text:
\section{Introduction}

Traditional approaches for detecting Trans Neptunian Objects (TNOs) rely on the identification of sources within individual images and then linking these sources to generate orbits \citep{kubica+2007,denneau+2013}. More recently digital tracking or ``shift-and-stack" techniques have been developed to search for moving sources below the detection limit of any individual image \citep{Gladman+1997, Allen+2001,Bernstein+2004,Heinze+2015}. These approaches are fundamentally different from the traditional techniques in that they assume a trajectory for an asteroid and align a set of individual images along that trajectory in order to look for evidence for a source.

Shift-and-stack methods share many commonalities with the ``track before detect''
(TBD) method used for the tracking of satellites and
missiles \citep[e.g.][]{reed+1988, johnston+2002}. This field is mature in
literature and implementation, and has been generalized to enable the
detection of not just linear motion, but also the tracking of
``acutely maneuvering non--cooperative
targets'' \citep{rozovskii+1999}.  We will adopt several
of the features of TBD, in particular those described
in \cite{johnston+2002}, who outline the core principles of accumulating the
track detection probability, and in quantifying the false alarm
probability.

A related approach to faint moving object detection is
presented in \cite{Lang+2009}, who describe a search for
high proper motion stars.  These objects move by a distance comparable
to the PSF FWHM over the course of an entire survey (i.e. years).
Thus a direct image stack is sufficient to detect objects.
However, \cite{Lang+2009} return to the individual science
images to perform a joint fit for the proper motion and parallax of
the objects, even though they appear at low signal--to--noise in any
individual image.  The scaling of detection depth in these techniques
goes formally as $\Delta m$ = 1.26 log($N$) magnitudes, or 1 magnitude
deeper after the linking of 6 faint detections, and 2 magnitudes after
40 detections.

The advantage of digital tracking is that we increase the detection limit for a series of $N$ images as $\sqrt{N}$ (assuming a constant point spread function (PSF) and background across all images). For objects having power--law distributions in apparent magnitude -- e.g. the double power--law TNO model of \cite{Bernstein+2004} -- linking 6 epochs of data would yield an increase of 4--7 times as many objects {\it from the same data}. The cost of digital tracking comes from the combinatorial and computational complexity of having to search a large number of candidate trajectories for each pixel within an image.
Digital tracking must combine the individual images along a proposed motion vector that will depend on the assumed distance of the asteroid. Even for slow moving asteroids searches will scale as $Nn$ where $n$ is the number of pixels in an image.

These computational costs have limited the application of digital tracking to searches for slowly moving objects or to narrow ``pencil-beam'' surveys. In this paper we introduce a new approach that utilizes a probabilistic formalism for the detection of sources in images (removing the need to stack or coadd images) and  Graphics Processing Units (GPUs) to massively parallelize the number of searches that can be undertaken concurrently. In \S 2 we introduce a maximum-likelihood formalism for the detection of sources and extend this for the case of moving objects. In \S 3 we apply this approach to the High Cadence Transient Survey (HiTS) and describe some of the filtering techniques that were applied to exclude false positives in the candidate asteroids. In \S 4 we discuss the asteroids detected by this approach and compare their properties to current models and observations.

\section{Fast tracking and stacking of images}

Digital tracking assumes a set of $N$ images have been observed over a period of time (from minutes to days) with the individual images covering approximately the same part of the sky. The individual images are astrometrically shifted along a proposed
motion vector, coadded, and then searched for point sources in the resulting coadds \citep{Gladman+1997,Allen+2001,Bernstein+2004}. This approach is illustrated in Figure \ref{shift_stack_demo} where the image on the far right is the sum of the previous three images added along the motion of the highlighted object. Creating a stack optimized for faint, moving objects must include astrometric
offsets between the images that correspond to the distance that an object has
moved between observations. Unfortunately, this angular velocity
is not known \textit{a priori}, and in fact differs for each moving object in
the field.  Thus a stacked search for Solar System objects in
time--series data requires a sequence of coadds, each optimized for a
particular motion vector.
Due to the combinatorial complexity
of the problem, these efforts have traditionally been optimized for
TNO recovery, where apparent angular motions are small.

During an observation of a Solar System object, there are apparent
motion contributions from the object's own space velocity, as well as
from the reflex motion of the Earth, which is primarily due to the
Earth's motion around the Sun but also includes the Earth's rotation
around its axis.  These contribute to an apparent angular motion of
the Solar System object, which will trail during an
exposure.  If the object trails by more than the PSF full-width half-maximum (FWHM) during
observation, its signal is spread over additional background pixels,
which lessens the overall signal-to-noise \citep{Shao+2014}.

These ``trailing losses" also apply to a
stacked image: the image stack velocity must be close enough to the
true velocity of the object to not spread the signal over more than
the PSF FWHM.  This requirement, together with the range of expected
apparent motions of the desired objects, sets the number or sampling of the velocities
(or orbits) that must be searched and stacks that must be examined.
Typical apparent motions (at opposition) range from $20\arcsec$/hr for
main belt asteroids at 3 AU to $1\arcsec$/hr for TNOs.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{stamp_demo.pdf}
	\caption{Shifting and stacking of individual images along the asteroid's trajectory creates a single point source in the stacked image.}	\label{shift_stack_demo}
\end{figure}

\subsection{A likelihood-based approach for source detection} \label{sec:likelihood_detection}

\subsubsection{Form of single pixel likelihood function} \label{singlePix}

Our goal is to derive the likelihood functions for a given source
being present in the coaddition of a series of images.  First we start
by finding the form of the likelihood for a single pixel in a single
image. The following derivation is based upon work in \citet{kaiser} and interpreted in \citet{bosch} and \citet{Bosch+2017}.
Photons landing on a pixel in a detector follow Poisson
statistics. For a given pixel the probability of counting
$n$ photons with an expected value of $\mu$ is defined as
\begin{equation} \label{eq:1}
P(n|\mu)= \frac{\mu^{n}e^{-\mu}}{n!}.
\end{equation}
This probability can also be interpreted as the likelihood that a
model of the sources within an image (hereafter the true image)
generates a predicted count $\mu$ given we observe $n$ counts on a
pixel. The log-likelihood of our prediction model is, therefore,
\begin{equation}\label{eq:2}
\mathcal{L}(\text{model}) = \ln P(\text{data}|\text{model}) = \ln P(n|\mu) = n \ln \mu - \mu - \ln n!
\end{equation}
Differentiating $\mathcal{L}$ with respect to $\mu$, the
log-likelihood has its peak when $\mu_{o} = n$. Furthermore, if we
Taylor expand around this maximum value then we get
\begin{equation}\label{eq:3}
\mathcal{L}(\text{model}) = n \ln \mu_{o} - \mu_{o} - \ln n! + (\frac{n}{\mu_{o}} - 1)(\mu - \mu_{o}) - \frac{n}{2\mu_{o}^{2}}(\mu - \mu_{o})^{2} + \ldots
\end{equation}
\begin{equation}\label{eq:4}
\mathcal{L}(\text{model}) = \text{constant} - \frac{1}{2} \frac{(n - \mu)^{2}}{n} + \ldots
\end{equation}
where the constant contains all of the terms that depend only on $n$ and so
are independent of our model. Finally, if $n$ is large we can ignore
higher order terms and approximate our likelihood function as that of
a Gaussian with likelihood proportional to
$e^{- \frac{(n - \mu)^{2}}{2n}}$.

\subsubsection{Pixels and PSF}
At this point we need to take a step back and understand what exactly
goes into calculating photon counts at each pixel. To do this we will
follow the derivation laid out in \citet{bosch} but for a
two-dimensional image. First, we start with the number of counts $n(x, y)$ we get
from the true sky intensity $I(x, y)$ on a pixel centered at
$(x,y)$. The observation on our detector will be the true sky
intensity convolved with the PSF, $T(x, y)$. In addition, there will
be an extra integral to account for the binning into a pixel centered
at $(x,y)$ with side length $a$. That gives us
\begin{equation}
n(x,y) = \int_{x-a/2}^{x+a/2} \int_{y-a/2}^{y+a/2} dv \ du \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} dq \ dp \ I(p,q)\ T(u-p, v-q),
\end{equation}
which can be rewritten in terms of two convolutions where we rewrite the pixel binning integral as a convolution with a square top hat function $H(x,y)$ with height 1 and side length $a$
\begin{gather}
n(x, y) = \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} dv \ du \ H(x-u, y-v) \int_{-\infty}^{\infty} \int_{-\infty}^{\infty}  dq \ dp \ I(p,q)\ T(u-p, v-q) \\
n(x,y) = [H \ast I \ast T](x,y)
\end{gather}
and if we exploit the associative and commutative properties of convolutions we can rewrite this as
\begin{equation}
n(x,y) = [I \ast (H \ast T)](x,y)
\end{equation}
and following the procedure in \citet{bosch} simplifying the term in parentheses to
\begin{equation}
T_{a}(x,y) = (H \ast T)(x,y)
\end{equation}
and finally ending up with
\begin{equation}
n(x,y) = [I \ast T_{a}](x,y)
\end{equation}
so that when we refer to the PSF below as $T_{a}$ we are referring to
the PSF including the pixel transfer function.

\subsubsection{Full likelihood function for a single image}
Now we understand how to represent the pixel data as a function of the sky and PSF as well as how to write out the likelihood function of a single pixel. We consider an image $\textbf{x}$, where the $i$th pixel is $\textbf{x}_{i}$ and the real sky is modeled as $f(\textbf{x})$. Then we use our assumption of a large photon count to justify a Gaussian likelihood function as described in \ref{singlePix} and get
\begin{equation}
L(\textbf{x}_i) = P(\text{data}|\text{model}) = P(n(\textbf{x}_{i})|f(\textbf{x}_{i})) = \frac{1}{\sqrt{2\pi\sigma_{\textbf{x}_{i}}^{2}}}e^\frac{-\frac{1}{2}(n(\textbf{x}_{i})-[f \ast T_{a}](\textbf{x}_{i}))^{2}}{\sigma_{\textbf{x}_{i}}^2}
\end{equation}
but if we are looking at the entire image plane we need to go from a single variable Gaussian to a multivariate Gaussian distribution. Thus, the likelihood for the full image is
\begin{equation}
L(\textbf{x}) = P(n(\textbf{x})|f(\textbf{x})) = \frac{1}{\sqrt{(2\pi)^{k}|C|}} e^{-\frac{1}{2}\sum_{i,j} (n(\textbf{x}_{i})-[f \ast T_{a}](\textbf{x}_{i})) \times C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) \times(n(\textbf{x}_{j})-[f \ast T_{a}](\textbf{x}_{j}))}
\end{equation}
where $k$ is the number of pixels and $C$ is the pixel covariance matrix. If we take the log-likelihood function then we have
\begin{equation}\label{fullLikeForm}
\mathcal{L}(\textbf{x}) = -\frac{k}{2}\ln(2\pi) - \frac{1}{2}\ln|C| -\frac{1}{2}\sum_{i,j} (n(\textbf{x}_{i})-[f \ast T_{a}](\textbf{x}_{i})) \times C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) \times (n(\textbf{x}_{j})-[f \ast T_{a}](\textbf{x}_{j}))
\end{equation}
for the full form of the log-likelihood of a given sky model $f(\textbf{x})$.

\subsubsection{Likelihood of detection of a point source}
If we want to find a point source at a pixel $\textbf{y}$ in the image with flux $\alpha$ then the sky model we are proposing is $f(\textbf{x}) = \alpha \delta({\textbf{x} - \textbf{y}})$, a delta function located at the pixel lcoation of the source multiplied by the flux. Now let $n(\textbf{x})$ represent the flux counts for each pixel we have in a background subtracted image and notice that the first two terms in equation \ref{fullLikeForm} are independent of the model $f(\textbf{x})$. Furthermore, in order to keep values positive, we change to the negative log-likelihood function which we are now trying to minimize in order to get the most likely parameters. Thus, we get
\begin{equation}
\mathcal{L}(\alpha, \textbf{y}) = \text{constant} +\frac{1}{2}\sum_{i,j} (n(\textbf{x}_{i})-\alpha T(\textbf{x}_{i}-\textbf{y})) \times C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) \times (n(\textbf{x}_{j})-\alpha T(\textbf{x}_{j}-\textbf{y}))
\end{equation}
where the constant holds terms that are
model independent. Also, we have defined $\alpha T(\textbf{x}_{i}-\textbf{y}) = [f \ast T_{a}](\textbf{x}_{i})$ which is the convolution of the point source sky model with the PSF and is thus equivalent to the PSF centered at $\textbf{y}$.
$T(\textbf{x}_{j}-\textbf{y}))$ is the same for the pixels in the $j$ indexed sum. We have two sums here since we are summing across every combination of pixels from the covariance matrix. If we multiply out these terms it looks like
the following:
\begin{multline}
\mathcal{L}(\alpha, \textbf{y}) = \text{constant} + \frac{1}{2} \sum_{i,j} C^{-1}(\textbf{x}_{i}, \textbf{x}_{j})(n(\textbf{x}_{i}) n(\textbf{x}_{j})) - \alpha \sum_{i,j} C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) n(\textbf{x}_{i})T(\textbf{x}_{j} - \textbf{y}) \\
+ \frac{\alpha^{2}}{2} \sum_{i,j} C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) T(\textbf{x}_{i} - \textbf{y}) T(\textbf{x}_{j} - \textbf{y})
\end{multline}
Furthermore we define the following terms
\begin{gather}
\Psi(\textbf{y}) = \sum_{i,j} C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) n(\textbf{x}_{i})T(\textbf{x}_{j} - \textbf{y}) \\
\Phi(\textbf{y}) = \sum_{i,j} C^{-1}(\textbf{x}_{i}, \textbf{x}_{j}) T(\textbf{x}_{i} - \textbf{y}) T(\textbf{x}_{j} - \textbf{y})
\end{gather}
and once again add into constant the terms that are not model dependent. Finally, this gives us the likelihood in a compact form
\begin{equation}
\mathcal{L(\alpha, \textbf{y})} = \text{constant} - \alpha \Psi(\textbf{y}) + \frac{\alpha^{2}}{2} \Phi(\textbf{y})
\end{equation}
where $\Psi(\textbf{y})$ and $\Phi(\textbf{y})$ are new types of images that can be created using the PSF.

\subsubsection{Coaddition of likelihood images for point source detection} \label{sec:coadd_likelihood}

We make the assumption that the signal for the majority of candidate detections will be dominated by the background noise and that the background noise is independent in each pixel. While there may be sources of correlation between pixels such as electronic detector effects (e.g.\ crosstalk or the dependence of the PSF on intensity), for this treatment we will assume these effects are small enough to ignore and thus will continue as if we have a completely diagonal covariance matrix. This simplifies our calculations into images that can be precomputed with a simple kernel that approximates the PSF.

Thus, our equations for $\Psi$ and $\Phi$ images are reduced to
\begin{gather}
\Psi(\textbf{y}) = \sum_{i} \frac{1}{\sigma_{i}^{2}} \ n(\textbf{x}_{i}) T(\textbf{x}_{i} - \textbf{y}) \\
\Phi(\textbf{y}) = \sum_{i} \frac{1}{\sigma_{i}^{2}} \ T(\textbf{x}_{i} - \textbf{y})^{2}
\end{gather}
where $\Psi$ is the inverse-variance weighted cross-correlation of the PSF and the data, which is also the convolution of the PSF rotated by 180 degrees or just the PSF if it is symmetric.  $\Phi$ is the effective area of the PSF weighted by the inverse variance \citep{Bosch+2017}.

Bright pixels in the image can be dealt with by a mask and we set the inverse variance to zero so that they will add nothing to the likelihood sum. Since trajectories will only cross over this pixel once when we run them over a series of images this serves to give noisy pixels zero weight in the full sum of a trajectory while keeping the information in the other images.

Solving for the maximum-likelihood solution we end up with
\begin{equation}
\frac{\partial \mathcal{L}_{ML}}{\partial \alpha} = - \Psi(\textbf{y}) + \alpha \Phi(\textbf{y}) = 0 \label{max_likelihood_derivative}
\end{equation}
and as a result we find that,
\begin{gather}
\alpha_{ML} = \frac{\Psi(\textbf{y})}{\Phi(\textbf{y})} \label{maxLikeFlux}
\end{gather}
and
\begin{gather}
\mathcal{L}_{ML}(\alpha_{ML}, \textbf{y}) = \text{constant} - \frac{\Psi^{2}(\textbf{y})}{2\Phi^{2}(\textbf{y})} \label{finalLikelihood}
\end{gather}
where $\alpha_{ML}$ is the most likely flux for a source at pixel $x_i$ and $\mathcal{L}_{ML}(\alpha_{ML}, \textbf{y})$ is the probability
of that source given the observation $n(x_i)$.
The kernel that maximizes the likelihood of our flux measurements is, therefore, the 180 degree rotation of the PSF or, in the case of a symmetric PSF, the PSF itself.
Additionally,
Equation \ref{finalLikelihood} is the log form of a Gaussian---where by
analogy $\Psi \simeq (x-\mu)$ and $\Phi^{2} \simeq \sigma^{2}$---so we
can approximate a $\chi^{2}$ distribution. If we define $\nu =
\frac{\Psi}{\sqrt{\Phi}}$ and then make an image of $\nu$, we can call
points above some threshold value $m$ to be $m$-sigma detections \citep{szalay+1999}.

In order to make a coadded likelihood image, we create our $\Psi$ and $\Phi$ images from sums across all pixels in all images. This amounts to making $\Psi$ and $\Phi$ likelihood images for each of our original images, $i$, with the appropriate PSF for each respective image and summing them separately so that
\begin{gather}
\Psi_{coadd} = \sum_{i} \Psi_{i}({\textbf{y}_{i}}) \\
\Phi_{coadd} = \sum_{i} \Phi_{i}({\textbf{y}_{i}}) \\
\nu_{coadd} = \frac{\Psi_{coadd}}{\sqrt{\Phi_{coadd}}}
\end{gather}
Here, $\nu$ is the signal-to-noise of the detection of a source at pixel $x$ within the coadded image. Therefore, the final value for the likelihood of a point object is just the sum of the values at a given set of points in the $\Psi$ image divided by the sum of the values at the same points in the $\Phi$ image. This means that if we wish to do a moving object detection, all that is needed is to sum the $\Psi$ and $\Phi$ values as shown, but we must use the appropriate pixel coordinates for a given trajectory as the $\textbf{y}_{i}$ values in each image---there is no need to shift and stack the likelihood images at any point. The $\Psi$ and $\Phi$ images can also be precalculated meaning we are able to store them in memory as many trajectories are searched.

\subsection{Object detection as Optimization}
Detecting moving point sources in a stack of images can now be approached as an optimization problem. For the linear trajectories we are considering, the core task can be reduced to finding the initial positions $\textbf{y}_{0}$ and velocities \textbf{v} so that $\nu$ is maximized. For a given potential object, the best candidate source is then
\begin{gather}
 \mathbf{\argmax}_{\textbf{y}_{0},\textbf{v}} (\nu_{coadd})
\end{gather}

We hope to find all unique $\textbf{y}_{0}$ and \textbf{v} such that $\nu(\textbf{y}_{0}, \textbf{v})$ is above a desired detection threshold. The most straightforward computational approach to this problem is to evaluate $\nu$ for all possible values of $\textbf{y}_{0}$ and \textbf{v} that describe realistic orbits. An illustration of this approach is shown in Figure \ref{searchtree} starting from a single value of $\textbf{y}_{0}$. This method directly computes $\Psi$ and $\Phi$ for every relevant trajectory through all images by sampling pixels along each trajectory and storing the result if the integrated $\nu$ is above the threshold. The computational complexity of this algorithm is bounded by $\mathcal{O}(na(tu)^2/p)$ where $n$ is the number of images to be stacked, $a$ is the area on the sky to be searched, $p$ is the area of a single pixel, $t$ is the duration between the first and last image, and $u$ is the range of an object's apparent velocity. The complexity scales with time and velocity range quadratically because all trajectories ending anywhere inside an area with radius $tu$ must be considered.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{conesearch.pdf}
	\caption{ Visualization of the many trajectories that must be searched in order to cover a defined velocity and angle range over a stack of images of the same field taken at different times.}	\label{searchtree}
\end{figure}

\subsection{GPU Implementation}

Comprehensively searching a stack of images for moving objects directly requires computing the value of $\nu$ billions of times. Fortunately each evaluation of $\nu$ is entirely independent from the others, and this allows for natural parallel execution. Rectangular patches of adjacent trajectories are grouped into thread blocks with dimensions 32x16 which are distributed across the GPU's multiprocessors. $\Psi$ and $\Phi$ pixels are interleaved in memory and within each thread block horizontally adjacent trajectories access them contiguously, enabling high throughput. To achieve good performance all images must be stored in the GPU's memory at once, which limits the size and number of images that can be used to about 100 4K x 4K images. In general, the runtime of this algorithm can be estimated as $Nnk$ where $N$ is the number of images in the stack, $n$ is the number of trajectories, and $k$ is a performance constant that is experimentally determined by the computer hardware and implementation. In our implementation we have a measured value of $k$ about $2.4\times10^{-11}$ seconds per image for each trajectory. Practically this means searching for a wide range of objects (240 billion trajectories) in a stack of 30 images (4K x 4K) takes about 180 seconds.
Besides computing $\nu$, we also use the GPU for convolving the images. The convolution is done in a single pass with a spatially invariant kernel. In this work we only used a Gaussian PSF approximation, and could have used a two-pass separable convolution but chose to use a slower single pass to support non-Gaussian PSF's in the future.

\section{Searching for faint KBOS} \label{sec:search_description}

\subsection{The High Cadence Transient Survey (HiTS)}\label{sec:hits}

We tested our software using the first three nights (February 17-19, 2015) of the 2015 campaign of the High Cadence Transient Survey (HiTS) \citep{HITS}. The 2015 HiTS campaign involved repeatedly visiting 50 3-square-degree fields in the DECam \textit{g}-band filter. There were typically 5 visits per night to each of the 50 fields, taken with a 1.6hr cadence. The remaining three nights of the 2015 HiTS campaign data, also taken with the DECam \textit{g}-band, were used for follow up of the detected objects \citep{HITS}.

These data were taken using the Dark Energy Camera (DECam) at Cerro Tololo Interamerican Observatory (CTIO). The best quartile of seeing at CTIO is about 0.4'' full width at half maximum (FWHM). DECam has 60 2K x 4K CCDs, each with a pixel scale of 0.26 arcsec/pixel. We processed all of the data using the LSST Data Management (DM) Software \citep{lsst-dm} and ran our software using the warped science images that were laid out in 4K x 4K pixel patches. This means that the effective field of view of each warped image is about 0.34 square degrees  \citep{Flaugher_DECam_Instrument}. Because we currently do not search trajectories across CCDs, this is the effective field of view of any individual search. We also used the masks and variance planes from the LSST DM processing output.

\subsection{Application of the KBO search}

We created $\Psi$ and $\Phi$ images from the individual science images. To mask static objects we identify all sources that are detected at more than 5$\sigma$ above the background and mask the pixels associated with these sources. Masks for each static source are grown by an additional two pixels (in radius) to exclude lower surface brightness halos around these sources. To ensure that we do not mask bright \textit{moving} sources we require that any masked pixel in a science image must be masked in at least one of the other science images (i.e.\  a source must be present at the same position in two of the images for it to be defined as static and masked). We apply the union of the mask for all individual images as a global mask to the final science images.

Our search started at every pixel in the earliest observation (in time) for each HITS field and covered a range of 32,000 trajectories across the stacks of 12-13 images corresponding to three nights of HITS data. The 32,000 trajectories came from a linearly-spaced grid of 128 angular steps within $\pm 12^{\circ}$ of the ecliptic and 250 steps in velocities ranging from 1"-- 5.7"/hour. This grid was set up so that at the end of the search period, our maximum separation between the final pixels in a search pattern would be no more than approximately 2 PSF widths. This means that that we would be within 1 PSF width of any possible trajectory. For this search, we went down to a signal-to-noise ratio (SNR) threshold of 10, as measured by the $\nu$ parameter introduced in Section \ref{sec:coadd_likelihood}. For these data, this corresponds to a single image limiting magnitude in \textit{g} of 23.1.

\subsection{Filtering of candidate trajectories}

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{filtering_demo.pdf}
	\caption{Left: Change in light curve when an image with an outlier flux is removed from the light curve. Right: Shifted and stacked postage stamps before and after outlier removal. After the single outlier observation is removed by the filter the trajectory is obviously not following a true object and is discarded.}	\label{filter_demo}
\end{figure}

After running the initial GPU search, we use a set of Python tools to filter out false detections. One such tool is an outlier filtering process that modifies estimates of the flux and variance of an object given a sequence of observations. This is done using the light curve of the candidate trajectory and a variation on the Kalman filter \citep{kalman} commonly used in signal processing. We filter out any observations that are more than five standard deviations away from the best fit Kalman flux at that observation and use the remaining observations to recalculate the likelihood of the candidate. As an example of this process, Figure \ref{filter_demo} shows postage stamps of the candidate before and after the filtering. In this case a fast, bright, moving object moved across the trajectory in a single image. Excluding that image leads us to the correct decision to discard this candidate trajectory, as the likelihood of an object along this trajectory drops to near zero, once the interloping object is removed.

After the outlier detection we build coadded postage stamps for all remaining candidate trajectories. We then use scikit-image \citep{skimage} to calculate the central moments of the images and filter based upon the similarity of the moments to a Gaussian centered at the middle of the stamp. This does a good job of eliminating elongated shapes and trajectories where a bright source appeared in a single image but was off center. For example, in Figure \ref{moment_filter} the left column shows postage stamps of real objects that passed through the filtering. The right column of the figure shows postage stamps that made it through the outlier filtering but were ruled out after the image moment filter.
\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{moment_filtering.pdf}
	\caption{Left Column: Stamps that passed the lightcurve filter and the image moment filter. Right Column: Stamps that passed the lightcurve filter but were rejected by the image moment filter.}	\label{moment_filter}
\end{figure}

Our final step in the filtering process is clustering to remove duplicate results from the same object. We take the starting x and y pixels and the horizontal and vertical velocities of the candidate trajectories and use the DBSCAN clustering method \citep{dbscan} in the scikit-learn python package \citep{scikit} to group similar trajectories. We then take the highest likelihood trajectory for each group and save the results with postage stamps and light curves to file for final examination by eye.

\subsection{Found Objects} \label{subsec:found_objects}
\subsubsection{KBO properties in the HiTS field}

In total we found 45 KBOs in our search, of which only 6 were previously detected by the Pan-STARRS 1 (PS1) survey according to the Minor Planet Center (MPC). PS1 used the Pan-STARRS Moving Object Processing System (MOPS) which links detections from sources identified in individual difference images \citep{denneau+2013}.

The full information for all our object detections is shown in Table \ref{object_table} in Appendix \ref{sec:object_table}. We used the orbit fitting code of \citet{bernstein} for initial orbit determination and used the remaining HITS data (see Section~\ref{sec:hits}) to get additional observations where possible before submitting to the MPC. From this we estimated that, for the 45 KBOs, semi-major axes ranged from 21 AU to 67 AU and DECam \textit{g}-band magnitudes ranged from 22.1 to 24.7 mags. Figure \ref{semimajor_axis_inclination} compares semi-major axis to inclination for our discoveries and shows the overlap with KBO populations commonly discussed in the literature.
\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{detected_ai.pdf}
	\caption{Semi-major axis versus inclination for detected objects with the range of different KBO populations highlighted.}	\label{semimajor_axis_inclination}
\end{figure}

\subsubsection{Comparison with known asteroids}
The candidate objects were checked with the MPC database of known objects. Of the 45 objects that were detected, 6 were known. Four of these objects were detected in our search down to a SNR threshold of 10 and they are noted in Table \ref{object_table}. The 2 remaining objects were much fainter at V magnitudes of 24.1 and 24.3 while the faintest MPC object we recovered in our search was at V = 23.0. We attempted to recover the objects at a fainter SNR threshold by rerunning the search on the image stacks corresponding to the predicted locations. One of the objects was recovered when we reduced our SNR threshold to 5.96. The final object ran from the edge of one image stack to another between the first and second night. Our code cannot currently account for this situation in our search. However, we ran the search using only the second and third nights of data to find the object and our code was able to find it at a reduced SNR value of 5.31. This means that when going to a faint enough search threshold we were able to recover all of the known KBOs in the search area.

After submission to the MPC database two more results were matched to objects in the MPC catalogs. Previous observations had not predicted the orbits to fall within the observation fields, but were linked and recalculated by the MPC after submission of our results. These new matches are also noted as previously discovered in Table \ref{object_table}.

\section{Results}

\subsection{Recovery Efficiency} \label{sec:recovery_efficiency}

To understand the efficiencies of our search method, we inserted simulated objects with \textit{g} magnitudes between 20-26 into the images from one of the 2015 HITS fields and tested our ability to recover these objects. The objects all had a simple Gaussian PSF that matched our search PSF and the velocity angles and magnitudes were uniformly distributed within the ranges of our search parameters. Figure \ref{simulated_recovery} shows the results in terms of counts on the left and the fraction of the total simulated set on the right. Both plots are a function of DECam \textit{g} magnitude with bin widths of 0.25 magnitudes. For the right plot we fit an efficiency function of the form $f(m) = f_{0} / e^{1 + \frac{m - L}{w}}$, where $f_{0}$ is the efficiency ceiling, $L$ is the 50\% detection probability magnitude, and $w$ is the width in magnitudes of the drop-off in sensitivity.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{simulated_recovery.pdf}
	\caption{Recovery of simulated objects inserted into a HITS field. Left: Histogram comparing counts of recovered simulated objects to the full set as a function of magnitude. Right: Fraction of recovered simulated objects as a function of magnitude fitted with an efficiency curve for both the full range of simulated objects and for $g > 21$ only.}	\label{simulated_recovery}
\end{figure}

We plotted two efficiency functions, one for the entire range of magnitudes of the simulated objects and then one at magnitudes $g > 21$. The drop-off at the brighter magnitudes is due to the extra masking we did at a specific count threshold. We will say more about this effect in the discussion in Section \ref{sec:discussion}. On the fainter end we see the limit set by the SNR cutoff at SNR = 10. The detection efficiency ceiling for the full range is $84.6\pm3.7$\% with a 50\% threshold at $g=24.13\pm.07$ and drop-off width of $0.11\pm.07$ mags. When we look at efficiencies at $g > 21$ we get an efficiency ceiling of $92.5\pm1.2$\% and a 50\% detection threshold at the very similar $g=24.10\pm.02$ value and drop-off width of $0.14\pm.02$ mags.

\subsection{Comparison with Existing Models}
While our original searches used only three nights of data, we had additional nights in the HITS data that we used for supplying additional observations when estimating the orbits of the discovered KBOs. This still limited our baseline for the discovered objects to a range from 3-10 days and made accurate estimates of semi-major axis and eccentricity difficult. We have confident measurements on the inclinations and all our inclination estimates for objects in the MPC catalog matched the published inclinations within 1 $\sigma$ of the output values from the \citet{bernstein} orbit fitting code. Therefore, we present a comparison of our inclination distribution to the general KBO population such as that in \citet{brown_inclinations}.

\subsubsection{Inclination Distribution} \label{sec:inclination_comparison}
Our inclination values range from $7^{\circ}$-$44^{\circ}$ where the lower limit comes from the fact that our closest field to the ecliptic is at $-6.4^{\circ}$. Even though our fields are all at moderate latitudes off the ecliptic (as far as $-21.3^{\circ}$), \citet{brown_inclinations} provides a method to compare our results to a predicted distribution by using the inclinations of the objects and the ecliptic latitude of discovery.

\citet{brown_inclinations} estimates an inclination distribution for the full KBO population with a double Gaussian multiplied by $\sin{i}$:
\begin{equation} \label{brown_distribution_equation}
f_t(i) = \sin{i}[a\exp{(\frac{-i^{2}}{2\sigma^{2}_{1}})} + (1-a)\exp{(\frac{-i^{2}}{2\sigma^{2}_{2}})}]
\end{equation}
where $a = 0.89$, $\sigma_{1} = 2.7^\circ$ and $\sigma_{2} = 13.2^\circ$. To compare our results to this distribution we follow the method outlined in Section 3 of \citet{brown_inclinations}. For a given inclination distribution, $f_t$, the probability that an object, $j$, with discovery latitude, $\beta_j$, would have an inclination equal to or below the actual inclination, $i_j$ is given by Equation \ref{discovery_inc_probability}.
\begin{equation} \label{discovery_inc_probability}
P_j = \int^{i_j}_{\beta_{j}} \frac{f_{t}(i')}{(\sin^{2}i' - \sin^{2}\beta_{j})^{1/2}}di' \times [\int^{\pi/2}_{\beta_{j}}\frac{f_{t}(i')}{(\sin^{2}i' - \sin^{2}\beta_{j})^{1/2}}di']
\end{equation}
The distribution of $P_j$ for the actual KBO population estimated by \citet{brown_inclinations} varies uniformly between 0 and 1. Therefore, if we take as the null hypothesis for our observations that they are an unbiased sample down to our magnitude limits and representative of the distribution of KBO inclinations in the fields we searched we should compare the distributions of $P_j$ for our objects to the uniform distribution. To do this we start by calculating $P_j$ for each of our objects and plot their sorted distribution in Figure \ref{brown_ks_test}. We compare our observed distribution to the inclination distribution of \citet{brown_inclinations} using the Kuiper variant of the Kolmogorov-Smirnov (K-S) test as done in \citet{brown_inclinations} and according to Equation \ref{ks_test_statistic}.
\begin{equation} \label{ks_test_statistic}
D = \max(P_j - j/N)
\end{equation}
The actual test statistic is $D\sqrt{N}$ where $N$ is the sample size which is $N=45$ in our data set. In order to find the confidence levels for the test, we create $10^5$ sets of N=45 random samples drawn from a uniform distribution and calculate $D\sqrt{N}$ comparing to a uniform distribution. The $1\sigma$ confidence value occurs when the probability of getting higher than a given $D\sqrt{N}$ value is 84.1\%. We find this to be at $D\sqrt{N} = 1.47$.

Finally, we calculate the $P_j$ values using the Monte Carlo methods described in \citet{brown_inclinations}. We first draw $10^5$ inclinations from the \citet{brown_inclinations} distribution and randomly place them along circular orbits. For each of our observed objects, $j$, we then take all of the Monte Carlo objects within $\pm 0.5^\circ$ of the latitude of discovery, $\beta_j$ and construct an empirical inclination distribution. In order to derive $P_j$ for an object, we use this distribution to calculate the probability that an object with the given $\beta_j$ will have an inclination at or below $i_j$. Using this set of $P_j$ values we then perform the K-S test compared to a uniform distribution between 0 and 1. We perform the Monte Carlo simulation 1000 times and use the mean $D\sqrt{N}$ value as our test statistic for comparison. The K-S test comparison for one of the Monte Carlo distributions is shown in Figure \ref{brown_ks_test}. Our mean $D\sqrt{N}$ value after 1000 runs was 1.37, corresponding to a confidence level of 75\% and within the $1\sigma$ level. This means that we cannot reject the hypothesis that our observations come from the \citet{brown_inclinations} distribution and are consistent with this prediction.
\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{brown_ks_test.pdf}
	\caption{Kuiper variant of K-S Test comparing \citet{brown_inclinations} inclination distribution to our recovered results.}	\label{brown_ks_test}
\end{figure}

As a further comparison we did a basic survey simulation to determine the expected distribution of objects we would find in the HITS data. We used the Monte Carlo distribution of inclinations and locations of objects scattered around circular orbits from Figure \ref{brown_ks_test} along with the locations of the HITS fields and approximated the DECam field of view as a circle with $2.2^\circ$ diameter. With this information, we recorded the objects in the Monte Carlo simulation visible through the survey pattern. We then scaled this distribution to the discovery of the same number of objects that we found in the data and plotted them on top of one another in Figure \ref{figure:inclination_comparison}. A $\chi^2$ test on this data gives $\chi^2 = 2.23$ and a p-value of 0.69, meaning we cannot reject the hypothesis that our observed samples come from the \citet{brown_inclinations} inclination distribution function for the general KBO population.
\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{brown_comparison.pdf}
	\caption{Inclination distributions of detected objects in the HITS fields compared to predicted Brown distribution accounting for the ecliptic latitudes of the HITS observations and normalized to the same number of discovered objects.}	\label{figure:inclination_comparison}
\end{figure}

\subsubsection{Magnitude Distribution}
We next compare the magnitudes of our discoveries to the KBO magnitude distribution of \citet{fraser_luminosity}. This distribution gives the number of observed objects per square degree as:
\begin{equation} \label{luminosity_distribution}
N_{obs} = 10^{\alpha(m-m_o)}
\end{equation}
where $\alpha = 0.65 \pm 0.05$ and $m_{o} = 23.42 \pm 0.13$ for R magnitudes. Since Equation \ref{luminosity_distribution} is based upon the number of expected objects per square degree at the ecliptic we needed to scale our viewing area appropriately. Satisfied by the work in Section \ref{sec:inclination_comparison}, where we showed our results are consistent with the \citet{brown_inclinations} inclination distribution, we converted this to a latitudinal distribution. We then multiplied the 3 square degree DECam viewing area for each field by the fraction of expected objects at the field's ecliptic latitude compared to the maximum value at the ecliptic. This gave us an effective viewing area of 91.05 square degrees. The next step was converting between the R magnitudes used for Equation \ref{luminosity_distribution} and the \textit{g} magnitudes of our observations. \citet{fraser_luminosity} also had to do magnitude conversions for various datasets and used a KBO $<g'-R>$ color of 0.95 which we use here as well. Putting together the scaling and magnitude offsets we compare our results to the \citet{fraser_luminosity} expected number of objects for our survey fields in Figure \ref{luminosity_comparison}.
\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{expected_diff_counts.pdf}
	\caption{Completeness comparison of KBOs found in HITS survey using KBMOD. Our results are consistent with a complete sample at 24th magnitude compared to the single image depth of 23.1.}	\label{luminosity_comparison}
\end{figure}
The drop-off expected from Section \ref{sec:recovery_efficiency} around 24th magnitude is present and seems to occur after $g=24.25$ after which we fall below 50\% completeness which is consistent with the magnitude where our search drops below 50\% efficiency as shown in Section \ref{sec:recovery_efficiency}. Figure \ref{luminosity_comparison} also shows the 10-sigma threshold we used in our search of the HITS data. A $\chi^2$ test on the data for $g<24.25$ gives $\chi^2 = 10.21$ and a p-value of 0.25, meaning our results are consistent with the \citet{fraser_luminosity} luminosity distribution.

\section{Discussion} \label{sec:discussion}

\subsection{Filtering Analysis}

We used the field with simulated objects from Section \ref{sec:recovery_efficiency} to study the effects of the various stages of our filtering process. The red line in Figure \ref{fig:filtering_effects} shows the results for the searches over the full field in our processing which included an extra masking step that we discuss below. We start with the total number of searches based upon the number of grid steps multiplied by the number of pixels on the focal plane. We only keep those above our detection threshold which is the biggest single reduction in the number of possible results. After that, each step in our filtering process is able to reduce the number of false positives by 1-3 orders of magnitude in the number of total results. The most effective is the postage stamp filtering which uses the moments of the postage stamp image and their resemblance to those of a Gaussian source model.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{filtering_effects.pdf}
	\caption{Number of positive trajectories after each step of filtering the search results from a field with simulated objects inserted.}	\label{fig:filtering_effects}
\end{figure}

\subsection{Masking and Threshold Effects} \label{subsec:mask_thresh_effects}

We made a series of decisions in our search based upon our target objects and the use of science images instead of less contaminated difference images. The first choice we made was to include an additional bright pixel mask when creating our $\Psi$ and $\Phi$ images. This mask was in addition to the masking described in Section \ref{sec:search_description}. Any pixel in any image that exceeded 120 counts, corresponding to $g \sim 21$, was masked in our searches. This additional masking covers extended bright halos, that were not covered by our initial footprint masking, and bright fast moving objects which are likely much closer to Earth than our target population. The effects of additional masking on recovery were tested on the simulated object field (Section 4.1). Comparing the red line with this extra masking to the blue line without it in Figure \ref{fig:filtering_effects} shows the major benefit of this masking was a significant decrease in the amount of false positives we had to filter out after our likelihood cut. The amount of positive results after the likelihood cut in Figure \ref{fig:filtering_effects} shows about five times more objects to filter after the threshold cut without the masking. A decrease of $4 \times 10^{8}$ false positives over a single field saves us hours of computation time during our analysis since we processed the results through the lightcurve filter at a rate of 70 seconds for 500,000 objects. Over 50 fields this adds up to days of processing time that we were able to avoid.

However, we also looked to see what was the price we paid for this extra computational efficiency. Figure \ref{fig:simulated_recovery_compare} compares the efficiency curves of Section \ref{sec:recovery_efficiency} and Figure \ref{simulated_recovery} to the results without the additional bright pixel masking. The recovery efficiency is higher across all magnitudes at $92.7\pm1.5$\%, but when we compare to the recovery at only $g > 21$ with the masking it is very similar to the $92.4\pm1.2$\% of that result. The 50\% efficiency depth is nearly unchanged at $g=24.09\pm.03$ without the masking meaning the bright pixel masking does not affect our overall depth but only the bright end of our recovery at $g < 21$. Using the \citet{fraser_luminosity} curve for the expected number of KBOs for magnitudes $20 < g < 21$ in our survey fields, we calculate that the number of expected objects in this magnitude range to be less than 0.3. We find this to be an acceptable trade off for the computational savings in this particular example. When going to difference imaging or with a different population of brighter expected objects we do not plan to include this extra masking step.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{simulated_recovery_compare_thresh.pdf}
	\caption{Comparison of efficiency curves run on the same simulated object set with and without the count masking described in Section \ref{subsec:mask_thresh_effects}.  There is almost no effect on the depth of our recoveries.}	\label{fig:simulated_recovery_compare}
\end{figure}

Another decision we made was to set our threshold for detection at a limit of 10$\sigma$ instead of a lower, typical catalog level threshold of $5\ \sigma$. This decision was motivated by the use of science images and the extra noise that would be present as this threshold was lowered. We looked at the increase of false positives as we go from 10$\sigma$ to 5$\sigma$ by rerunning the same field as Section \ref{sec:recovery_efficiency} without any simulated objects and the timestamps randomly scrambled so that any detections were false positives. We show histograms comparing the false positives to true detections at the two thresholds in Figure \ref{fig:false_positive_comparison}. We also calculated the precision of our final results where precision is defined as $\frac{True\ Positive}{True\ Positive\ +\ False\ Positive}$ and show the results in Figure \ref{fig:precision_comparison}. While we do detect objects to over a magnitude fainter ($g \sim 25.4$) with the lower likelihood threshold we are overwhelmed by false positives at the fainter magnitudes and even at magnitudes at which we achieve high precision with a higher threshold. At a threshold of 5$\sigma$ we are below 50\% precision at $g = 24.1$ while we are never below 90\% precision when using a 10$\sigma$ threshold. This degradation at a brighter magnitude in the 5$\sigma$ results happens because bright false positives with fewer observations will appear at a higher likelihood than a dim object with more observations (e.g., a greater number of the observations were in a masked area or off the edge of the CCD). Due to this false positive performance when running on science images we used the 10$\sigma$ threshold in this work to show the effectiveness of the algorithm and code while also producing useful results. We discuss our future plans in the next section including what we will do to run the code at a lower detection threshold going forward.

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{false_positive_comparison.pdf}
	\caption{Comparing the false positives at detection thresholds of 10$\sigma$ versus 5$\sigma$ in a field with simulated objects inserted. False positives overwhelm our filtering methods at the lower threshold when using science images.}	\label{fig:false_positive_comparison}
\end{figure}

\begin{figure}
	\centering
    	\includegraphics[width=\linewidth]{precision_comparison.pdf}
	\caption{Comparing the precision at detection thresholds of 10$\sigma$ versus 5$\sigma$ in a field with simulated objects inserted. We are very confident in our detections at 10$\sigma$ with science images but would not be if we were to go to 5$\sigma$ detections with the science images.}	\label{fig:precision_comparison}
\end{figure}

\subsection{Future Work}

There is ongoing work to decrease the detection threshold for sources in order to extend the searches ov
er longer timescales thus allowing us to find fainter and more distant objects. Work with longer baselines and larger stacks of images would also allow us to push this detection limit lower and confidently go beyond the limits of current population studies of KBOs. We plan to use the code with difference images in the future which we hope will reduce the time spent filtering and also remove many of the artifacts from science images that lead to false positives above 5$\sigma$ and also pushed us to add in additional masking procedures in this work. We also plan to move to a deep learning based postage stamp classifier that we hope will perform well at fainter magnitudes and plan to do a comparison versus our existing technique on the difference imaging search. We will also address working with non-linear trajectories and methods to find objects that move from one chip to another during the survey period. Finally, we are also working on enhancements to the GPU algorithm that will allow us to spend less time searching low likelihood trajectories thereby increasing search efficiency.

All of these improvements will help us explore deeper into the Kuiper Belt and enhance our ability to use stacks of images from long baseline surveys such as ZTF or LSST in the future.

\section{Conclusion} \label{sec:conclusion}

In this paper we presented a new algorithm that uses the power of GPU processing to search for slow moving sources across a sequence of images. Our approach is capable of searching over $10^{10}$ candidate moving object trajectories in one minute. Applying these techniques to existing data
we discovered 39 new KBOs and the recovered 6 KBOs already present in the Minor Planet Center catalogs. Finally, we used the results of our search to compare to the \citet{brown_inclinations} Kuiper Belt inclination distribution and the \citet{fraser_luminosity} magnitude distribution. We found both of the models to be consistent with observed results indicating that the recovered sample matches overall published characteristics of the KBO population. Combined with the high rates of detection efficiency recovered in tests, this indicates that our software provides a nearly complete recovery of an unbiased sample of moving objects down to the detection limit.

Our software, Kernel Based Moving Object Detection (KBMOD) is available to the public at \url{https://github.com/dirac-institute/kbmod}. This includes the GPU searching code as well as python analysis code we have used to process the search results. Development is continuing and can be followed at the Github repository hosting the code.

\acknowledgements
We would like to thank the anonymous referee for very helpful feedback that improved this paper. We also wish to thank Jim Bosch for valuable insights when we were starting this project. AJC and JBK acknowledge partial support from NSF awards AST-1409547 and OAC-1739419. AJC, JBK, PW  acknowledge support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation.

\software{KBMOD \citep{whidden+2018}, LSST DM Stack \citep{lsst-dm}, astropy \citep{astropy}, scikit-image \citep{skimage}, numpy \citep{numpy}, CUDA \citep{cuda}, scikit-learn \citep{scikit}, pandas \citep{pandas}, matplotlib \citep{matplotlib}}

redpajama_set_name: RedPajamaArXiv


text:
Humans vs. Superbugs: Who Will Win?
May 6, 2013 January 25, 2023 Anna Tomasulo
More and more, antibiotics are proving useless in the fight against many common diseases. Here's how we got here, and how we fight—and win—against these increasingly powerful bacteria.
Back in the late 1960s, U.S. Surgeon General Dr. William H. Stewart allegedly made this provocative, very definitive statement: "It is time to close the book on infectious diseases, and declare the war against pestilence won."
Fast forward 50 years and the U.K.'s Chief Medical Officer, Dame Sally Davies, had quite a different take, saying that antibiotic resistance to disease poses nothing less than an "apocalyptic" threat, reported The Guardian. The Centers for Disease Control added its voice, saying that antibiotic-resistant bacteria were a "nightmare," and called for action.
So what happened in those 50 intervening years?
Nothing that you couldn't see coming, which may make the current state of affairs even more tragic. The truth is, antibiotic resistance goes way back. In 2012, a group of scientists from the University of Akron, Ohio, and McMaster University, in Ontario, Canada, reported in PLOS One on the discovery of antibiotic-resistant bacteria in an underground cave in New Mexico that has been isolated for about four million years.
It helps to understand a few of the basics about resistance. First, there are two types—intrinsic and acquired. If a bacterium is intrinsically (or innately) resistant to a given antibiotic, the bacterium is resistant to a certain type of antibiotic coded into its DNA. Either due to the way the bacterium is structured or the way it functions, certain antibiotics are powerless against the germ.
As we create more antibiotics and introduce them into the environment, in animals, and in humans, microbes get smarter. They evolve for survival; this is called acquired resistance. Bacteria can acquire resistance either through a genetic mutation or by receiving genetic material from other bacteria who already have resistance. In science-speak, this is referred to as "horizontal gene transfer."
The modern era of antibiotics really started in the early 1900s with German scientist Paul Ehrlich. While looking for something that could kill specific bacteria, he produced a chemical dye, commercially known as Salvarsan; this compound (arsphenamine) became a magic bullet treatment against syphilis. Then, in 1928, English scientist Alexander Fleming noticed something odd in one of the petri dishes in which he had been culturing bacteria: Mold had begun to grow. Around the mold, all the bacteria was gone—it had been killed; this mold was Penicillum notatum, what we know today as penicillin.
A few years later, in 1935, German biochemist Gerhard Domagk created the first commercially available antibiotic, sulfonamide (or Prontosil as it was known then); these are the "sulfa drugs" we still use today and that have been used to treat infections that cause meningitis and pneumonia. By the late 1930s scientists Howard Florey and Ernst Chain had picked up on Fleming's research and, after teaming up with an American agricultural company, they began producing penicillin as a widely available antibiotic—just in time for America's entry into World War II. By the end of the war, penicillin was in mass production. With sulfa drugs and penicillin as huge success stories, the U.S. was beating back infectious disease.
At least that's what we thought. In the mid-1940s, Fleming saw what might be coming and issued a warning in the New York Times: We should avoid the overuse of penicillin if we want to stem the proliferation of antibiotic resistance, he said; even then, there was already growing resistance to sulfonamides, the first antibiotics.
Even if Fleming's message had sunk in, it's clearly been forgotten today. According to a 2009 article in the New England Journal of Medicine, nearly three million kilograms—about 6.6 million pounds—of antibiotics were given to humans that year. A year later, twice as much—about 13 million kilograms—were given to animals. Other reports suggest that about half of the antibiotics prescribed in 2010 were not even needed. Antibiotic resistance is so worrisome to many infectious disease and other health experts that it even earned a spot in the World Economic Forum's 2013 Global Risks Report.
Today, members of the science and medical communities plead for more attention to the issue, but and as journalist Maryn McKenna writes in Wired magazine, the pleas are met with "instant alarm, followed almost immediately by apathy."
So what, truly, is the outlook? Are we doomed? Will the bugs eventually win—or do we have a shot at doing something about the growing resistance to superbugs like MRSA, carbapenem-resistant enterobacteriacae, and malaria? The Food and Drug Administration says it has groups on the case, like its Center for Drug Evaluation and Research and the government-initiated surveillance program, the National Antimicrobial Resistance Monitoring System (NARMS).
But many say we are not doing nearly enough. The UK Health Protection Agency and Dame Davies say that the "pipeline [for antibiotics] is running dry" and that we have a broken model for antibiotic production. Drug companies favor medications for chronic diseases like diabetes and hypertension since these conditions require medications over years or decades, so are much more profitable than short-term treatments like antibiotics.
The Infectious Disease Society of America (ISDA) reports that our progress toward the goal of developing 10 new antibiotics by 2020 is definitely not on track: Just two new antibiotics have been approved since 2009. And only seven more are in stages two or three of research and development—a figure ISDA calls "alarmingly low." ISDA's graph showing how many antibiotics have been approved over the past few decades makes this very clear: In the 1980s as many as 16 new antibiotics were approved.
But there is hope. In the same New England Journal of Medicine piece, physicians outlined five areas of focus in the fight to beat the superbugs, as well as the current status of each (meaning whether we are making progress or not). Our goals, they said, should include:
• Preventing infection and resistance
• Refilling the antibiotic pipeline by aligning economic and regulatory approaches (making it easier to develop, approve, and distribute improved antibiotics)
• Preserving the antibiotics we already have and slowing resistance to these drugs (This may mean decreasing or eliminating use of the drugs in animals for growth, for example, and better public reporting on antibiotic use to get a clear idea of who's using antibiotics and why, partly to help stem the prescribing of unnecessary drugs.)
• Looking at treatments that attack microbes with decreased potential to drive resistance, which may mean we need to develop methods that don't kill bacteria, but prevent them from causing disease in people.
• Developing treatments that target the host (i.e., people and animals) rather than microbes to avoid making resistance worse.
Whatever efforts we undertake, though, one message is crystal clear: We can no longer be apathetic about antibiotic resistance.
In the coming weeks we'll be taking a look at some of the superbugs posing the biggest threat, both in the U.S. (carbapenem-resistant enterobacteriacae, MRSA, gonorrhea) and abroad (drug-resistant tuberculosis and malaria).
This article was originally posted on TakePart.com.
Bacteria Without Borders: The Fight Against TB & Malaria
June 3, 2013 January 25, 2023 Anna Tomasulo
Sex and the Superbug: Meet Antibiotic-Resistant Gonorrhea
May 29, 2013 January 25, 2023 Anna Tomasulo
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redpajama_set_name: RedPajamaCommonCrawl


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And now I am seeing him as an almost hero.
So, what happens when Princess meet Darth Vader?


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The control box may have over three terminals. After you have the correct size box and have fed the cable to it, you're almost prepared to permit the wiring begin. Then there's also a fuse box that's for the body controls that is situated under the dash. 8 Way 3 Motor Car Seat Wiring Diagram. You will find that every circuit has to have a load and every load has to have a power side and a ground side. Make certain that the transformer nameplate power is enough to supply the load that you're connecting.


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Home Sections Technology
Remote Employees Aim for a Healthy Work-life Balance
By Kayla Ebner September 5, 2019 2136
Mom Tech
Many people assume that working from home is less productive than spending time in the office. However, the opposite is oftentimes true. This is especially true now that technology allows for quick and easy communication between home and office, giving employees, especially moms, the ability to work efficiently from home while maintaining a healthy work-life balance.
When Tiffany Appleton looks back on raising her now-19-year-old daughter, she remembers how difficult it was to have a full-time job on top of the 24-hour job called parenting. As a single parent, she really didn't have a choice whether to go to work or not — she had to find a way to balance the two.
And she did — but she also realizes how much easier that might have been in today's world, where technology allows employees to work from home productively and sustain a healthy work-life balance.
Appleton, recruiter and director of the accounting and finance division at Johnson & Hill Staffing, finds more and more people are working from home, and sees benefits for both the employee and the employer.
"I've interviewed many people who have had a work-from-home schedule, and usually they say that they end up working more than they would if they were in the office," she explained, adding that it is oftentimes easier to be productive at home than working in an office environment, with the myriad distractions found there.
"I think much of this desire for having flexibility to work remotely came from moms who wanted to have their hands in balancing both the career and raising a family, and not having to feel like they could only do one or the other."
In fact, the work-from-home population has grown by 159% since 2005, and the number of employers offering a remote option has grown by 40% in the past five years. The start of this fairly new trend, Appleton said, can be attributed to the moms.
"I think much of this desire for having flexibility to work remotely came from moms who wanted to have their hands in balancing both the career and raising a family, and not having to feel like they could only do one or the other," she said.
Mary Shea, vice president of digital strategy at GCAi, can attest to this. She's a new mom of a 4½-month-old boy. She commutes from Sturbridge but works from home on Mondays and Fridays, a schedule she says took some getting used to but now allows her use her time more productively while helping her maintain a healthy lifestyle. Her position at GCAi includes building and managing ad campaigns for her clients, a job she says she can do very well remotely.
Between her long commute and having a new baby boy, Mary Shea says working from home twice a week makes a huge difference in her life.
"Most of the time, I don't have to be in the office," Shea told BusinessWest. "I've set it up where Mondays and Fridays are my set schedule. Those are the days I'll work on things that I know are online, and then, the other three days, I come into the office or go on location for a video shoot."
Working from home saves Shea three hours a day that would otherwise be spent in a car — time she spends either working more, grocery shopping, or fitting in some exercise. And she never feels disconnected from the company, knowing her team back in the Springfield office is only a phone call away.
"Technology today has enabled parents, particularly moms like me, to work remotely," she said, adding that hard and soft technology like the cloud-based project-management system GCAi uses and applications on her phone make this possible. "Being able to work remotely in the situation I'm in now is pretty vital because it's just such a busy week."
Shea isn't the only mom, or employee in general, who feels this way. Karen Buell, vice president of Operations at Payveris and mother of two, has been working from home three days a week for eight years.
"Some women are pushing off having a family or they're choosing between a career and having a family. For me, I can choose both," she said, adding that being part of a tech company makes this a pretty easy thing to do.
Tiffany Appleton says Western Mass. businesses are adopting work-from-home policies slower than bigger cities, but it is still becoming more normal in the area.
In fact, Buell says about a third of the employees at Payveris are 100% remote.
But for some employers, this can be a difficult thing to embrace. Appleton says the negative stigma that surrounds those who work from home can sometimes prevent employers from making the jump.
"I've found, in Western Mass., we're a little slower to adopt it than the cities are," she said. "Sometimes employers get scared by work-life balance and think, 'that means people don't want to work, they just want to have a life and pretend they're working.' They just assume the worst."
This negative perception is one of the things Buell experienced in her early work-from-home days, with people telling her she'd have a hard time being visible or ever being promoted. Despite the lingering stereotype, she was promoted at Payveris just a couple months ago.
"It doesn't hold you back. If you're there and you're showing up and being productive, you can do anything," she said. "It's not about where you are, it's about how productive you can be."
Another challenging aspect about working from home is maintaining a connection with those who are at the office. Both Appleton and Shea agreed this responsibility lies largely with the employee, but also the cooperation of co-workers to maintain connectivity.
"Keeping the culture of the office is probably the most important thing the employer can do when having people who are not in the office all the time — finding ways to make sure that they are included, even if they're not there in person," Appleton said.
This may even include something as simple as telling a co-worker not to bring a lunch tomorrow because the office is ordering pizza or letting them know that so-and-so down the hall got engaged.
"Those are the things that usually irk people," she continued. "Making sure there are ways to include the people when they're not there — and being very conscious to include them and make them feel like they are part of the team — is important."
Karen Buell says employers would benefit from seeing the upside of remote work instead of focusing on the negatives.
Technology makes all this especially simple. Appleton says more and more employers are investing in the kinds of technology that can be accessed remotely, such as Freedcamp, a collaborative project-management system that GCAi uses for everyday business and communication.
Win-win Situation
With increasingly adaptive technology that allows employees to do things like videoconferencing and sending documents through group-sharing software within seconds, disconnectedness is becoming less and less of a problem.
"Taking the next step to make sure the tools you're investing in for the office have those abilities for people to work from anywhere is crucial," Appleton said.
When she thinks about becoming a working parent 19 years ago, she realizes how helpful modern technology would have been when her daughter was home sick from school and she had to take the day off from work. Or on a snow day, when it wouldn't have been necessary to get in the car and drive to the office to be productive.
"It's nice now that you can do everything you need to do from home," she said. "I think it's good for the employees and the employers at the end of the day."
Kayla Ebner can be reached at [email protected]m
Tags: Balance home Remote Employees Work-life
Hadley's Route 9 Remains in a Constant State of Motion
Hall of Fame Project Is a Shining Example of Teamwork, Coordination
Put VDI on Your IT Christmas List
By BusinessWest Staff December 16, 2014
IT Labor Force Presents Challenges and Opportunities
By Contributor August 4, 2021


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ZFF Newsletter - everything you need to know about the festival, including new additions to the programme, prize draws and the theatrical launch dates of festival films. Subscribe now!
Locations & Accommodation
Press Pictures 2019
Saturday October 5
Sunday, October 6
Labs & Grants
Application Film Music Competition
Film Music Competition & Concert
Fringe Events
Guests of Honour
Guests+Jury
There are countless options for accommodation situated around the festival site. The cinemas and various festival locations are easily accessible via public transport or on foot. Further information regarding accommodation can be found on the Zurich Tourist Office website.
Zurich Film Festival Partner-Hotels
The Baur au Lac has the discerning tastes of its guests to thank for the leading position it has maintained for more than 165 years in the international luxury hotel industry. Set in its own park in the cit center, the hotel enjoys a unique location overlooking the lake and the Alps.
Nine carefully refurbished townhouses create a distinctive style in which you can discover the new in the old, the old in the new. Every room is individually styled. The fusion of historical architecture and innovative modern design creates a relaxed ambience with real character.
The Dolder Grand is the perfect environment for people who are looking for pleasure, exclusivity and relaxation. The original "Curhaus" dating from 1899 presents itself anew as the luxury-class City Resort. The hotel's elevated location, between the pulsating life of the city and a refreshingly natural green zone, provides a magnificent view of the city of Zurich, the lake and the Alps.
Connect with nature in a bustling business hub at this urban retreat. Decked out with stylish furnishings, this hotel is an iconic landmark steeped in celebrity history, having been visited by Steve McQueen, Freddie Mercury and Muhammad Ali. Set at the foot of Zurich's Uetliberg mountain, the Atlantis by Giardino boasts gorgeous green surroundings and a stunning city view that is beyond compare.
ALDEN Hotel Splügenschloss welcomes you just a few steps away from Lake Zurich and the old town. Enjoy pure luxury and unique elegance in one of the 22 exclusive suites with 60 to 100 sqm, air conditioning, »The Frame« TV, Wi-Fi and spa bathrooms.
The renowned 4-star hotel is just a few minutes' walk from the opera house. In addition to its 58 stylish rooms, the city hotel has been impressing visitors with its newly renovated THE LOBBY - a stylish and cosy destination for locals and visitors of the city.
Discover Zurich with this centrally located hotel in Zurich's district Sihlcity, which is part of the urban entertainment center Sihlcity with the Festival cinema Arena. Built on the grounds of the former Sihl paper mill, parts of the Four Points Sihlcity Zurich consist of listed buildings from the old mill. A unique symbiosis of modern architecture and nostalgic industrial history.
From the hotel Steigenberger Bellerive au Lac, the guest will enjoy the view of Lake Zurich and the city centre which is a mere ten minutes away. The small boutique hotel with its spacious rooms and suites is comfortably furnished.
Sleeping where once beer was being brewed - Located in the former Hürlimann brewery the B2 Boutique Hotel + Spa with its picturesque Wine Library is a surprising and unusual city hotel linked to the public Thermalbath & Spa Zürich with infinity roof top pool.
25hours Hotel Zürich Langstrasse ****
25hours Hotel Zurich Langstrasse is located close to Zurich's main station at the intersection between the traditional district of subculture and night owls and the dignified Eurogate. The hotel regards itself as a small, independent universe where the worlds and cultures of art and finance converge.
In the middle of Zurich there is a breeze of British style – and of football. When you know that this popular sport has its modern origins in England, both come together in a harmonious mix.
The highly traditional Hotel Schweizerhof Zürich is directly opposite the main train station. Right where the famous car-free Bahnhofstrasse of Zurich starts: Just a few steps away you enter the world of exclusive shops and banks, and can admire the most attractive sights of Limmatstadt.
Close to the Zurich Film Festival – near Limmatquai and in the midst of the buzzing Oldtown Zurich – the family-run Boutique Hotel Helmhaus awaits you. Surrounded by manifold culture and exclusive gastronomy we surprise you with 24 elegant rooms, warm service, traditional charme and the hotel's own art.
PLATZHIRSCH Boutique Hotel is located in the very heart of the historic and picturesque quarter Niederdorf, in the city center of Zurich, in a prestigious house built in the year 1879 in neoclassical style. The pleasant 24 room owner-managed boutique hotel with award-winning Swiss Made design and Ligne Roset furniture provides a perfect base for all your leisure and business stays in Zürich.
Casually luxurious, modern and historically protected, this property gives visitors to Zurich a taste of the old, a twist of the new and a whole lot of charm. Located in the historical area of Zurich, a short walk from Bellevue square as well as the Kunsthaus, just within one reach of the busy financial centre and surrounded by few of the best and most interesting restaurant and bars, vibrant cafes and most exciting theatre, nightlife and shopping.
Only a few steps away from Lake Zurich and the Opera House, amidst the trendy Seefeld neighbourhood with countless bars and restaurants for the food lovers, you will find the stylishly furnished Hotel Seehof Zurich.
The Design Hotel Plattenhof with its 37 quiet rooms lies in the heart of Zurich, tucked away in the quiet university neighborhood and only 10 minutes walking distance from the picturesque old town and the lake of Zurich. Museums, theaters, the opera, the university hospital, the ETH and a vast selection of restaurants and cafés are located in the area.
The Hotel Seegarten is located in the Seefeld district only a few steps from the Bellevue, the Stadelhofen train station, the lake, the opera house. Our rooms are individually and charmingly furnished. Well, not to forget the fine Italian restaurant Latino, which belongs to the hotel.
The LADYs FIRST hotel welcomes discerning guests – ladies and gentlemen - that love to discover the extraordinary. Discover our Art Nouveau villa located in the vibrant "Seefeld" quarter. You can choose from 28 tastefully furnished rooms. The lobby lounge with fireplace and the small rose garden are other places that invite you to relax.
Quietly located yet in the middle of the action, the Florhof is a "home away from home", in which our guest feel welcome, secure and unique. A gastronomic restaurant and a picturesque terrace complete the overall picture.
The centrally located, stylish apartments with service offer a cost-effective alternative to traditional accommodation. The apartments are fully equipped and have their own kitchen. Additional services such as WiFi, TV and weekly cleaning are included.
For the Special Offers go there.


Non Stanford is a Welsh professional triathlete and is part of British Triathlon's team of nine coming to Glasgow this summer.
A member of the UK Sport Lottery Funded World-Class Performance Squad, Leeds based Stanford was the ITU World Champion in 2013 and represented Team GB at the Rio 2016 Olympic Games, finishing in fourth.
However, her career didn't start out over three disciplines, Non was a successful cross country runner before studying Sport and Exercises Sciences at the University of Birmingham.
In the summer of 2008 she changed to swimming due to injury and later joined the University's triathlon club, and she has never looked back!
In the 2009 British Triathlon Super Series she came second, and in 2009/10 was awarded the Paul Weston Triathlon Scholarship to concentrate on triathlon. After graduating in 2010, in the same year, she won the bronze medal at the Premium European Cup in Brasschaat.
Since moving to Leeds she has trained alongside Alistair and Jonathon Brownlee and in 2012 she had a breakthrough year.
She won gold at the ITU Triathlon Mixed Relay and another gold at the U23 ITU World Championships and 2013 brought more success winning the ITU Madrid World Triathlon series, before winning the Series Title in London.
An injury setback put Non out for 2014 but 2015 saw her finish second at the Rio Olympic Test Event and the ITU World Triathlon Grand Final in Chicago and in 2016 she came an agonising fourth at the Rio Olympics.
More injuries prevented Non really making an impact in 2017 except for a number of strong placements and a gold medal at the Chengdu ITU Triathlon World Cup.
This year, three podium finishes in the ITU Series and 8th place at the Gold Coast Commonwealth Games, Non stands a real chance of European glory at Glasgow 2018.


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Fisd and Airborne relegated, Dwangwa and Max Bullets promoted,
Surestream have dropped back to the SRFL
Fisd Wizards and Airborne Rangers have been relegated from the top-flight TNM Super League and will be heading back to the regional leagues.
The two teams' fate was sealed at the weekend when Wizards lost to Be Forward Wanderers 3-1 while Rangers were beaten 2-1 by Azam Tigers.
Wizards still anchor the table with 21 points from 26 games and even if they win their last two games, they will finish with 27 points and can only go a place up.
On the other hand, Rangers are second from the bottom with 25 points and a single game to wind up the season and even if they win, they will not get out of the relegation zone.
Rangers coach Kondwani Gawani was at pains to come to terms with reality, saying: "It is a painful moment for us. We do not know where it all went wrong, but I guess it part of the game and we will have to start all over again."
Wizards technical director and owner Peter Mponda could not hide his disappointment.
"It is very frustrating to play good football but end up in a situation like ours where we have lost our Super League status.
"Our adventure has come to an end, but it is not the end of it all. Football will still be played and we will still keep this team together. We will be back in the Super League," said the former Flames skipper.
Meanwhile, in the Chipiku Central Region Football Association (CRFA) Premier League, Dwangwa United have booked their ticket into the Super League after emerging champions even with two games in hand.
Dwangwa achieved the feat despite losing 2-0 to Kasungu Medicals.
They have 79 points whereas second-placed Support Battalion—who also have two games to wind up the league—train by seven points.
This means that even if Dwangwa lose their two remaining matches and Support win both their games, Dwangwa will still win the league by a point.
In the Southern Region Football Association (SRFA) Premier League, Max Bullets on Saturday beat Prison United 1-0 to seal the title and earned themselves promotion to the TNM Super League next season, with three games in hand.
Max Bullets have 88 points from 37 games played this far whereas Cobbe Barracks are second with 74 points from 36 games. As we went to press, both teams had games on Sunday.
PrevPreviousBB hit 60 points target
NextBullets beat Fisd WizardsNext


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Sidmouth to lose another bank following Santander announcement
Clarissa Place
Santander, Sidmouth. Picture: Google Maps
Picture: Google Maps
Another Sidmouth bank is set to close its doors for good.
Santander announced today (Wednesday) that it will be closing 140 of its branches in 2019, with Sidmouth's bank closing on June 20.
A bank spokesperson said it will look to find alternative roles for 1,270 people affected wherever possible and expects to redeploy about a third of staff.
It follows the closure of the town's NatWest bank last year and the Co-operative in 2016, leaving just HSBC and Lloyds as the only banks in the town.
Santender says the closures are in response to changes in how customers are choosing to carry out their banking.
Susan Allen, head of retail and business banking, said: "The way our customers are choosing to bank with us has changed dramatically in recent years, with more and more customers using online and mobile channels. As a result, we have had to take some very difficult decisions over our less visited branches, and those where we have other branches in close proximity.
"We will support customers of closing branches to find alternative ways to bank with us that best suit their individual needs. We are also working alongside our unions to support colleagues through these changes and to find alternative roles for those impacted wherever possible.
"We continue to believe that branches have a vital role to play and we will be refurbishing 100 of our branches over the next two years. We are confident that following these changes we will have the right branch network to serve our customers' changing needs, and we expect the size of our network to remain stable for the foreseeable future."
The bank says it plans to invest £55million to refurbish 100 branches over the next two years as part of its future branch network plans.
A bank spokesman said: "Santander will support customers of closing branches to find other ways to bank that best suit their needs, including individual help to find alternative Santander branches and access digital, telephone and Post Office banking services."


AFJD is the design studio of Amber Frid-Jimenez and Joe Dahmen. The studio is positioned at the intersection of architecture and information design. Our transdisciplinary approach engages advanced technology to create conceptually rigorous virtual and environmentally sustainable built environments. Our practice is eclectic and multidimensional, which reflects the changing culture of design and technology. We invent new processes and create social and political situations around the design of architecture, information design, and social media.
We have been working together for fifteen years. Much of that time we have been in Boston, where we stayed after finishing degrees at the Massachusetts Institute of Technology. In 2012, we relocated to Vancouver and travel back and forth to Norway and the Netherlands for research and projects.
Amber Frid-Jimenez is an artist and designer who works at the intersection of art, design and technology. She has taught design studios and seminars at the Rhode Island School of Design, Massachusetts Institute of Technology Visual Arts Program, at the National Arts Academy (KHiB) in Bergen, Norway and most recently, Emily Carr University, Vancouver, where she is Canada Research Chair in art and design technology. She holds a Masters in Media Arts and Sciences from the MIT Media Lab where she studied with John Maeda.
Joe Dahmen trained as an architect at MIT and is an expert on sustainable building technology. He is an Assistant Professor of Design and Sustainability Integration at the University of British Columbia School of Architecture and Landscape Architecture, where he teaches design studios and building technology courses. He is also a Faculty Associate of the Peter Wall Institute. He has an Master of Architecture from the Massachusetts Institute of Technology, where he worked with Yung Ho Chang and 2008 MacArthur Fellow John Ochsendorf.


Kodo Conlin took up solitary sitting in 2006. His formal Zen training began in 2011, and he has since ordained as a Soto Zen priest in the warm-hearted lineage of Shunryu Suzuki. He trained for nearly two years at Tassajara Zen Mountain Center, and now lives at San Francisco Zen Center's City Center. Kodo feels equally a part of the Zen and Vipassana traditions. The practices of insight into the breath and body, and the teachings of early Buddhism have been foundational. Kodo first met Vipassana in Texas, living and training at Southwest Vipassana Meditation Center. Since moving west, Kodo has found homes of practice in California Vipassana Center, Insight Retreat Center and Spirit Rock. Kodo instructs practitioners of all ages in active mindfulness as well as seated meditation. Most recently, he gratefully served at the Spirit Rock Family Retreat and Abhayagiri Teen Weekend. He has the simple, modest intention to share the practice of Dharma, in gratitude for how it has transformed his world.


The Little Thing That Runs The World. A brief, non-technical guide to insects and the diversity of critical ecosystem functions they perform.Manju V Sharma, Rufford Grant Foundation.
Indigenous farms and Resilience: a case from the Nilgiri Biosphere Reserve, India. Manju V Sharma, Robert Leo and Mathew John. Submitted to Farming Matters.
Montane swamps of the upper Nilgiris, Western Ghats, India: a case for conservation Shiny M Rehel, Arun Kanagavel, T Aradukuttan, B Mahadesha, Archana Sivaramakrishnan and Manju V Sharma. Submitted to Current Science.


Fast & Free Suave Fanm Sa MP3 download from Awesome MP3. We have found your song: Suave Fanm Sa MP3 music. Matching your searches top 10 results from Awesome MP3. Now we recommend you to Start Free Mp3 Download Suave Fanm Sa MP3 which is uploaded by Power Surge of size 7.17 MB , duration 5 minutes and 27 seconds and bitrate is 192 Kbps .


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First People Then And Now
Introducing Indigenous Australians
Introducing Indigenous Australians – Book
Australian Aboriginal Heroes Of The Resistance
Timeline For The First People Then And Now
First People Then And Now: Marji Hill – Author And Artist
First People Then And Now And
Prior to 1788 when the British occupied the east coast of Australia there were a number of visitors to what was regarded as the strange and unknown continent.
When the first visitors came is not known. But there has long been contact with the people of Papua New Guinea for many thousands of years. When the sea levels were low the two countries – Papua New guinea and Australia – were connected by land.
This was during the Ice Ages. The people of Papua New Guinea, those of the Torres Strait and Cape York intermarried and exchanged cultural values and technology.
The Chinese admiral Cheng Ho sailed into the waters bordering the Indian Ocean, including East Africa, India, Java and Sumatra. Who knows he may even have travelled into Australian waters between 1405 and 1432 on his great voyages of discovery.
Macassans
For several centuries to just after the conclusion of the nineteenth century at least a thousand Macassans from the Indonesian island of Sulewesi visited northern Australia. They were fishermen and they fished for the sea slug called trepang. This was a prized delicacy in Chinese cuisine.
Each year the Macassans would come. They would stay in northern Australia for approximately five months before heading back in April of each year when the winds changed to the southeast.
The Macassans set up settlements on sheltered beaches along the coast of Arnhem Land not far from the shallow waters where the trepang was collected. They set up processing plants for the trepang – rows of stone fireplaces which supported the cauldrons in which it was boiled.
The second stage of the processing involved burying the trepang in the sand. It was smoked and then packed ready to be taken back to Macassar and the markets in Asia.
Some Aboriginal families today have Macassan ancestors and relations. Some even travelled back to Macassar.
In 1907 the Australian government put an end to these visits by the Macassans because they became a threat to the pearling industry.
The Portuguese and Spanish
In the sixteenth and seventeenth centuries Portuguese and Spanish navigators explored the waters near Australia. In 1605 the Spanish navigator, Pedro Fernandes de Queirós claimed Vanuatu thinking it was the great southern land, and Luis Vaez de Torres in 1606 sailed through the waters that now bear his name – the Torres Strait.
In that same year 1606 the Dutch explored the waters around the northern, western and southern coasts. There were some unfriendly encounters with the Aborigines which dissuaded them from exploring inland. In 1697 Willem de Vlamingh made an attempt at exploration but he also withdrew.
Between 1771 and 1828 the French sent eight expeditions to Australia to extend their knowledge of the little known world of Australia and the Pacific. Many places in Australia are named after the French explorers: La Perouse, D'Entrecasteaux Channel, Freycinet, to name just a few.
The English
On 19 April 1770 Captain James Cook reached the east coast of Australia. Cook was instructed to take possession of the eastern half of the continent for the British crown.
At Possession Island, off Cape York Peninsula in North Queensland, he took possession of the whole of the eastern coast in the name of King George III. Cook declared that Australia was terra nullius meaning that Australia was land without people and that it was unoccupied and unowned.Therefore, the belief of the time was that the British could justify claiming the country as theirs.
Captain Cook took the east coast of Australia and he named it "New Whales " then "New Wales and finally settled on New South Wales. From this time on Aboriginal Australians became a shadow people with no rights of any kind.
This taking of so called unoccupied land was an application of the dictum of terra nullius (land without people). Terra nullius comes from the Latin meaning "nobody's land". It was the justification the British Government used to claim the eastern side of the continent as theirs.
What the British did not know or acknowledge was that Australia with its first people was one of the earliest centres of civilisation in the world with its beginnings at least 60,000 years ago.
The arrival of the First Fleet from England in 1788 changed the course of history for Australia's first people then and now forever. For the next two hundred years a tragic and catastrophic history unfolded which for Aboriginal people was marked by constant warfare with their British invaders.
c.Text Marji Hill 2017
c. Macassans – Art work; Gun on rock shelter Marji Hill 2017
Aboriginal Day Of Mourning
Australia A Continent On Fire
Musquito: Australian Aboriginal Hero Of The Resistance
Yagan: Hero Of The Australian Aboriginal Resistance
Books On Indigenous Australians


The Metro Paws Poopy Packs Yellow 8-Pack Dog Bags are designer, durable, and degradable! They're as much fun as you can have picking up poop because Metro Paws designs don't stink!
Metro Paws does everything they can to be eco-friendly, chic, and unique. They are a family owned business of dog lovers and owners. Their mission? To get as many licks a day as possible!


Do you have no respect for me? Why do you not tremble in my presence? I, the LORD, am the one who defines the ocean's sandy shoreline, an everlasting boundary that the waters cannot cross. The waves may toss and roar, but they can never pass the bounds I set.
Why don't you honor me? Why aren't you in awe before me? Yes, [me], who made the shorelines to contain the ocean waters. I drew a line in the sand that cannot be crossed. Waves roll in but cannot get through; breakers crash but that's the end of them.
Have you no fear of me? says the Lord; will you not be shaking with fear before me, who have put the sand as a limit for the sea, by an eternal order, so that it may not go past it? and though it is ever in motion, it is not able to have its way; though the sound of its waves is loud, they are not able to go past it.
Do you not fear me? says the LORD; Do you not tremble before me? I placed the sand as a boundary for the sea, a perpetual barrier that it cannot pass; though the waves toss, they cannot prevail, though they roar, they cannot pass over it.
Do you not fear Me?' says the LORD. 'Will you not tremble at My presence, Who have placed the sand as the bound of the sea, By a perpetual decree, that it cannot pass beyond it? And though its waves toss to and fro, Yet they cannot prevail; Though they roar, yet they cannot pass over it.
1 tn Heb "Should you not fear me? Should you not tremble in awe before me?" The rhetorical questions expect the answer explicit in the translation.
2 tn Heb "it." The referent is made explicit to avoid any possible confusion.