitzer Space
  Telescope Fellowship Program. This work was in part supported by
contract 1255094 from JPL/Caltech to the University of Arizona.
  This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
\end{acknowledgements}

\input{zheng_refes.tex}

\end{document}

redpajama_set_name: RedPajamaArXiv


text:
Curitiba Zero Grau is a 2010 Brazilian drama film directed by Eloi Pires Ferreira and starring Jackson Antunes.

Plot
In the cold city of Curitiba, four men spend their days in transit: one is a bus driver, the other is a catador (Paper Collector), the third works as an automobile merchant and the last one is a Motorcycle courier. The fate of these anonymous intersects in the heart of the city.

Cast
Jackson Antunes
Katia Drummond
Rodrigo Ferrarini
Camila Hubner
Diego Kozievitch
Enéas Lour
Stephanie Mattanó
Olga Nenevê

References

External links

Brazilian drama films
Films shot in Curitiba
2010 drama films
2010 films
2010s Portuguese-language films

redpajama_set_name: RedPajamaWikipedia


text:
/* TEMPLATE GENERATED TESTCASE FILE
Filename: CWE134_Uncontrolled_Format_String__char_console_vfprintf_84_goodG2B.cpp
Label Definition File: CWE134_Uncontrolled_Format_String.vasinks.label.xml
Template File: sources-vasinks-84_goodG2B.tmpl.cpp
*/
/*
 * @description
 * CWE: 134 Uncontrolled Format String
 * BadSource: console Read input from the console
 * GoodSource: Copy a fixed string into data
 * Sinks: vfprintf
 *    GoodSink: vfprintf with a format string
 *    BadSink : vfprintf without a format string
 * Flow Variant: 84 Data flow: data passed to class constructor and destructor by declaring the class object on the heap and deleting it after use
 *
 * */
#ifndef OMITGOOD

#include <stdarg.h>
#include "std_testcase.h"
#include "CWE134_Uncontrolled_Format_String__char_console_vfprintf_84.h"

namespace CWE134_Uncontrolled_Format_String__char_console_vfprintf_84
{
CWE134_Uncontrolled_Format_String__char_console_vfprintf_84_goodG2B::CWE134_Uncontrolled_Format_String__char_console_vfprintf_84_goodG2B(char * dataCopy)
{
    data = dataCopy;
    /* FIX: Use a fixed string that does not contain a format specifier */
    strcpy(data, "fixedstringtest");
}

static void goodG2BVaSink(char * data, ...)
{
    {
        va_list args;
        va_start(args, data);
        /* POTENTIAL FLAW: Do not specify the format allowing a possible format string vulnerability */
        vfprintf(stdout, data, args);
        va_end(args);
    }
}

CWE134_Uncontrolled_Format_String__char_console_vfprintf_84_goodG2B::~CWE134_Uncontrolled_Format_String__char_console_vfprintf_84_goodG2B()
{
    goodG2BVaSink(data, data);
}
}
#endif /* OMITGOOD */

redpajama_set_name: RedPajamaGithub


text:
Are You a Legal Professional? Join Our Network | Attorney Login
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Archive for category: Indiana
You are here: Home / Articles / Video Library / Videos by State / Indiana
Are Trucking Regulations Keeping You Safe?
Attorney Video Blog, Car and Truck Accidents, Indiana, Practice Areas, Video Library, Videos by State
More trucks are on the road now than ever in an economic boom and trucking regulations are being relaxed. Will this cause more accidents involving innocent passenger cars? The truth is that it likely will, and therefore, it is even more important to know how to protect yourself if a truck accident happens. Indiana truck […]
December 6, 2018 /On Behalf of AskTheLawyers™
https://cdn.askthelawyers.com/wp-content/uploads/2018/12/06165927/are-trucking-regulations-keeping.jpg 720 1280 Andrew https://www.askthelawyers.com/wp-content/uploads/2018/05/Asset-2-300x40.png Andrew2018-12-06 16:59:242018-12-06 17:03:28Are Trucking Regulations Keeping You Safe?
Betsy K. Greene – Personal Injury Attorney
Attorney Video Blog, Indiana, Personal Injury, Practice Areas, Video Library, Videos by State
"I am so grateful for the ability to help people." With nearly 40 years of legal experience, personal injury attorney Betsy Greene is passionate about helping people who are hurt. Personal injury cases come in all forms. She has tried over 100 jury trials throughout her career. "I am probably one of the most experienced […]
December 6, 2018 /On Behalf of Betsy Greene
https://cdn.askthelawyers.com/wp-content/uploads/2018/12/20110646/betsy-k-greene-personal-injury-a-1.jpg 720 1280 Betsy Greene https://www.askthelawyers.com/wp-content/uploads/2018/05/Asset-2-300x40.png Betsy Greene2018-12-06 15:50:272018-12-20 11:06:43Betsy K. Greene - Personal Injury Attorney
What Is Hidden in Electric Scooter Rental Agreements?
Attorney Video Blog, Car and Truck Accidents, Consumer Protection, Indiana, Practice Areas, Probate Litigation, Product Liability, Recent News Videos, Video Library, Videos by State
Liability in E-Scooter Accidents The rise of electric scooters, or e-scooters, leaves riders with many questions. Who is liable in an accident? What happens if someone gets hurt? Can riders file a lawsuit? Betsy Greene is an injury attorney in Bloomington, Indiana. As she explains in this video, the agreements for scooter rentals like Bird and […]
November 14, 2018 /On Behalf of Betsy Greene
https://cdn.askthelawyers.com/wp-content/uploads/2018/11/14141239/what-is-hidden-in-electric-scoot.jpg 720 1280 Betsy Greene https://www.askthelawyers.com/wp-content/uploads/2018/05/Asset-2-300x40.png Betsy Greene2018-11-14 14:11:102018-11-27 17:43:32What Is Hidden in Electric Scooter Rental Agreements?
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redpajama_set_name: RedPajamaCommonCrawl


What is the function of "az" in this sentence?
Does it change the meaning when it is after "ezek"?
It's just that you have to say "the buildings", something you wouldn't do in English, that's all. It has no special importance.
ahhh. So to say "these are buildings" you would say "ezek épületek" but to say "these buildings" you would say "ezek az épületek"?
These are buildings _ Ezek épületek. These are the buildings _ Ezek az épületek.


Joshua Zucker-Pluda is an artist who lives and works in New York City. He is the host and curator of the Roadside Picnic podcast and its accompanying record series, A Room Forever.
Nine Eglantine Yamamoto-Masson is a French-Japanese artist, scholar, and curator based in Berlin and at home in many places. She likes cats and strange music.
Jacob Kirkegaard is a Berlin-based Danish artist who focuses on the scientific and aesthetic aspects of resonance, time, sound, and hearing. His installations, compositions, and performances deal with acoustic spaces and phenomena that usually remain imperceptible. He has presented his works at exhibitions and festivals around the world and has released five albums (mostly on the British label Touch). He is also a member of the sound-art collective freq_out.


text:
Wed, 3, Jul, 2019 - 5:00:AM
Five Picks from This Year's New Zealand International Film Festival
It's coming up - that time of year when our evening hours have the potential to be a little bit sparklier (and full of screen-friendly snack consumption). The New Zealand International Film Festival brings a curated selection of some of the world's best and diverse cinematic works to our doorsteps, making planning date nights, catch-ups, and mindful me-time sessions that much more exciting. From movies brimming with hope about ways we can change the world, to guaranteed crowd-pleasers with plot twists and tight scripts, to nuanced slower-paced portrayals of small-town life… there's a perfect film for everyone in the 2019 schedule. Here are five movies topping my to-watch list this year.
The Inspirational: 2040
A climate emergency has been declared by numerous city councils across Aotearoa, but what are we going to do in the face of crisis? In this uplifting film, director Damon Gameau traverses the world looking for technologies and practices that might help us make our 2040 look better than the one in current projections. Regenerative farming practices, solar systems, marine permaculture experiments, and electric transportation development… as Gameau learns about each promising practice, he pieces together a vision of a better future and the practical steps that we can take to get there: "Not only are there so many people who want to take part in telling a new story… we have everything right now to make it happen."
The Documentary: The Biggest Little Farm
This story is about how Emmy Award-winning filmmaker John and Molly Chester ditched their Los Angeles apartment lifestyles to take the venture of transforming a historically unproductive 80 hectare orchard in the foothills of California's Ventura County. Their journey of regenerating the land using traditional farming methods against all odds (including a cast of "evil rogues" including "murderous coyotes, gobbling gophers, and a squadron of fruit-filching starlings") will likely leave you yearning for the joys of a simpler life (or at the very least prompt you to dabble again with the nitrogen to carbon ratio in your home compost). A reminder that there is beauty in the act of restoration, and a captivating portrait of one couple's efforts to bring eco-philosophy into the world, The Biggest Little Farm is a documentary that brings a lot of heart to the screen. Also ducks. And a very good pig called Emma.
The Rom-com: La Belle Époque
This big night draw promises to be one of the unicorns of the rom-com world - the rare gem of a film that is both crowd-pleaser and cinematic treat. A contemporary twist to the second chance at first love storyline, La Belle Époque takes you into a world where an agency can provide you the opportunity to play the protagonist in any moment in history with cast, costume, and set all provided. The point in history selected by an unhappy cartoonist (played by Daniel Auteuil) currently fighting with his wife? The happiest day of his life, several decades ago, when a beautiful mademoiselle entered a cafe...
Films for Equity: This Changes Everything
Another somewhat sneaky (but necessary!) addition to the list considering This Changes Everything is also a documentary. But Tom Donahue's film chronicling the systemic biases undermining women in Hollywood is a must-see for those passionate about gender parity. A brilliant example of ways that men can take a stand with women to fight discrimination, this frank film goes beyond the hashtags of #MeToo and #TimesUp in capturing and amplifying the voices of many Hollywood veterans (including Meryl Streep, Yara Shahidi, Cate Blanchett, Natalie Portman, Tiffany Haddish, Sandra Oh, and Tracee Ellis Ross) to deconstruct Hollywood's problematic ways.
The Home-grown: Bellbird
Having read Doug Dillaman's recent essay on why so few NZ films make it to Cannes Film Festival, I paid more attention than usual to the home-grown films in this year's NZIFF schedule. "It takes a long-term commitment to selling an artist and giving them the space to develop their voice", wrote Dillaman, "... to let them be distinctive across several stand-alone shorts and feature films". If following the works and development of our local talents is your thing, then writer-director Hamish Bennett's poignant yet subtly playful feature Bellbird stands out as one to watch. Bennett's short prequel to Bellbird (Ross & Beth) won prizes in the NZIFF 2014 New Zealand's Best short film competition, and now this feature masterfully illustrates the relationship between third generation dairy farmer Ross (Marshall Napier) and his squeamish son Bruce (Cohen Holloway) as they grapple with the loss of their wife/mother, and the ways their community looks out for them. Rachel House also plays the part of Connie (Bruce's friend and boss) and I absolutely can't wait to watch her in a feature as promising as this. For the record, Rachel House is a national gem I'd watch performing on repeat even in a soap commercial.
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text:
Dennisiomyces är ett släkte av svampar. Dennisiomyces ingår i familjen Tricholomataceae, ordningen Agaricales, klassen Agaricomycetes, divisionen basidiesvampar och riket svampar.

Källor

Basidiesvampar
Dennisiomyces


There is 1 holiday to Lake Garda for 2 adults, 0 child and 0 infant.
Lake Garda inclusive package deals: Our superb quality Lake Garda all inclusive holidays let you relax safe in the knowledge that all your meals and drinks are included. Many inclusive holidays in Lake Garda also include wine with your evening meal and all include your breakfast, lunch and dinner as part of the package.If you want to pay once for your holiday and then relax and forget about the money, there's no better way than by booking an all included Lake Garda package deal in 2019 and 2020. Enter your dates and airport below or call us now for the best deals.


March is spent preparing the vegetable plot and sowing seeds. The first dandelions are left for the bees to get their first feed. Just before they turn to seed we let the sheep make the first cut. Patch grazing at the right time saves petrol and gets rid of the weeds as well as gives the sheep a boost. They love the young leaves. We too add them to the salad bowl. They clean the blood and are very nutritious. Permaculture principles apply.


text:
Home Business Busiest Day at the Airport for Air Travel Since March
BBusiness
Busiest Day at the Airport for Air Travel Since March
On Thursday, the Transportation Security Administration reported its busiest day since March. As the demand dropped in mid-March due to the coronavirus pandemic.
However, it was the busiest day as many people travel on the long holiday weekend. That traditionally marks the end of the summer travel season.
Number of Passengers Increase
TSA screened 877,698 travelers on Thursday, the highest number since March 17. And the agency predicts that there will be more people traveling on Friday with the number of travelers most probably reaching 900,000.
"For travelers who have not flown since the beginning of the pandemic. The TSA checkpoint experience will be noticeably different as compared to Labor Day last year," TSA Administrator David Pekoske said in a press release.
"Passengers also play an important role in helping us ensure they safely and efficiently get through security screening at our airports while wearing masks and respecting social distancing."
However, U.S. airlines were expecting an increase in passenger numbers over Labor Day as it was on Memorial Day. And on the 4th of July, the number of passengers increased. But it was still down by 50% as compared to the same day last year.
Hot spot For Travelers
Most of the travel bookings of Labor Day have been for Florida, according to travel itinerary app TripIt.
"Florida is this year's hot spot," TripIt said in its findings. It also stated that bookings to the state have jumped 200% compared with Labor Day weekend last year.
However, experts are worried that an increase in travel to states where coronavirus cases are still active might cause a surge in COVID-19 cases.
"It's very predictable," ABC News contributor Dr. John Brownstein, chief innovation officer for the Boston Children's Hospital and a professor of epidemiology at Harvard Medical School, said.
"Major holidays, where people are moving — increases in mobility lead to transmission. And you see cases start to surge two weeks later."
The demand for air travel was gradually recovering. But when the number of coronavirus cases increased in the US especially in the Sun Belt, starting around late June, there was again a decline in air travel.
However, the number of passengers at the airport is 86% lower than last year. Therefore, airlines have reduced the number of flights they operate.
According to OAG, a global travel data provider has found that the US airlines have cut nearly 1,700 routes between January and May.
Japan Reopen Borders for Business Travelers from Abroad
The Japanese government is easing entry restrictions on business travelers for stays of up to three days. This…
Jet2 Not Refunding Scottish Travelers Flying To Portugal
Jet2 said it will not refund Scottish travelers scheduled to fly to Portugal. However, this decision comes after…
EasyJet Announces New Belfast to Tenerife Route
A new route from Belfast to Tenerife was announced by EasyJet. However, the flights to this route will…
American Airlines to Use Disinfecting Spray on its Planes
American Airlines will be using an antimicrobial disinfecting spray on its planes to kill the novel coronavirus. It…
Airlines Waiver Policies Due to Hurricane Laura
Major US airlines such as United, Southwest, Delta, American, and Spirit are issuing fee waivers due to Hurricane…
Heathrow to Layoff More Employees
Heathrow Airport will be losing 25,000 jobs if the government does not ease the quarantine rules or establish…


text:
Gfinity's Athlos Game Technologies (Athlos) will integrate its tournament platform into games developed by mobile game publisher Plagendary, which will see the mobile games offered by the company integrated.
The games have yet to be announced but the expectations are that the integration will happen in the near future, and as part of the deal Gfinity will be paid a monthly fee based on the number of active users that use the platform across Playgendary's games.
"We are delighted to join forces with the Playgendary team" Gfinity Chairman, Neville Upton said in an official statement. "Most gaming spend is now in-game and competitive players spend significantly more than casual gamers. This means our market-leading technology can deliver a significant and direct cash benefit to publishers".
In this article:Athlos Game Technologies, Gfinity, Playgendary


text:
Q: Why there is no way to derive Applicative Functors in Haskell? In Haskell, you can derive Functor, Foldable and Traversable automatically using deriving. There is no way to derive Applicative, though. Considering there is one obvious way to define an Applicative instance (which would amount to a zipped application), isn't there any way to enable deriving Applicative?

A: Now that DerivingVia has been released (GHC-8.6 or newer) it is actually possible to derive Applicative with the help of DeriveGeneric for any deterministic data type! That is to say, any data type with exactly one variant:
data Foo x = Foo x | Fe  -- This is non-deterministic and can't derive Applicative
data Bar x = Bar x x (Bar x) -- This is deterministic and can derive Applicative
data Baz x = Baz (Either Int x) [x] -- This is also ok, since [] and Either Int
                                    -- are both Applicative
data Void x -- This is not ok, since pure would be impossible to define.

To derive Applicative, we first need to define a wrapper for deriving via generics:
{-# LANGUAGE DerivingVia #-}
{-# LANGUAGE DeriveFunctor #-}
{-# LANGUAGE UndecidableInstances #-}
{-# LANGUAGE DeriveGeneric #-}
module Generically1 where

import GHC.Generics

newtype Generically1 f x = Generically1 { generically1 :: f x }

fromg1 :: Generic1 f => Generically1 f a -> Rep1 f a
fromg1 = from1 . generically1

tog1 :: Generic1 f => Rep1 f x -> Generically1 f x
tog1 = Generically1 . to1

instance (Functor f, Generic1 f, Functor (Rep1 f))
       => Functor (Generically1 f) where
  fmap f (Generically1 x) = Generically1 $ fmap f x

instance (Functor f, Generic1 f, Applicative (Rep1 f))
       => Applicative (Generically1 f) where
  pure = tog1 . pure
  f <*> x = tog1 $ fromg1 f <*> fromg1 x

instance (Functor f, Generic1 f, Monad (Rep1 f)) => Monad (Generically1 f) where
  return = pure
  m >>= f = tog1 $ fromg1 m >>= fromg1 . f

and to use it we first derive Generic1 for our data type and then derive Applicative via our new Generically1 wrapper:
data Foo x = Foo x (Int -> x) (Foo x)
  deriving (Functor, Generic1)
  deriving (Applicative, Monad) via Generically1 Foo

data Bar x = Bar x (IO x)
  deriving (Functor, Generic1)
  deriving (Applicative, Monad) via Generically1 Bar

data Baz f x = Baz (f x) (f x)
  deriving (Show, Functor, Generic1)
  deriving (Applicative, Monad) via Generically1 (Baz f)

As you can see, we did not only derive Applicative for our data types but could also derive Monad.

The reason that this works is that there are instances for Applicative and Monad for the Generic1 representations of these data types. See for example the Product type (:*:). There is however no instance of Applicative for the Sum type (:+:), which is why we can't derive it for non-deterministic types.
You can see the Generic1 representation of a data type by writing :kind! Rep1 Foo in GHCi. Here are simplified versions (excluding meta-data) of the representations for the types above:
type family Simplify x where
  Simplify (M1 i c f) = Simplify f
  Simplify (f :+: g) = Simplify f :+: Simplify g
  Simplify (f :*: g) = Simplify f :*: Simplify g
  Simplify x = x

λ> :kind! Simplify (Rep1 Foo)
Simplify (Rep1 Foo) :: * -> *
= Par1 :*: (Rec1 ((->) Int) :*: Rec1 Foo)

λ> :kind! Simplify (Rep1 Bar)
Simplify (Rep1 Bar) :: * -> *
= Par1 :*: Rec1 IO

λ> :kind! forall f. Simplify (Rep1 (Baz f))
forall f. Simplify (Rep1 (Baz f)) :: k -> *
= forall (f :: k -> *). Rec1 f :*: Rec1 f

Edit: The Generically1 wrapper is also available here: https://hackage.haskell.org/package/generic-data-0.7.0.0/docs/Generic-Data.html#t:Generically1

A: No, this is not obvious at all. Compare the following Applicative instances:

*

*[]

*ZipList

*Data.Sequence.Seq, whose Applicative instance declaration runs to several hundred lines.

*IO

*(->) r

*Parsers in parsec, attoparsec, regex-applicative.

*Proxy in the pipes package.

There very little uniformity here, and most of the instances are non-obvious.

As David Young comments, the [] and ZipList instances "are both, ultimately, two different, equally valid Applicative instances for the list type."

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The following sermon was presented today at the 77th General Convention of the Episcopal Church, meeting in Indianapolis IN through July 12.
Did you hear Ezekiel? Mortals! Stand up and listen! God is sending you to a rebellious house, full of impudent and stubborn folks. Your job is to go tell them, "listen up – here's the deal, from the Big Man himself." And if they don't listen, at least they will have met a prophet.
Garrison Keillor is famous for noting that nobody wants a prophet at a birthday party. Our image of prophets is something like fire-breathing dragons or maybe Nunzilla, but a prophet is simply somebody sent to speak for God, to tell it like it really is. Sometimes prophets speak words of comfort and strength, the kind of words the psalmist is asking for – mercy and relief. And sometimes the prophet speaks words that are harder to hear, reminding us that we're supposed to love God with all we are and have and love our neighbors as ourselves. The reminder usually comes because the audience hasn't been living up to that expectation. Whatever Jesus said in the synagogue seems to have been that kind of challenging word.
Jesus' friends and neighbors obviously don't expect to hear anything prophetic from the ordinary carpenter down the street or from the brother of their friends. He has never stood up in their synagogue before and said anything particularly challenging – so who does he think he is? Mark doesn't tell us what he reads or says. Luke says that it's the part of Isaiah that says, "the Spirit has anointed me to bring good news to the poor, release to the captives, healing to the blind, justice to the oppressed, and to announce the year of the Lord's favor." And his friends and neighbors are offended.
It is offensive – and confronting and challenging – to hear that even though you think you're getting along OK, you've missed the boat. Yet until we can see the chasm between what is and what ought to be, we don't have any hope of changing. Indeed it is the act of crossing that boundary between what is and what ought to be that is so characteristic of prophets. When Jesus is called a prophet, it has to do with erasing the boundary between God and human flesh. Prophetic words of comfort or challenge urge a kind of frontier work – getting across the fence between fear and possibility, reconciling division, transforming injustice, urging the lost onto the road home.
Sometimes those encouragers of boundary crossing come in very ordinary, even quiet, packages – and that may be what the people in Jesus' hometown were so annoyed about. It's harder to ignore somebody you respect or know pretty well.
Have mercy on us, Lord, have mercy, we've had more than enough contempt.
or abuse from proud and conceited people!
We started our gathering by talking about the hope of Americans and indeed people across the world for change, in the face of the contempt and arrogance they hear from Congress and other politicians. We soon moved to talking about the abuse and ridicule we hear from our brothers and sisters in Christ. That sort of confession brought hope, and urged us into other kinds of frontier crossing, beginning with finding a prayer partner. Mine is the Rev. Franklyn Richardson, pastor of Grace Baptist Church in Mt. Vernon, New York and Port St Lucie, Florida. Other kinds of prophetic action and word are going to emerge from this process, including a statement and a number of positive actions to encourage more civil and effective discourse in politics and in our religious lives. Words matter profoundly, and as Christians we affirm that every time we gather to give thanks for the frontier crossing incarnate Word in our midst.
Prophets speak and act for God, with spoken and incarnate words of strength, hope, and challenge. That ministry comes in many forms. Today we're going to give thanks for the prophetic work of the United Thank Offering, reaching out in creative possibility around the globe. Each triennial gathering of the Episcopal Church Women begins with a blessing and distribution of crosses, and the hands that are extended to receive them are a sacrament of blessing for this kind of prophetic work. When Jesus lays on hands and heals a few, even in a place that doesn't think he's got much to offer, he's doing something prophetic. The work those hands of ECW members do in gathering and blessing ministries around the globe is another way of reaching out across borders, boundaries, walls and fences of division.
What about your hands? They, too, are instruments of healing, reconciling, re-creation – let's see those hands! Here is a sacrament of God's mission. How will you use those hands in an impudent and rebellious house? These hands can be instruments of warning, or to comfort and strengthen the wavering. Hands can be instruments of prophetic communication, a gift only some among us have learned.
and to tend the garden we share with all the rest of creation.
Those five marks of mission are the work and mark of prophets, of all Jesus' friends and their partners. All of his commentary about what to take on the trip across the border is a reminder to keep it simple – to go as emissaries of the incarnate word, to be a gift and to speak and act for God's dream – to GO into the world of God's dream.
When we gather like this to make Eucharist, we offer all that we are and have for this work. That little exchange that starts, "lift up your hearts," is about entering another reality – some old translators put it, "hearts aloft!" Get moving! Go cross the frontier between heaven and earth – boldly go where Jesus has gone before – and invite others to go with you to help build the world that God intended at creation.
So – mortals, prophets – stand up! God is sending you to a rebellious house, full of impudent and stubborn folks. As the prophet Pogo said, "is us." Your job is to go and say, "listen up – here's the deal, God's got a better world in mind, and you are needed to help make it happen." And once you've started the conversation about good news, keep moving, keep showing and telling the world what God's dream looks like.
Eventually, the world will know they've met a prophet – a whole community of prophets.
 An Episcopal priest as well, he's been a prophetic force in the search for peace in Sudan.


text:
Trademarks and Service Marks which are used in interstate or foreign commerce may be registered with the United States Patent and Trademark Office. Trademark rights are used to prevent confusion in the marketplace, but not to prevent others from making similar goods or from selling similar goods or providing similar services under a clearly different Mark.
A Trademark is a word, name, symbol or device which is used in trade with goods to indicate the source of the goods and to distinguish them from the goods of others.
A Service Mark is the same as a Trademark except that it identifies and distinguishes the source of a service rather than a product.
The terms "Trademark" and "Mark" are commonly used to refer to both Trademarks and Service Marks.
NOTE: To file a Trademark or Service Mark application, you must be using the Mark with services or goods in the stream of interstate commerce or have a bona fideexpectation that the Mark will be used in the stream of interstate commerce within the next six (6) months.
Registering a Mark is not legally mandated; however, having a Federal Trademark Registration on the Principal Register provides several advantages:
provides a constructive public notice of the registrant's claim of ownership of the Mark;
a legal presumption of the registrant's ownership of the Mark and the registrant's exclusive right to use the Mark nationwide on or in connection with the goods and/or services listed in the registration;
the ability to bring a legal action concerning the Mark in federal court;
the use of the U.S Registration as a basis to obtain registration in foreign countries; and
the ability to file the U.S. Registration with the U.S. Customs Service to prevent importation of infringing foreign goods.


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Tun Tavern: Stop by Tun Tavern to enjoy delicious dining and craft brews near the Atlantic City Convention Center. One of Atlantic City's most popular restaurants, Tun Tavern features award-winning steaks and other house specialties.
Shoe Bar: Join friends and colleagues for cocktails and savory snacks at Shoe Bar, open daily for dinner in our hotel. Browse the memorabilia from past Miss America contests or watch a game on the large LCD screens.
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Premier Lai Ching-te today announced a major Cabinet reshuffle that would swear in nine new top officials. Among the ministries that will get new leaders are justice, finance, transportation, education and the interior ministry. This Cabinet "refresh" falls just before the 9-in-1 elections and is seen as a bid to boost support for the DPP. But the premier today insisted that his personnel rotation is aimed at meeting the needs of government, not elections.
The Ministry of Education has been without a head for more than a month now, but it will come next week in the form of Yeh Jiunn-rong, currently the interior minister.
Interior Minister Yeh Jiunn-rong has told me many times before that the time he promised me is up. He had promised to help me for a period of time but now he wants to go back to his beloved academia. But in the end I asked Minister Yeh Jiunn-rong if he could help lead the Ministry of Education. He was chosen on the basis of his academic standing, patience, perseverance, ideals, and gift for innovation.
Once Yeh leaves his post, current Executive Yuan Spokesman Hsu Kuo-yung will take over as interior minister. And the new Executive Yuan spokesman will be Kolas Yotaka, who is currently a legislator-at-large. The new minister of justice will be Investigation Bureau chief Tsai Ching-hsiang, while the next finance minister will be the current deputy finance minister, Su Jain-rong.
Finance Minister Sheu Yu-jer came to me on his own and said he had completed his part of the mission. He said he hoped to pass on the baton to the younger generation. As for Justice Minister Chiu Tai-san, he has pushed many institutional reforms in the Justice Ministry over the last two years. Recently he has come to me repeatedly and said he wants to rest and go back to school to teach.
As for the Transportation Ministry, the top post will go to Wu Hong-mo, chairman of the Taiwan International Ports Corporation.
Under the leadership of Transportation Minister Ho Chen, we have worked from excellent blueprints over the past two years. In the future, we want to keep accelerating our progress in the Forward-looking Infrastructure Development Program. Of course, transport safety management is also very important. We'll do both at the same time.
Premier Lai will round off his list of nine new appointees with new picks for the National Palace Museum, the Sports Administration, and the Agriculture and Food Agency. He stressed that the Cabinet reshuffle had nothing to do with November's local elections, but was aimed simply at meeting his government's operational needs.


text:
Le département de l'information médicale (DIM) gère l'information de santé des patients, y compris les données personnelles dans un centre hospitalier ou dans un établissement privé de type ESPIC ou ex OQN. À ce titre, il assure le respect des règles de confidentialité, sous l'autorité du directeur de l'établissement de santé et du président de la Commission médicale d'établissement (CME), et le codage de l'information médicale (l'organisation du codage, le traitement, la conservation des données et l'interprétation des résultats).

Missions

L'activité du département d'information médicale se décline selon plusieurs axes :
 il organise le recueil, la circulation et le traitement des données médicales sous l'autorité d'un médecin notamment dans le cadre du Programme de médicalisation des systèmes d'information (PMSI) participant ainsi à la valorisation de l'activité de soins, base de la tarification à l'activité de l'hôpital ;
 il participe ensuite au suivi épidémiologique des patients. Les données médicales recueillies sont protégées par le secret professionnel et le DIM participe à la qualité et à l'intégrité du dossier du patient ;
 il joue un rôle actif dans la mise en place du système d'information de santé centré sur le patient ;
 il traite et analyse les données au moyen d'outils statistiques et produit le rapport d'activité du centre hospitalier.

Notes et références

Voir aussi

Articles connexes
 Information médicale
 Informatique médicale
 Système d'information hospitalier
 Santé publique
 Technicien d'information médicale
 Hôpital

Liens externes
 Site de l'ATIH
 Site du DIM

Santé publique
Hôpital